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植物形質を測っただけの研究ではなく、フェノタイピング手法の開発・検証・実質的利用・ベンチマーク・方法レビューとの関連性が見つかった論文を中心に表示します。
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Why it matches plant phenotyping methodsUAV画像からダイズ群落の葉の傾斜角を抽出する枠組みであり、明示的な植物形質の取得手法が題名の中心です。
titleA UAV-based framework for field-scale extraction of leaf inclination angles in soybean canopies
Accurately simulating shoot-scale light scattering in physically based radiative transfer models remains a key challenge for conifer ecosystems. This study evaluates the high-resolution three-dimensional (3D) radiative transfer capability of the Discrete Anisotropic Radiative Transfer (DART) model using laboratory reflectance measurements and detailed photogrammetric reconstructions of Norway spruce ( Picea abies (L.) H. Karst) shoots. Samples representing multiple age classes and crown positions were collected from temperate (Czech Republic) and hemiboreal (Estonia) Norway spruce stands. Their geometry was reconstructed with sub-millimetre accuracy using structured blue-light 3D scanning, while the optical properties of needles and twigs were measured using an integrating sphere. We measured shoot reflectance under controlled laboratory illumination and compared it to DART simulations based on the identical 3D structures and optical inputs. DART simulations accurately reproduced the measured spectral signatures (R 2 = 0.95; median spectral angle mapper = 4.8°), demonstrating the model's capacity to simulate shoot-scale reflectance across diverse viewing geometries. These results suggest that detailed 3D shoot representations can improve radiative transfer modelling accuracy, and that DART efficiently simulates shoot reflectance across diverse viewing geometries as an alternative to labour-intensive goniometer measurements. This work provides the first empirical evaluation of DART at the shoot-scale and establishes a transferable framework for integrating detailed 3D photogrammetry into radiative transfer modelling. This approach enables more accurate upscaling from the conifer needle to the canopy-level and can enhance future model intercomparison exercises, such as the Radiation Transfer Model Intercomparison benchmark. • First empirical validation of DART simulation of conifer shoots. • High-resolution blue-light photogrammetry captures realistic shoot architecture. • DART-simulated reflectance closely matches laboratory measurements. • Framework enables realistic needle-to-canopy upscaling in radiative transfer models.
Why it matches plant phenotyping methods針葉樹シュートの3D構造を高精度に取得し、反射率モデルを実測値で検証する技術研究であり、植物形質（シュート構造・反射特性）の取得とモデル評価が中心である。
abstractTheir geometry was reconstructed with sub-millimetre accuracy using structured blue-light 3D scanning
To accurately invert the canopy parameters (Total photosynthetic area index, TPAI) of oilseed rape, a microwave characteristic layered measurement experiment was designed, and a multilayer microwave scattering semiempirical model (MLMSSM) was constructed based on the radar response changes induced by the differences in the vertical structure and canopy components at different phenological stages. This model was then applied to the main oilseed rape production areas to conduct regional TPAI inversion. Microwave characteristic layered measurement experiments were performed at six-leaf, flowering, beginning ripening and fully ripening stages of oilseed rape in the laboratory of target microwave properties (LAMP). The MLMSSM was constructed based on the LAMP-measured data, corresponding to different plant structures containing two- and three-layer submodels, and the TPAI inversion model was derived based on the correlation between the MLMSSM parameters and the crop biophysical variables. Finally, regional TPAI inversion and validation were carried out in the main oilseed rape production area (Hengyang), using Sentinel-1 SAR data. Visualization results, MLMSSM parameters and TPAI inversion results based on LAMP data all revealed the occurrence of multiple scattering interactions among distinct oilseed rape structural layers and verified the effectiveness of the measurement scheme and the model. The regional validation results showed high TPAI inversion accuracy throughout entire oilseed rape growth stages, with R² = 0.78, RMSE = 1.03, and MAE = 0.74 under VV polarization, and R² = 0.84, RMSE = 0.75, and MAE = 0.52 under VH polarization. The MLMSSM was found to significantly outperform the modified water cloud model (MWCM), increasing R² by 0.05 and 0.18 under VV and VH polarization respectively, while reducing the RMSE and MAE by 0.49–0.72. These results prove the accuracy and applicability of the MLMSSM for regional TPAI inversion of oilseed rape.
Why it matches plant phenotyping methods油糧ナタネの群落光合成面積指数を推定するマイクロ波計測・散乱モデルを開発し、Sentinel-1 SARで地域検証しており、植物形質取得法が中心である。
abstracta multilayer microwave scattering semiempirical model (MLMSSM) was constructed
The accurate three-dimensional (3D) distribution of plant area density (PAD) within forests is crucial for understanding canopy structure and provides essential scene inputs for 3D Radiative Transfer Models (RTMs) to facilitate remote sensing interpretation. However, current lidar-based voxelization methods that estimate detailed PAD distributions often cover limited areas, constraining their applications in conducting broad forest studies and interpreting Earth Observation Satellite (EOS) data of various scales and resolutions. To address this, we developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow capable of producing extensive high-resolution 3D voxelized forest scenes (up to 100 km² with ≤2 m voxel size) from worldwide open-access airborne lidar scanning (ALS) data. By applying LS-PVlad to the ALS data acquired during the extensive NASA Goddard's LiDAR, Hyperspectral & Thermal Imager (G-LiHT) campaigns, we developed the first release of FoScenes—a high-fidelity PAD product comprising 40 seamless scenes from 28 diverse forest sites, with individual area ranging from ∼50 to ∼11,000 ha. The leaf area estimates of LS-PVlad have been validated by two-year field-measured leaf area index (LAI) from litter collection (best RMSE = 0.35 m²/m²) and digital hemispherical photography (DHP) images (RMSE = 0.46 m²/m²) across multiple plots at a deciduous forest site. Additionally, a broad comparison between FoScenes and MODIS plant/leaf area index product demonstrates high consistency (R² = 0.70, RMSE = 0.86 m²/m²). By providing multi-dimensional forest characterizations, FoScenes enables temporal insights into structure dynamics. Its integration with the discrete anisotropic radiative transfer (DART) model underscores the potential of FoScenes for extensive 3D RTM applications at various scales.
Why it matches plant phenotyping methods森林の植物面積密度を推定する3D再構成ワークフローを開発し、実測LAI等で検証した大規模フェノタイピング製品・データセットであり、植物形質取得が中心である。
abstractwe developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow
Near-real-time (NRT) daily crop monitoring at the field scale is crucial for precision agriculture, yet remains challenging due to limitations in the spatial or temporal resolution of existing remote sensing methods. While Sentinel-2 provides adequate spatial resolution for field-level applications, its temporal resolution is insufficient for capturing rapid crop dynamics, especially in cloudy regions. Existing spatiotemporal fusion techniques require multiple clear-sky images and lack true NRT capability, while ground-based sensors offer continuous monitoring but with limited spatial coverage. To address these limitations, this study develops the Near-Real-Time Ground-Satellite Fusion (NRT-GSF) algorithm, a novel approach based on a Bayesian dynamic linear model and Kalman filtering. The algorithm uniquely integrates Sentinel-2 imagery with continuous measurements from Internet of Things for Agriculture (IoTA) systems to generate daily 10-m Green Area Index (GAI) products. Its recursive framework supports both forward prediction in NRT mode following satellite overpasses and backward updating to refine historical profiles. Implemented over French wheat fields using 34 IoTA systems and Sentinel-2 time series from 2019, the algorithm effectively enhanced spatiotemporal completeness and accuracy (R = 0.75–0.98, RMSE = 0.1–0.49). A comprehensive leave-one-out Sentinel-2 evaluation demonstrated its superiority over the current Consistent Adjustment of the Climatology to Actual Observations (CACAO) algorithm. Ground validation using handheld RGB cameras further confirmed the accuracy of the GAI products from the new algorithm (RMSE = 0.5). The NRT-GSF framework offers a robust and operationally solution for daily, high-resolution crop GAI mapping in NRT mode, and it can be extended to other traits or applications in the near-real-time context.
Why it matches plant phenotyping methods圃場規模の日次Green Area Index（GAI）という植物形態形質を、衛星画像と地上センサーから推定する融合アルゴリズムを開発し、比較評価と地上検証を行っているため、植物フェノタイピング手法が中心である。
abstractthis study develops the Near-Real-Time Ground-Satellite Fusion (NRT-GSF) algorithm
Many commodity crops have growth stages during which they are particularly vulnerable to stress-induced yield loss. In-season crop progress information is useful for quantifying crop risk, and satellite remote sensing (RS) can be used to track progress at regional scales. At present, all existing RS-based crop progress estimation (CPE) methods which target crop-specific stages rely on ground truth data for training/calibration. Such data are collected via field trials or surveys. This reliance on ground survey data confines CPE methods to surveyed regions, limiting their utility. In this study, a new method is developed for conducting RS-based in-season CPE in unsurveyed regions by combining data from surveyed regions with synthetic crop progress data generated for an unsurveyed region of interest. Corn-growing zones in Argentina were used as surrogate ‘unsurveyed’ regions. These zones have climates and dual planting systems which differ from the single planting system in the US Midwest – the surveyed region in this study. Existing weather generation, crop growth, and optical radiative transfer models were linked to produce synthetic weather, crop progress, and canopy reflectance data. These data mimic weather, cultivars, and cropping practices in the unsurveyed region. A neural network (NN) method based upon bi-directional Long Short-Term Memory was trained separately on surveyed data, synthetic data, and two different combinations of surveyed and synthetic data. In the absence of real validation data in unsurveyed regions, a stopping criterion was developed which uses the weighted divergence of surveyed and synthetic data validation loss. F1 score was modified to measure CPE accuracy when the NN was trained on each data combination, with scores based on over- and under-estimates of crop progress throughout the season. Including synthetic data during training improved performance in 9 out of 11 corn-growing zones in Argentina. Net F1 scores across all crop progress stages increased by 8.7% when trained on a combination of surveyed region and synthetic data, and overall performance was only 21% lower than when the NN was trained on surveyed data and applied in the US Midwest. Performance gain from synthetic data was greatest in zones with dual planting windows, while the inclusion of surveyed region data from the US Midwest helped mitigate NN sensitivity to noise in NDVI data. Overall results suggest in-season CPE in other unsurveyed regions may be possible with increased quantity and variety of synthetic crop progress data.
Why it matches plant phenotyping methods作物の生育ステージを衛星リモートセンシングとニューラルネットワークで推定する手法を開発し、合成データによる性能評価も行っており、植物状態の取得・推定が研究の中心である。
abstractIn this study, a new method is developed for conducting RS-based in-season CPE in unsurveyed regions by combining data from surveyed regions with synthetic crop progress data generated for an unsurveyed region of interest.
Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R² values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4 % to 8.4 % for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. These findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.
Why it matches plant phenotyping methodsPRISMAおよびNEON AOP hyperspectralデータを用い、複数の植物形質を推定・検証する多段階フレームワークを開発しており、形質取得手法と性能評価が研究の中心である。
abstractThis study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits
The accurate three-dimensional (3D) distribution of plant area density (PAD) within forests is crucial for understanding canopy structure and provides essential scene inputs for 3D Radiative Transfer Models (RTMs) to facilitate remote sensing interpretation. However, current lidar-based voxelization methods that estimate detailed PAD distributions often cover limited areas, constraining their applications in conducting broad forest studies and interpreting Earth Observation Satellite (EOS) data of various scales and resolutions. To address this, we developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow capable of producing extensive high-resolution 3D voxelized forest scenes (up to 100 km 2 with ≤2 m voxel size) from worldwide open-access airborne lidar scanning (ALS) data. By applying LS-PVlad to the ALS data acquired during the extensive NASA Goddard's LiDAR, Hyperspectral & Thermal Imager (G-LiHT) campaigns, we developed the first release of FoScenes—a high-fidelity PAD product comprising 40 seamless scenes from 28 diverse forest sites, with individual area ranging from ∼50 to ∼11,000 ha. The leaf area estimates of LS-PVlad have been validated by two-year field-measured leaf area index (LAI) from litter collection (best RMSE = 0.35 m 2 /m 2 ) and digital hemispherical photography (DHP) images (RMSE = 0.46 m 2 /m 2 ) across multiple plots at a deciduous forest site. Additionally, a broad comparison between FoScenes and MODIS plant/leaf area index product demonstrates high consistency (R 2 = 0.70, RMSE = 0.86 m 2 /m 2 ). By providing multi-dimensional forest characterizations, FoScenes enables temporal insights into structure dynamics. Its integration with the discrete anisotropic radiative transfer (DART) model underscores the potential of FoScenes for extensive 3D RTM applications at various scales. • We developed a large-scale ALS-data-driven 3D forest reconstruction workflow. • FoScenes product consists of 40 various forest scenes derived from NASA G-LiHT data. • The estimated leaf/plant area index strongly aligns with field data and EOS products. • FoScenes captures temporal structure variation by multi-dimensional characterization. • FoScenes can be integrated into DART for realistic simulations at varied scales.
Why it matches plant phenotyping methods森林の植物面積密度・葉面積指数を推定するALSベースの3D再構成ワークフローを開発し、実測LAI等で検証した方法・データセット研究であり、植物形態の取得が中心です。
abstractwe developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow
Vegetation indices (VIs) are widely employed in remote sensing for quantitative monitoring of plant disease due to their simplicity and robustness. However, factors such as canopy structure, the distribution of diseases in the canopy, and observation geometry may influence the spectral response of diseased canopies, potentially affecting the performance of VIs developed under specific disease conditions (e.g., early-stage). To date, fewer comprehensive analytical strategy has been proposed to quantitatively assess the confounding effects of multiple factors, which has hindered the selection of optimal VIs for practical disease monitoring. This study proposes an integrated analytical strategy that combines a three-dimensional radiative transfer model (3D RTM) with a multi-criteria decision-making method — entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) — to systematically evaluate existing disease-related VIs at the canopy scale, based on simulation outputs and ground measurements. We employed the LargE-Scale remote sensing data and image Simulation framework (LESS) to simulate the bidirectional reflectance factor (BRF) of canopies affected by two representative apple diseases, quantitatively evaluated the confounding effects of tree shape, disease distribution and observation geometry on VIs, and systematically ranked the performance of 40 VIs from two critical perspectives. Results from analyses on two disease types showed that Health Index 2014 (HI2014) and Water Band Index in SWIR (WBISWIR) were the top-performing indices for monitoring apple blotch disease (AMB) and apple mosaic disease (MD), respectively, Notably, WBISWIR emerged as the co-optimal index, exhibiting the highest monitoring efficacy across both diseases. Among all indices, the Normalized PRI (PRIn) demonstrated the greatest robustness against variations in tree shapes and disease distributions. WBISWIR exhibited good performance across diverse observation geometries. When comparing the relative influence of three factors on VI performance, tree shape and disease distribution exerted greater effects than observation geometry. Our findings highlight the complex interactions between VIs and confounding factors, emphasizing the necessity of caution when applying disease-related VIs and advocate for comprehensive consideration of tree shape and stress distribution effects during VI selection, especially for early-stage disease detection. This study offers a robust methodological framework for selecting VIs tailored to specific disease and vegetation characteristics, enhancing the precision of remote sensing-based plant disease assessments.
Why it matches plant phenotyping methodsリンゴ樹の病害状態を対象に、3D放射伝達モデル、シミュレーション、実測データ、TOPSISを統合して病害スペクトル指標を評価・選定する方法論が中心であり、植物病害フェノタイピング手法に該当する。
abstractThis study proposes an integrated analytical strategy that combines a three-dimensional radiative transfer model (3D RTM) with a multi-criteria decision-making method — entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) — to systematically evaluate existing disease-related VIs at the canopy scale, based on simulation outputs and ground measurements.
The rapid growth of remotely sensed earth observation data presents clear opportunities for monitoring complex ecosystem change and answering fundamental ecological questions. However, large-scale automated monitoring of ecosystems faces challenges. Data-driven models require extensive datasets and often lack generalizability when training data are unrepresentative, while process-driven models, such as radiative transfer models (RTMs), can be imprecise due to gaps in knowledge, or simplified representation of physical processes. To enhance the prediction of plant functional traits and simultaneously discover where process-driven models can be improved, we explore the potential of Physics-Informed Neural Networks (PINNs) as a hybrid approach that combines the strengths of both methodologies at the leaf scale. In contrast to data augmentation approaches, our implementation directly integrates the widely-used PROSPECT5B model into the architecture of an autoencoder framework. Our results show that our PINNs approach is able to outperform data-driven techniques even when trained on very limited training data (i.e. 17 % training vs 83 % validation). We also identified weak points in the PROSPECT5B model by progressively replacing individual components of PROSPECT5B with convolutional neural networks. Our case study indicates that especially Prospect's generalized “plate model” could be refined to improve predictive ability. Hence, our framework provides a self-diagnostic capability and identifies areas for improvement in process-driven models and their components. Thus, we conclude that PINNs 1) improve data-driven predictive accuracy while maintaining physical consistency with minimal training data while 2) being able to identify limitations in process-driven models. Hence, we believe our framework could serve as a new standard for evolving and improving radiative transfer models.
Why it matches plant phenotyping methods葉レベルの植物機能形質を推定するPINNsと放射伝達モデル統合手法を開発・検証しており、形質取得・推定法が研究の中心である。
abstractwe explore the potential of Physics-Informed Neural Networks (PINNs) as a hybrid approach that combines the strengths of both methodologies at the leaf scale.
Methods based on upward canopy gap fractions are widely employed to measure in-situ effective LAI (Le) as an alternative to destructive sampling. However, these measurements are limited to point-level and are not practical for scaling up to larger areas. To address the point-to-landscape gap, this study introduces an innovative approach, named NeRF-LAI, for corn and soybean Le estimation that combines gap-fraction theory with the neural radiance field (NeRF) technology, an emerging neural network-based method for implicitly representing 3D scenes using multi-angle 2D images. The trained NeRF-LAI can render downward photorealistic hemispherical depth images from an arbitrary viewpoint in the 3D scene, and then calculate gap fractions to estimate Le. To investigate the intrinsic difference between upward and downward gaps estimations, initial tests on virtual corn fields demonstrated that the downward Le matches well with the upward Le, and the viewpoint height is insensitive to Le estimation for a homogeneous field. Furthermore, we conducted intensive real-world experiments at controlled plots and farmer-managed fields to test the effectiveness and transferability of NeRF-LAI in real-world scenarios, where multi-angle UAV oblique images from different phenological stages were collected for corn and soybeans. Results showed the NeRF-LAI is able to render photorealistic synthetic images with an average peak signal-to-noise ratio (PSNR) of 18.94 for the controlled corn plots and 19.10 for the controlled soybean plots. We further explored three methods to estimate Le from calculated gap fractions: the 57.5° method, the five-ring-based method, and the cell-based method. Among these, the cell-based method achieved the best performance, with the r² ranging from 0.674 to 0.780 and RRMSE ranging from 1.95 % to 5.58 %. The Le estimates are sensitive to viewpoint height in heterogeneous fields due to the difference in the observable foliage volume, but they exhibit less sensitivity to relatively homogeneous fields. Additionally, the cross-site testing for pixel-level LAI mapping showed the NeRF-LAI significantly outperforms the VI-based models, with a small variation of RMSE (0.71 to 0.95 m²/m²) for spatial resolution from 0.5 m to 2.0 m. This study extends the application of gap fraction-based Le estimation from a discrete point scale to a continuous field scale by leveraging implicit 3D neural representations learned by NeRF. The NeRF-LAI method can map Le from raw multi-angle 2D images without prior information, offering a potential alternative to the traditional in-situ plant canopy analyzer with a more flexible and efficient solution.
Why it matches plant phenotyping methodsNeRFとUAV多視点画像を組み合わせ、トウモロコシ・ダイズの葉面積指数を推定・マッピングする手法を開発し、実圃場で性能検証しているため、植物表現型取得が中心である。
abstractthis study introduces an innovative approach, named NeRF-LAI, for corn and soybean Le estimation that combines gap-fraction theory with the neural radiance field (NeRF) technology
Canopy chlorophyll content per ground area (CCC, g·m⁻²) is tightly related to vegetation photosynthesis and is a promising indicator of photosynthetic capacity. However, a global operational CCC product is not yet available. To fill this gap, we developed a two-step upscaling method to estimate global CCC from Sentinel-3 OLCI top-of-atmosphere (TOA) reflectance. In the first step, a physically-based PROSAIL-D inversion model produced accurate CCC maps from over 20,000 high-spatial resolution (1 m) airborne hyperspectral images collected across 50 sites within the National Ecological Observatory Network (NEON) between 2019 and 2021. The validation against ground CCC measurements showed an R² of 0.89 and an RMSE of 0.30 g·m⁻². In the second step, these high-resolution CCC maps were resampled or upscaled to a spatial resolution of 300 m, and combined with Sentinel-3 OLCI TOA reflectance images to train random forest (RF) models. The RF model demonstrated robust performance with leave-one-site-out cross-validation, yielding an R² of 0.92 and RMSE of 0.14 g·m⁻². The two-step method also showed minimal sensitivity to angular effects and land cover variations, underscoring its robustness. In comparison, the traditional direct inversion method (the one-step method) led to underestimation of CCC by 0.16 g·m⁻² and a moderate estimation accuracy (R² = 0.65, RMSE = 0.30 g·m⁻²). We generated a long-term global OLCI CCC product using Sentinel-3 OLCI TOA reflectance data from 2016 to 2024, which can also be continuously updated using current data. This global CCC product can provide important plant physiological information, for parameterizing terrestrial biosphere models and capturing spatiotemporal photosynthetic patterns, thereby advancing research on vegetation carbon dynamics cycles at the global scale.
Why it matches plant phenotyping methodsSentinel-3および航空ハイパースペクトル画像から植物キャノピーのクロロフィル含量を推定する二段階センシング・機械学習手法を開発し、地上測定およびサイト間交差検証で性能評価しているため、植物表現型取得が研究の中心である。
abstractwe developed a two-step upscaling method to estimate global CCC from Sentinel-3 OLCI top-of-atmosphere (TOA) reflectance.
Disentangling pathogen infection signals in plants is critical for understanding the physiological processes that underlie the complex host-pathogen interactions and predicting impending disease outbreak. The rapid progression of rice blast lesions, caused by the filamentous fungus Magnaporthe oryzae, and its imperceptible disease-related symptoms during the asymptomatic stages render real-time detection and visualization challenging. Efforts to reveal pre-visual disease symptoms are of both broad concern and significant interest but remain challenging, as subtle disease signals are often obscured or diluted by other factors at asymptomatic stage. We introduce an imaging spectroscopy-based purification methodology that isolates the disease signals revealed by spectral unmixing on a pixel basis without considering the complex pathogen-induced physiological variations. With multi-temporal proximal hyperspectral imagery, our method captured the transition of disease lesions from asymptomatic to severely symptomatic stages, and successfully distinguished the subtle pathogen-induced signals with few false alarms as early as three days (two days after inoculation, DAI 2) before visual lesions became apparent (DAI 5). The lesion prediction results were confirmed by extensive in vivo visual inspections. Remarkably, we demonstrated that spatially aggregating the isolated disease signals improved the accuracy of pre-visual RB identification to a remarkable level up to 93 % (F1-score = 0.91), enabling unprecedented visualization of potential lesions in a narrow time window of pathogen infection. Although limitations remain regarding model validation and scalability for broader applications, this method represents a significant advancement in early disease forecasting across spectral and spatial domains, and offers new opportunities for high-throughput screening of susceptible varieties in next-generation plant resilience phenotyping.
Why it matches plant phenotyping methodsイネいもち病の病斑を、発病前のハイパースペクトル画像から抽出・予測する画像解析手法を開発し、精度検証している。植物病態の表現型取得が研究の中心である。
abstractWe introduce an imaging spectroscopy-based purification methodology that isolates the disease signals revealed by spectral unmixing on a pixel basis
Increasing combined heat and drought extremes due to climate change heighten the risk of crop failure, underscoring the need for improved stress diagnosis for effective management strategies. However, current plant physiology indicators struggle to differentiate crop stresses in hot-dry environments. This study proposes using specific leaf metabolites, detectable by leaf reflectance spectra, for more precise identification of heat and drought stress compared to traditional methods. We conducted two rounds of one-week drought treatments under heat stress on soybean seedlings. Throughout the experiment, we monitored stomatal conductance, reflectance spectra, and metabolites, including Abscisic Acid (ABA), Jasmonic Acid (JA), Salicylic Acid (SA), and proline (Pro), on a daily basis. Our findings revealed that ABA and JA exhibited differential sensitivities to drought and heat stress, respectively. In contrast, stomatal conductance was unable to differentiate between the two stressors. Using partial least-squares regression (PLSR), we determined that both ABA and JA could be detected via leaf spectroscopy with moderate predictive performance (R² = 0.53, relative RMSE = 14.28 %; R² = 0.53, relative RMSE = 14.96 %) and exhibited distinct sensitive spectral signatures. The metabolite-derived, stress-specific spectral models enable more precise and earlier diagnosis and differentiation of stress in a hot-dry environment than traditional physiological indicators (e.g., relying on stomatal conductance). This study provides an example of using metabolites as novel stress indicators, which could contribute to precision agriculture, offering the potential for accurate, stress-specific, and pre-physiological detection of crop health.
Why it matches plant phenotyping methods葉の反射スペクトルとPLSRにより、熱・ drought stressに関連する代謝物を推定し、植物ストレス状態を識別する手法の開発・検証が研究の中心である。
abstractThis study proposes using specific leaf metabolites, detectable by leaf reflectance spectra, for more precise identification of heat and drought stress compared to traditional methods.
Functional diversity can be assessed remotely from optical sensors using vegetation index-based plant traits. Without effective corrections, employed reflectance values are affected by absorption and scattering processes in the atmosphere and on the ground, which modify radiance and irradiance values used for the reflectance retrieval. Additionally, the anisotropic nature of vegetation canopies induces observation and illumination angle-dependent reflectance variations. Often, however, the reflectance retrieval is not accurate enough to compensate for these effects in the atmosphere and on the surface, resulting in uncertain reflectance values. Furthermore, the effects in retrieved reflectance values propagate into derived products, like the vegetation indices used for calculating functional diversity, where they manifest as apparent differences between temporally close observations of the same area. A key to compensating for these effects lies in the capacity and consideration of several processing steps, such as atmospheric, topographic, and anisotropy correction. To date, it is unknown how these effects and their correction influence the estimation of functional richness. Here, we estimate functional richness based on three differently retrieved reflectance datasets in the overlapping area of three consecutively acquired flight lines with short temporal differences but with three distinct acquisition geometries. We analyze how atmospheric, topographic, and anisotropy effects influence functional richness estimates and how functional richness varies due to different observation and illumination angles. We show that reflectance data before correction for atmospheric, topographic, and anisotropy effects yield up to 15% larger median functional richness estimates compared to data after respective corrections. We discuss under which circumstances comprehensive data processing can reduce between-observation differences. Furthermore, we show that resulting functional richness estimates correlate with the number of shaded pixels (r 2 ≈ 0.7). Consequently, observations in the solar principal plane with more or fewer shadows can lead to larger or smaller functional richness estimates and to differences compared to observations perpendicular to the solar principal plane. We conclude with recommendations concerning best-suited data processing and acquisition geometry for reliable and repeatable assessments of functional richness from optical remote sensing data and discuss applications to aerial and space-based observations of functional diversity.
Why it matches plant phenotyping methods航空画像分光の反射率補正と取得ジオメトリが植物形質に基づく機能的豊かさの推定へ与える影響を検証し、信頼性・再現性のための処理と取得条件を提案しており、植物表現型推定手法が中心である。
titleData processing and acquisition geometry impact the estimation of plant trait-based functional richness from airborne imaging spectroscopy
This study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial–spectral resolution airborne data. The research combines the Discrete Anisotropic Radiative Transfer (DART) model with the Dynamic L-System-based Architectural maize (DLAmaize) growth model to simulate field reflectance. Comparison with the 1D RTM SAIL revealed limitations in representing row structure effects, field slope, and complex light–canopy interactions. Novel Global Sensitivity Analyses (GSA) were carried out using dependence-based methods to overcome limitations of traditional variance-based approaches, enabling better characterization of hyperspectral sensitivity to changes in leaf biochemistry, canopy architecture, and soil moisture. GSA provided complementary results to assess estimation uncertainties of the proposed traits retrieval method across growth stages. A hybrid inversion framework combining DART simulations with an active learning strategy using Kernel Ridge Regression was implemented for traits estimation. The approach was validated using ground data and HyPlant-DUAL airborne hyperspectral images from two field campaigns in 2018 and achieved high retrieval accuracy of key maize traits: leaf area index (LAI, R 2 =0.91, RMSE=0.42 m 2 /m 2 ), leaf chlorophyll content (LCC, R 2 =0.61, RMSE=3.89 μ g/cm 2 ), leaf nitrogen content (LNC, R 2 =0.86, RMSE=1.13 × 10 −2 mg/cm 2 ), leaf dry matter content (LMA, R 2 =0.84, RMSE=0.15 mg/cm 2 ), and leaf water content (LWC, R 2 =0.78, RMSE=0.88 mg/cm 2 ). The validated models were used to generate two-date 10 m resolution maps, showing good spatial consistency and traits dynamics. The findings demonstrate that integrating 3D RTMs with dynamic growth models is suited for maize trait mapping from hyperspectral data in varying growing conditions.
Why it matches plant phenotyping methods3D放射伝達モデル、動的生長モデル、ハイパースペクトル画像、機械学習を統合し、トウモロコシ形質推定法を開発・検証しており、形質取得が研究の中心である。
abstractThis study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial–spectral resolution airborne data.
This study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial–spectral resolution airborne data. The research combines the Discrete Anisotropic Radiative Transfer (DART) model with the Dynamic L-System-based Architectural maize (DLAmaize) growth model to simulate field reflectance. Comparison with the 1D RTM SAIL revealed limitations in representing row structure effects, field slope, and complex light–canopy interactions. Novel Global Sensitivity Analyses (GSA) were carried out using dependence-based methods to overcome limitations of traditional variance-based approaches, enabling better characterization of hyperspectral sensitivity to changes in leaf biochemistry, canopy architecture, and soil moisture. GSA provided complementary results to assess estimation uncertainties of the proposed traits retrieval method across growth stages. A hybrid inversion framework combining DART simulations with an active learning strategy using Kernel Ridge Regression was implemented for traits estimation. The approach was validated using ground data and HyPlant-DUAL airborne hyperspectral images from two field campaigns in 2018 and achieved high retrieval accuracy of key maize traits: leaf area index (LAI, R²=0.91, RMSE=0.42 m²/m²), leaf chlorophyll content (LCC, R²=0.61, RMSE=3.89 μg/cm²), leaf nitrogen content (LNC, R²=0.86, RMSE=1.13 × 10⁻² mg/cm²), leaf dry matter content (LMA, R²=0.84, RMSE=0.15 mg/cm²), and leaf water content (LWC, R²=0.78, RMSE=0.88 mg/cm²). The validated models were used to generate two-date 10 m resolution maps, showing good spatial consistency and traits dynamics. The findings demonstrate that integrating 3D RTMs with dynamic growth models is suited for maize trait mapping from hyperspectral data in varying growing conditions.
Why it matches plant phenotyping methods3D放射伝達モデル、成長モデル、ハイパースペクトル画像、機械学習を統合し、トウモロコシの複数形質を推定する手法を開発・検証しており、植物表現型取得が研究の中心である。
abstractThis study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial–spectral resolution airborne data.
Accurate knowledge of vegetation water content (VWC) and crop height is crucial for agricultural management, environmental monitoring, and for satellite-based retrieval algorithms for geophysical variables. Traditional methods to estimate VWC, primarily rely on optical indices, which has limitations of biomass saturation, and sensitivity to atmospheric conditions. This study introduces a novel application of geospatial foundation models (GFMs), leveraging extensive, unlabeled datasets through self-supervised learning to enhance the skill of VWC and crop height estimation. We developed a comprehensive model integrating Sentinel-1 A C-band SAR and Sentinel-2 A/B indices with weather parameters to estimate soybean and corn VWC and crop height. Our research study area spans a variety of climatic zones and management practices, from the humid continental climate of Iowa and Michigan to the subtropical environment of Florida, encompassing both irrigated and non-irrigated fields as well as diverse tillage practices. We compared the performance of Single-Task Learning GFM (STL-GFM), Multi-Task Learning GFM (MTL-GFM), and machine learning techniques including Random Forest (RF), and XGBoost (XGB) to evaluate their effectiveness in estimating VWC and crop height. Results demonstrated that STL-GFM outperforms other methods in accuracy and generalizability. For VWC estimation, STL-GFM achieved R² values of 0.90 and 0.89 for soybean and corn, respectively. For crop height, R² values reached 0.95 for soybean and 0.98 for corn. The integration of SAR, optical, and climate data provided more reliable estimations than using individual data sources. Feature importance analysis identified NDVI, NDWI, VH backscatter, and precipitation as key drivers for accurate VWC and height estimations. The red-edge band emerged as significant for VWC estimation but showed limited importance for height prediction. Notably, surface roughness demonstrated a noticeable impact on corn VWC and height estimations, while soil moisture exhibited less influence than initially anticipated. Notably, without directly incorporating soil moisture and surface roughness data, but by including diverse field conditions in training and validation, the STL-GFM model demonstrated strong generalization capabilities. This study highlights the potential of GFMs in advancing crop monitoring techniques, offering more reliable data for precision agriculture, and supporting sustainable farming practices across diverse agricultural landscapes.
Why it matches plant phenotyping methodsSAR・光学衛星データと自己教師あり地理空間基盤モデルを統合し、作物のVWCと草丈を推定する手法を開発・比較・評価しており、植物表現型の取得・抽出が中心である。
abstractThis study introduces a novel application of geospatial foundation models (GFMs), leveraging extensive, unlabeled datasets through self-supervised learning to enhance the skill of VWC and crop height estimation.
Satellite land surface temperature (Ts) provides valuable information on vegetation drought stress via its physical linkage to plant stomatal activity and transpiration. New-generation geostationary satellites offer opportunities to monitor sub-diurnal variations in Ts and thus track plant physiological stress response occurring at sub-daily timescales. Nevertheless, the potential of satellite Ts and its derived metrics for early detection of vegetation drought stress before visible canopy changes occur has not been widely assessed. Here, we developed a parsimonious Surface-Air Temperature Difference Anomaly (SATDA) method for tracking vegetation drought stress using the cumulative sub-diurnal difference from late-morning to early-afternoon between Ts from the Himawari-8 geostationary satellite and hourly air temperature (Ta) from meteorological grids. SATDA utilised Ts−Ta as the physical driving gradient for sensible heat flux (H) to capture anomalous sensible heating due to reduced plant transpiration. We used SATDA to monitor the spatio-temporal patterns of the 2017–2019 Tinderbox Drought in southeast Australia. We benchmarked the skill of SATDA in forecasting visible drought-induced vegetation greenness decline against both conventional water availability-based indices (i.e., precipitation and soil moisture anomalies) and existing satellite Ts indices (i.e., Temperature Condition Index and Temperature Rise Index) across diverse climates and land covers. SATDA effectively captured a rapidly intensifying flash drought event at multi-week timescales (Jul to Sep 2019) embedded within the multi-year Tinderbox Drought, which contributed to detrimental impacts on agricultural production and increased wildfire risk. SATDA showed the best vegetation greenness forecast skill in the transitional semi-arid and sub-humid climates, with forecast correlation >0.5 at 32-day lead time. The advantage over water availability-based indices was more evident in woody-dominated ecosystems than herbaceous-dominated ecosystems, likely due to the importance of physiological regulations by trees during droughts such as deeper roots and stronger stomatal control. SATDA, based on Ts−Ta, showed overall better vegetation greenness forecasts than two Ts-only indices, especially in woody vegetation. Finally, SATDA showed consistently greater advantage over water availability-based and Ts-only indices in forecasting visible vegetation decline as the drought intensity increased. The parsimonious process-based SATDA method suits global-scale operational implementation to complement vegetation drought monitoring and early warning systems.
Why it matches plant phenotyping methods衛星温度と気象データから植物の干ばつストレスを推定するSATDA法を開発し、既存指標と比較検証しており、植物状態の取得手法が研究の中心である。
abstractHere, we developed a parsimonious Surface-Air Temperature Difference Anomaly (SATDA) method for tracking vegetation drought stress
Remote sensing of hyperspectral vegetation reflectance and solar-induced chlorophyll fluorescence (SIF) is essential for evaluating crop functionality and photosynthetic performance. While primarily applied in monocultures, these tools show promise in diverse cropping systems, enhancing ecological intensification. Plant-plant interactions in such systems can influence key physiological processes, such as photosynthesis, making SIF a valuable tool for evaluating how crop diversity affects photosynthetic function and productivity. However, detecting SIF in diverse stands remains challenging due to uncertainties in light re-absorption and scattering. To address these challenges, we propose a hybrid model inversion framework that combines canopy observations with physical modeling to derive leaf biochemical, canopy structural variables, and SIF spectra at leaf and photosystem levels. This approach employs a machine learning retrieval algorithm (MLRA), trained on synthetic spectra from radiative transfer model (RTM) simulations, to quantify re-absorption and scattering effects. Using the SpecFit retrieval algorithm, the temporal evolution of full-spectrum SIF at the canopy level can be derived. To downscale SIF to the photosystem level and retrieve its quantum yield, we corrected the canopy SIF spectrum for re-absorption and scattering effects calculated from TOC reflectance. Spectral measurements were gathered from field experiments conducted over three years, covering various growth stages of cereal and legume monocrops and their mixture. Our method accurately predicts important leaf biochemical and canopy structural variables, such as leaf area (LAI, R² = 0.75) and leaf chlorophyll content (LCC, R² = 0.91), and shows a general high retrieval performance for light absorption (fAPARCₕₗ, R² = 0.99 for the internal model validation). We confirmed the reliability of our method in modeling re-absorption and scattering processes by comparing canopy SIF downscaled to the leaf level with independent leaf-level SIF measurements. While the results show a good prediction accuracy in terms of fluorescence magnitude at the leaf level, we did not find a strong agreement of corresponding leaf and canopy measurements at the single plot level.
Why it matches plant phenotyping methods作物キャノピーのSIFを葉・光合成系レベルへ推定するハイブリッド逆解析法を開発し、独立測定との比較で検証しているため、植物生理表現型の取得手法が中心である。
abstractTo address these challenges, we propose a hybrid model inversion framework that combines canopy observations with physical modeling to derive leaf biochemical, canopy structural variables, and SIF spectra at leaf and photosystem levels.
Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multi-component analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas. TRUST-DART utilizes the DART model and requires inputs including at-surface radiance imagery, downwelling sky irradiance, a 3D mock-up with component classification, and standard DART parameters (e.g., spatial resolution and skylight ratio). This method produces maps of component emissivity and temperature. The accuracy of TRUST-DART is evaluated using both vegetation and urban scenes, employing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images and DART-simulated pseudo-ASTER images. Results show a residual radiance error is approximately 0.05 W/(m²·sr). In absence of the co-registration and sensor noise errors, the median residual error of emissivity is approximately 0.02, and the median residual error of temperature is within 1 K. This novel approach significantly advances our ability to analyze thermal properties of urban areas, offering potential breakthroughs in urban environmental monitoring and planning. The source code of TRUST-DART is distributed together with DART (https://dart.omp.eu).
Why it matches plant phenotyping methods植物を含む混合画素から温度・放射率という生理状態関連の形質を抽出する熱赤外リモートセンシング手法を開発し、植生シーンで精度検証しているため、都市監視用途を含むが方法論的中心性が高い。
abstractWe propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsトウモロコシとダイズの有効葉面積指数（LAI）をUAV画像から推定する新規手法の開発が題名で明示されており、植物形質の取得・推定が中心である。
titleNeRF-LAI: A hybrid method combining neural radiance field and gap-fraction theory for deriving effective leaf area index of corn and soybean using multi-angle UAV images
This study introduces a method for optimizing flight modes using unmanned aerial vehicles (UAVs) and light detection and ranging (LiDAR) technology, aiming for the efficient and accurate estimation of crop phenotypes in wide-narrow row planting patterns, for cotton. It proposes specialized flight plans that take into account the unique growth stages of cotton and recommends the s-along flight path, which is derived from a detailed analysis of the cross flight path, to facilitate effective and precise data collection. A comprehensive phenotypic index, labeled as ‘P', and a fitting function are developed to describe the relationship between flight parameters, paths, and digital elevation model (DEM) data. The study also introduces two flight strategies, one focusing on accuracy and the other on efficiency, utilizing a sophisticated multi-objective optimization method. Comparative analyses show that the s-along flight path significantly improves efficiency without sacrificing accuracy, compared to traditional cross flight path techniques. The use of high-precision prior DEM data greatly enhances the precision in estimating critical phenotypic parameters such as plant height (PH) and leaf area index (LAI), especially during key stages of canopy growth. By carefully adjusting flight height, speed, and overlap during different growth stages, an ideal balance is achieved between the precision and efficiency of data collection. These strategies markedly increase the accuracy of estimating phenotypic features (P > 0.75) and efficiency (by 42 %–44 %). This research highlights the potential of these approaches in facilitating large-scale phenotypic data collection for precision agriculture, demonstrating their ability to simultaneously improve data quality and operational efficiency. Future research will aim to expand the applicability and robustness of these methods across various planting conditions and crops, further enhancing essential tools for the advancement of precision agriculture practices and development.
Why it matches plant phenotyping methodsUAV LiDARの飛行経路・パラメータ最適化と多目的最適化を開発し、綿の草丈・葉面積指数などの表現型推定精度と効率を評価しており、表現型取得法が研究の中心である。
abstractThis study introduces a method for optimizing flight modes using unmanned aerial vehicles (UAVs) and light detection and ranging (LiDAR) technology, aiming for the efficient and accurate estimation of crop phenotypes
Crop models are essential for evaluating the effects of climate change on crop yields, optimizing agronomic practices, and guiding policy decisions to enhance food security. However, using traditional crop models, including both process-based and statistical models, for regional applications presents significant challenges. Process-based crop models often require extensive, locally-sensed inputs to drive the models, which are generally lacking at the regional level. Meanwhile, statistical crop models depend heavily on training data, but it is often difficult, or even impossible, to find high-quality training data on a large scale. Solar-induced chlorophyll fluorescence (SIF), a more physiologically based proxy for gross primary production (GPP), has shown good potential for estimating GPP and crop yield. We developed a practical SIF-based crop model driven by satellite SIF observations and three readily available datasets: air temperature, vapor pressure deficit, and soil moisture content. The key improvement of our research is to parameterize the fraction of open PSII reaction centers (qL) for crops, and incorporate variations in qL into the SIF-based estimation of crop GPP and yield. Using a leaf-level measurement system, we provided parameters for qL in corn and soybean. We showed that the simulated qL closely matches the measured qL, with R² > 0.95 and RMSE <0.05, even under conditions of high light and/or high temperature, whereas the performance of SIF alone significantly decreased under stress. By using SIF and qL within the mechanistic light response model, one can accurately estimate crop GPP without the need to parameterize various plant physiological processes or nutrient dynamics and management practices. This improvement substantially simplifies the model, reduces the need for driving variables and calibration data, and minimizes associated uncertainties. We applied the model to estimate corn and soybean yields in the U.S. Midwest for the period 2018–2023. A comparison with eddy covariance-based GPP measurements reveals that the simulated GPP accounts for 85 % of the variability in daily observed GPP for corn and 81 % for soybean. The model's performance at the regional scale was assessed by comparing it against county-level crop yield statistics. On average, the model captures 78 % of the county-level yield variability across more than 700 counties during the study period, achieving 76 % for corn and 81 % for soybean, with RMSE values of 14.47 Bu/Acre, and 4.09 Bu/Acre, respectively. The practical, yet mechanistic, SIF-based model introduced in this study represents a significant advance in regional and national crop yield estimation.
Why it matches plant phenotyping methodsSIF衛星観測とqL測定を統合した作物生理・収量推定モデルを開発し、葉レベル測定およびGPP・郡別収量で技術検証しており、植物状態の取得・推定手法が中心である。
abstractWe developed a practical SIF-based crop model driven by satellite SIF observations and three readily available datasets: air temperature, vapor pressure deficit, and soil moisture content.
Automatic collection of tree-level crown information is essential for sustainable forest management and fine carbon stock estimation. UAV-based light detection and ranging (LiDAR) and UAV-based multi-angle photogrammetry (UMP) data depict the 3D structure of forests at a fine-grained level by generating detailed point clouds, making them potential alternatives to labor-intensive forest inventories. However, the accuracy of the individual tree crown segmentation algorithms that have been developed is unstable in forest stands with high terrain undulation and high canopy density, mainly due to the various crown sizes and interlocking crowns resulting in varying degrees of over- or under-segmentation. Here, we propose self-similarity cluster grouping (SCG) algorithm for individual tree crown segmentation that integrates multivariable calculus of crown surfaces and spectral-texture-color spatial information of crown. Firstly, according to the property that DSM and its multi-order gradient information can characterize the crown surface variation and concavity-convexity features, first- and second-order edge detection operators were used to preliminarily determine the crown patch edges in order to reduce under-segmentation. Then, we developed a self-similarity weight function controlled by the spectral, texture and color spatial information of the tree crown patches to increase the similarity difference between adjacent crown patches of the same tree and those of neighboring trees, and designed the strategy for cluster grouping crown patches to complete individual tree crown segmentation. The performance of the proposed SCG algorithm was verified in Mytilaria, Red oatchestnu, Chinese fir and Eucalyptus plots in subtropical forests of China using LiDAR and UMP data. The overall accuracy of F-score (f) was above 0.85 for crown segmentation, and the rRMSE for crown width, crown area and crown circumference extractions reached 0.13, 0.22 and 0.14, respectively. On this basis, we evaluated the effect of spatial resolution of DSM on the segmentation accuracy of SCG algorithm, and found that the crown segmentation accuracy was proportional to the spatial resolution. Compared to the normalized cut algorithm, marker-controlled watershed algorithm and threshold-based cloud point segmentation algorithm, the SCG algorithm improved the overall accuracy f of individual tree crown segmentation by 0.06, 0.13 and 0.05 for LiDAR and 0.06, 0.21 and 0.10 for UMP, respectively. Furthermore, the effectiveness and generalizability of the SCG algorithm was verified in other Mytilaria, Red oatchestnut, Chinese fir and Eucalyptus plots in subtropical forests and Larch and Chinese pine plots in temperate forests using UMP data. The crown segmentation accuracy was better than 0.82, and the crown width extraction accuracy was up to 89 %. Overall, our proposed SCG algorithm reduces the over- and under-segmentation in complex forest structures and provides technical support for accurate crown information extraction at both plot and forest stand levels.
Why it matches plant phenotyping methodsUAV LiDAR・写真測量から個体樹冠を分割し、樹冠幅・面積・周長を抽出するSCG手法の開発、比較検証、汎化評価が論文の中心であるため。
abstractHere, we propose self-similarity cluster grouping (SCG) algorithm for individual tree crown segmentation that integrates multivariable calculus of crown surfaces and spectral-texture-color spatial information of crown.
Canopy height mapping is critical for assessing forest structure, forest resilience, carbon stocks, habitat, and biodiversity, all of which are threatened by changing climate and weather extremes. While current tools utilizing lidar (e.g., GEDI) and multispectral imagery (e.g., Landsat, Sentinel-2, airborne imagery) produce canopy height products, significant challenges remain, particularly in capturing fine-scale spatial details across large areas with high frequency. PlanetScope CubeSat imagery, with its 3 m spatial resolution and near-daily frequency, offers a unique opportunity to estimate woody plant structure by capturing fine-scale texture and temporal patterns that shift throughout the year. In this study, we adapted a 3D Spatio-Temporal Convolutional Neural Network (ST-CNN) to estimate canopy height at 3 m resolution, utilizing sequential PlanetScope time series over five months, summer Sentinel-1 radar imagery, and solar illumination layers as inputs. We generated a large and diverse reference database covering 2,296 sample scenes (each scene = 768 × 768 m, totaling ∼135,000 ha) using a semi-automatic labeling process that leverages 23 aerial lidar surveys conducted in California between 2016 and 2021. Trained on a random selection of 2,046 scenes, the accuracy assessment on the remaining 250 scenes demonstrates strong performance across various ecoregions, capturing 80.8% of the observed variance in live canopy height with a mean absolute error (MAE) of 3.6 m and a bias of -0.53 m compared with aerial lidar. Analysis of all 681 GEDI footprints over the same testing scenes estimates the MAE of 6.5 m, bias of -1.82 m, and R2 of 0.58 for the GEDI L2A Vector Canopy Top Height RH98 product. The ST-CNN model accurately identifies heterogeneous canopy structures, and shows sensitivity to canopies reaching 50 to 60 m in height. We found a major contribution from the PlanetScope time series, compared to a single PlanetScope image, and marginal benefits of including Sentinel-1 and terrain-based solar irradiance layers to improve performance on dense canopies or diverse topography. Example applications demonstrate the ability to generalize to different years, maintaining consistent predictions between years and capturing changes in canopy height over a seven year period (2017–2023) within 400 plots representing regrowth, minimal change, selective logging, and clear cut areas. We also demonstrate improved canopy height estimation compared to existing products from Landsat (MAE = 8.41 m) and Sentinel-2 (MAE = 7.19 m). A visualization tool displays our data alongside existing products for the Sierra Nevada in 2022. The Planet ST-CNN model, using a 15-day PlanetScope satellite time series, offers a scalable approach for annual canopy height estimation in California, achieving a high level of detail, often down to the resolution of individual trees. This improved capability of forest structure monitoring is expected to deliver crucial, comprehensive data for assessing and tracking forest carbon, biodiversity, and vulnerability, ultimately facilitating data-driven strategies to improve forest resilience.
Why it matches plant phenotyping methods衛星時系列画像と深層学習により、樹冠高という植物構造形質を推定する手法を開発・検証しており、手法が研究の中心である。
abstractIn this study, we adapted a 3D Spatio-Temporal Convolutional Neural Network (ST-CNN) to estimate canopy height at 3 m resolution
Light photometric and polarimetric characteristics are crucial for describing the optical properties of leaf reflections, which play an essential role in investigating biochemical and surface structural trait inversion and radiative balance between vegetation and atmospheric system. Although several physical models are available, research on a comprehensive model that accounts for both photometric and polarimetric characteristics and incorporates biochemical and surface structural traits is still inadequate. In this study, we introduced PROPOLAR, a leaf model that considered leaf reflection in terms of polarized and unpolarized components and linked leaf reflection to leaf traits. PROPOLAR employed PROSPECT to simulate non-polarized component associated with biochemical traits, while used a three-parameter function (linear coefficient, refractive index factor, and roughness of leaf surface) to simulate the polarized component. The model was validated using a dataset (composed of both photometric and polarimetric measurements) collected from 533 samples of 13 plant species under various illumination-viewing geometries. The results showed that PROPOLAR outperformed PROSPECT and PROSPECULAR (a leaf model charactering BRF) in simulating light intensity (R² = 0.98), and effectively simulated bidirectional polarization reflectance factor (BPRF) and degree of linear polarization (Dolp) across a wide spectral range (450–2300 nm) and species, with R² = 0.92, and 0.80, respectively. Furthermore, PROPOLAR enhanced the accuracy of PROSPECT and showed comparable accuracy with PROSPECULAR in the inversion of biochemical traits from the multi-angular polarization measurements, including chlorophyll (R² = 0.89, RMSE = 12.83 μg/cm²), equivalent water thickness (R² = 0.90, RMSE = 0.0032 g/cm²), and leaf mass per area (R² = 0.38, RMSE = 0.0031 g/cm²), due to the incorporation of polarization reflection and a linear coefficient during calibration. Notably, PROPOLAR can invert roughness and showed reasonable consistency with measured roughness (R² = 0.61). These results demonstrated the effectiveness of PROPOLAR in simulating both photometric and polarimetric properties of leaf reflection, as well as its potential for biochemical and surface structural trait inversion. PROPOLAR may advance remote sensing applications in vegetation management by integrating photometric and polarimetric properties.
Why it matches plant phenotyping methods葉の光学・偏光反射から生化学的および表面構造形質を推定するPROPOLARモデルを開発し、多数試料で検証しており、植物表現型取得・推定手法が中心である。
abstractIn this study, we introduced PROPOLAR, a leaf model that considered leaf reflection in terms of polarized and unpolarized components and linked leaf reflection to leaf traits.
Remotely sensed top-of-the-canopy (TOC) SIF is highly impacted by non-physiological structural and environmental factors that are confounding the photosystems' emitted SIF signal. Our proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach, capable of accounting physically for the main confounding factors, i.e., SIF scattering and reabsorption within a leaf, by canopy structures, and by the soil beneath. Here, we propose a novel SIF downscaling method that separates the structural component from the functional physiological component of TOC SIF signal by using the 3D Discrete Anisotropic Radiative Transfer (DART) model coupled with the leaf-level fluorescence model Fluspect-CX, and estimates the Fluorescence Quantum Efficiency (FQE) at photosystem level. The method was first applied on in-situ diurnal measurements acquired at the top of the canopy of an alfalfa crop with a near-distance point-measuring FloX system. The retrieved photosystem-level FQE diurnal courses correlated significantly with photosynthetic yield of PSII measured by an active leaf florescence instrument MiniPAM (R = 0.87, R² = 0.76 before and R = −0.82, R² = 0.67 after 2.00 pm local time). Diurnal FQE trends of both photosystems jointly were descending from late morning 9.00 am till afternoon 4.00 pm. A slight late-afternoon increase, observed for three days between 4.00 and 7.00 pm, could be attributed to an increase in FQE of PSI that was retrieved separately from PSII. The method was subsequently extended and applied to airborne SIF images acquired with the HyPlant imaging spectrometer over the same alfalfa field. While the input canopy SIF radiance computed by two different methods, i) a spectral fitting method (SFM) and ii) a spectral fitting method neural network (SFMNN), produce broad and irregularly shaped (skewed) histograms (spatial coefficients of variation: CV = 29–35 % and 14–20 %, respectively), the retrieved HyPlant per-pixel FQE estimates formed significantly narrower and regularly bell-shaped near-Gaussian histograms (CV = 27–34 % and 14–17 %, respectively). The achieved spatial homogeneity of resulting FQE maps confirms successful removal of the TOC SIF radiance confounding impacts. Since our method is based on direct matching of measured and physically modelled canopy SIF radiance, simulated by 3D radiative transfer, it is versatile and transferable to other canopy architectures, including structurally complex canopies such as forest stands.
Why it matches plant phenotyping methods3D放射伝達モデルと蛍光モデルを用いて、作物の光合成系レベルの蛍光量子効率を推定する手法を開発し、地上・航空観測で検証・適用している。植物生理状態の取得手法が研究の中心である。
abstractOur proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach
The retrieval of wheat green leaf area index (LAIG) from satellite imagery is critical for monitoring crop growth and assessing food security. Numerous vegetation indices (VIs) derived from spectral reflectance have been widely used to estimate LAIG. In particular, red edge VIs can mitigate the confounding effect of multiple factors, such as the soil background and leaf inclination angle variation, and typically are highly correlated with LAIG. However, their relationship to LAIG tends to be affected by variations in leaf chlorophyll content (LCC), because the position of the red edge of vegetation spectra shifts with changes in LCC. This issue directly limits the operational use of VI-LAIG models, especially those employing red-edge bands. Therefore, to reduce the sensitivity of VI-LAIG relationships to LCC variation, this study proposed an innovative approach, called the Difference Combination between Spectral Indices (DCSI). Using synthetic data simulated with the PROSAIL radiative transfer model, we tested the dependence of the algebraic difference between common VIs on LCC. The results show that many combinations of VIs are insensitive to LCC variation. The newly developed DCSI combination between the Sentinel-2 red edge position (S2REP) and B6-red edge band (RE2) (i.e., DCSI(S2REP&RE2)), produces the most accurate LAIG model when LCC varies. We also modified the constant of this DCSI combination, to develop the Sentinel-2 modified red edge position (S2MREP) for LAIG retrievals. In comparison to traditional VI-LAIG models, the S2MREP-LAIG model has higher accuracy, with Rcal2 of 0.76 in calibration, and in validation Rval2 of 0.72 and RRMSE of 23.61 %. In addition, the S2MREP-LAIG model (RRMSE=28.64 %) also outperforms the existing Sentinel-2 LAI product (RRMSE=38.20 %) in the retrieval of wheat LAIG. In summary, the proposed DCSI approach and S2MREP effectively mitigate the impact of LCC variations on LAIG retrievals, thus facilitating the large-scale retrieval of LAIG and the spatial mapping of wheat LAIG.
Why it matches plant phenotyping methods小麦の緑葉面積指数（LAIG）という植物形質を衛星スペクトルから推定する手法を開発し、既存モデルおよび製品と精度比較・検証しており、フェノタイピング手法が中心です。
abstractthis study proposed an innovative approach, called the Difference Combination between Spectral Indices (DCSI).
We present a new method for estimating biophysical parameters from Earth Observation (EO) data using a crop-specific empirical model based on the PROSAIL Radiative Transfer (RT) model, called an ‘archetype’ model. The first-order model presented uses maximum biophysical parameter magnitude, phenological and soil parameters to describe the spectral reflectance (400–2500 nm) of vegetation over time. The approach assumes smooth variation and archetypical coordination of crop biophysical parameters over time for a given crop. The form of coordination is learned from a large sample of observations. Using Sentinel-2 observations of maize from Northeast China in 2019, we map reflectance to biophysical parameters using an inverse model operator, synchronise the parameters to a consistent time frame using a double logistic model of LAI, then derive the model archetypes as the median value of the synchronised samples. We apply the model to estimate time series of biophysical parameters for different cereal crops using an ensemble framework with a weighted K-nearest neighbour solution, and validate the results with ground measurements of different crops collected near Munich, Germany in 2017 and 2018. The results show R values greater than 0.8 for leaf area index (LAI) and leaf brown pigment content (Cbrown), with an RMSE of 0.94 m2/m2 for LAI and 0.15 for Cbrown. The chlorophyll content (Cab) and canopy water content (CCw) were retrieved at a higher level of accuracy, with R values around 0.9 and an RMSE of 6.59μg/cm2 for Cab and 0.03 g/cm2 for CCw. Comparison of forward-modelled hyperspectral reflectance with independent ground measures shows that the retrieved parameters account for 90% of the variation in canopy reflectance, with an overall RMSE of around 0.05 in reflectance units. The retrievals for all terms are mostly within 1σ when measurement and prediction uncertainty are taken into account, except for some early and late season issues in leaf and canopy water due to the complexity of canopy structure and understory during these periods. The approach provides a new form of constraint for the simultaneous estimation of biophysical parameters from EO and greatly reduces the rank of the problem. It is suitable for monitoring crop conditions where biophysical parameters vary smoothly over time consistently with each archetype form. The approach can be refined for other canopy types and canopy representations and could provide strong constraints on expected smoothly-varying canopy features to aid in the interpretation of EO signals across different regions of the electromagnetic spectrum.
Why it matches plant phenotyping methods作物のLAI、色素、葉・樹冠水分などの植物形質をSentinel-2データから推定する新しい逆モデル手法を開発し、地上観測で検証しているため、フェノタイピング手法が中心である。
abstractWe present a new method for estimating biophysical parameters from Earth Observation (EO) data using a crop-specific empirical model based on the PROSAIL Radiative Transfer (RT) model, called an ‘archetype’ model.
Foliage area volume density (FAVD) and leaf chlorophyll content (LCC) are two key traits closely linked to the structure and physiological status of trees. However, their physically-based retrieval at the individual tree level has remained challenging due to the complex interactions of scattering and absorption within the irregularly shaped tree crowns, as well as multiple scattering among neighboring trees, particularly in the near-infrared (NIR) spectrum. In this study, we proposed a tree-specific retrieval strategy that leverages unmanned aerial vehicle (UAV) imagery and corresponding photogrammetric point clouds to establish a tree-specific spatial adjacency constraint within the three-dimensional (3D) RTM-based inversion procedure for each individual tree. Unlike previous approaches that relied exclusively on pixel-level information from the region of interest, the proposed method fully accounted for the multiple scattering from adjacent trees and explicitly incorporates the irregularity of tree crown shapes. In the RTM-based prediction of the spectral reflectance of a focal tree (i.e., the target tree), the structures of adjacent trees were integrated alongside the focal tree, thereby forming a spatial adjacency constraint. This ensures that the scattering regime of the focal tree in the simulated scenario aligns with that of the actual scenario. The proposed method was assessed using both real UAV data and synthetic datasets. The results showed that tree-level retrieval under the adjacency constraint was highly consistent with reference (RRMSE of less than 0.22), whereas retrieval without the adjacency constraint exhibited substantial mis-estimation, particularly for FAVD (RRMSE of up to 0.44). Although the multiple scattering from adjacent trees was primarily influenced by the illumination geometry and tree canopy cover (TCC), sensitivity analysis of the sun zenith angle (SZA) and TCC revealed that retrieval accuracy slightly improved with a decreasing SZA and an increasing TCC. This improvement can be attributed to the enhanced treatment of multiple scattering under these conditions. These findings underscore the effectiveness of the tree-specific retrieval strategy for accurately estimating plant functional traits across forest stands. Moreover, they suggest the potential for monitoring functional diversity and long-term ecosystem process at the forest landscape scale through the use of functional traits.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と3D放射伝達モデルを統合し、個体樹木のFAVDおよび葉クロロフィル含量を推定する手法の開発・検証が中心である。
abstractIn this study, we proposed a tree-specific retrieval strategy that leverages unmanned aerial vehicle (UAV) imagery and corresponding photogrammetric point clouds to establish a tree-specific spatial adjacency constraint within the three-dimensional (3D) RTM-based inversion procedure for each individual tree.
Croplands are essential for food security but also impact the environment, biodiversity, and climate. Understanding, monitoring, modeling, and managing these impacts require accurate, comprehensive information on cropland vegetation cover. This study aimed to continuously monitor the state and vegetative processes of cropland, focusing on the assessment of bare soil and its cover with photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) at the national level. We employed regression-based unmixing techniques using time series of Sentinel-2 and Landsat imagery to quantify cover fractions of NPV, PV, and soil during the agricultural growing season. Our approach extends existing spectral unmixing methods by incorporating a novel soil-specific unmixing process based on a soil reflectance composite. The extension accounts for variations in the spectral characteristics of soils which is particularly relevant for large-scale monitoring of annually cultivated croplands, as the spectral soil properties can vary considerably at national level and periods of bare soil are frequent. All cover fractions were predicted with mean absolute errors between 0.13 and 0.19. Introducing soil-specific unmixing reduced the mean absolute error of the predictions for soil by 11.3 % and NPV by 15.1 % without compromising PV predictions, particularly benefiting areas with bright soils. These findings demonstrate the efficacy of our method in accurately predicting crop cover throughout the cultivation period and underline the added value of incorporating the soil adjustment into the unmixing workflow. The contributions of this research are twofold: first, it provides essential data for the continuous monitoring of cropland cover, supporting agricultural carbon cycle and soil erosion modeling. Second, it enables further investigation into cropland management practices, such as cover cropping and tillage, through time series analysis techniques. This work underscores the potential of advanced spectral unmixing methods for enhancing agricultural monitoring and management strategies.
Why it matches plant phenotyping methods衛星時系列画像のスペクトルアンミキシングを改良し、耕地の光合成・非光合成植生被覆率を定量化・検証しており、植物状態の取得手法が中心である。
abstractWe employed regression-based unmixing techniques using time series of Sentinel-2 and Landsat imagery to quantify cover fractions of NPV, PV, and soil during the agricultural growing season.
Remotely sensed top-of-the-canopy (TOC) SIF is highly impacted by non-physiological structural and environmental factors that are confounding the photosystems' emitted SIF signal. Our proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach, capable of accounting physically for the main confounding factors, i.e. , SIF scattering and reabsorption within a leaf, by canopy structures, and by the soil beneath. Here, we propose a novel SIF downscaling method that separates the structural component from the functional physiological component of TOC SIF signal by using the 3D Discrete Anisotropic Radiative Transfer (DART) model coupled with the leaf-level fluorescence model Fluspect-CX, and estimates the Fluorescence Quantum Efficiency (FQE) at photosystem level. The method was first applied on in-situ diurnal measurements acquired at the top of the canopy of an alfalfa crop with a near-distance point-measuring FloX system. The retrieved photosystem-level FQE diurnal courses correlated significantly with photosynthetic yield of PSII measured by an active leaf florescence instrument MiniPAM ( R = 0.87, R 2 = 0.76 before and R = −0.82, R 2 = 0.67 after 2.00 pm local time). Diurnal FQE trends of both photosystems jointly were descending from late morning 9.00 am till afternoon 4.00 pm. A slight late-afternoon increase, observed for three days between 4.00 and 7.00 pm, could be attributed to an increase in FQE of PSI that was retrieved separately from PSII. The method was subsequently extended and applied to airborne SIF images acquired with the HyPlant imaging spectrometer over the same alfalfa field. While the input canopy SIF radiance computed by two different methods, i) a spectral fitting method (SFM) and ii) a spectral fitting method neural network (SFMNN), produce broad and irregularly shaped (skewed) histograms (spatial coefficients of variation: CV = 29–35 % and 14–20 %, respectively), the retrieved HyPlant per-pixel FQE estimates formed significantly narrower and regularly bell-shaped near-Gaussian histograms (CV = 27–34 % and 14–17 %, respectively). The achieved spatial homogeneity of resulting FQE maps confirms successful removal of the TOC SIF radiance confounding impacts. Since our method is based on direct matching of measured and physically modelled canopy SIF radiance, simulated by 3D radiative transfer, it is versatile and transferable to other canopy architectures, including structurally complex canopies such as forest stands. • A novel solar-induced fluorescence (SIF) downscaling method based on DART modeling. • Method removes confounding structural impacts from top-of-canopy SIF observations. • Applied to in-situ SIF measurements, it produced FQE diurnal courses of alfalfa crop. • Adapted to airborne SIF images, it mapped FQE spatial variation.
Why it matches plant phenotyping methodsDARTとFluspect-CXを統合し、作物の光合成系レベルの蛍光量子効率を推定するセンシング・解析手法を開発し、地上および航空観測で検証・適用している。植物生理状態の取得が研究の中心である。
abstractHere, we propose a novel SIF downscaling method that separates the structural component from the functional physiological component of TOC SIF signal by using the 3D Discrete Anisotropic Radiative Transfer (DART) model coupled with the leaf-level fluorescence model Fluspect-CX, and estimates the Fluorescence Quantum Efficiency (FQE) at photosystem level.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と3D放射伝達モデルを用い、個々の樹木レベルの形質を推定する手法が題名で明示されており、植物表現型の取得・計算推定が研究の中心です。
titleSeeing into individual trees: Tree-specific retrieval of tree-level traits using 3D radiative transfer model and spatial adjacency constraint from UAV multispectral imagery
The intensity and spectral properties of solar-induced chlorophyll fluorescence (SIF) carry valuable information on plant photosynthesis and productivity, but are also influenced by leaf and canopy structure. Physically based models provide a quantitative means to investigate how SIF intensity and spectra propagate and scale from the photosystem to the leaf and to the canopy levels. However, the validation of canopy SIF models is limited by the lack of methods that combine direct, independent, and complementary measurements of the full fluorescence spectrum at the leaf and canopy levels. Here, we propose a novel validation approach that combines in situ measurements of leaf and canopy fluorescence spectra. The approach is demonstrated with measurements in a rice crop at two contrasting stages of canopy development. We measured leaf reflectance, transmittance, and fluorescence spectra in situ, and subsequently inverted leaf structural and biochemical parameters and determined the leaf fluorescence quantum efficiency (FQE) using the Fluspect-Cx model. Two FQE inversion methods (Inversion-IIA and Inversion-IIB) were tested for the forward simulation of leaf fluorescence spectra. Leaf fluorescence spectra were then scaled up to the canopy level using 1D, 2D, and 3D radiative transfer schemes (SCOPE, mSCOPE, and DART), and compared with the direct canopy fluorescence spectral observations measured under red, green, blue, and white illumination. The validation results demonstrate that accounting for 3D canopy structure, as in the DART model, is critical to successfully scale the fluorescence spectrum from the leaf to the canopy level, whereas 1D SCOPE or even 2D mSCOPE were unable to fully reproduce the canopy fluorescence spectra. The results also demonstrate that the Inversion-IIB method matches relatively well the measurements with mean relative absolute errors (MRAE) of 20 %, 37 %, and 43 % versus Inversion-IIA with mean relative absolute errors (MRAE) of 62 %, 100 %, and 108 % for DART, mSCOPE, and SCOPE, respectively. We suggest that our validation approach is transferable to other plant species and canopy geometries, providing a means to standardize and evaluate the performance of canopy SIF models and improve our understanding of canopy SIF observations.
Why it matches plant phenotyping methods葉・群落の蛍光スペクトルを用いてSIF放射伝達モデルを検証・比較する手法が研究の中心であり、植物の光合成状態に関わる生理形質を測定する。
abstractHere, we propose a novel validation approach that combines in situ measurements of leaf and canopy fluorescence spectra.
Accurate and reliable prediction of leaf traits is crucial for understanding plant adaptations to environmental variation, monitoring terrestrial ecosystems, and enhancing comprehension of functional diversity and ecosystem functioning. Currently, various approaches (e.g., statistical, physical models) have been developed to estimate leaf traits through hyperspectral remote sensing and leaf spectroscopy. However, the absence of high-performing, transferable, and stable models across various domains of space, plant functional types (PFTs) and seasons hinder our ability to quantify and comprehend spatiotemporal variations in leaf traits. This study proposes robust and highly transferable models for better predicting leaf traits with hyperspectral reflectance. Initially, three datasets were assembled, pairing common leaf traits — chlorophyll (Chla+b), carotenoids (Ccar), leaf mass per area (LAM), equivalent water thickness (EWT) — with leaf spectra measurements collected across diverse geographic locations in the U.S. and Europe, PFTs, and seasons. Measurements were acquired using spectroradiometers (e.g., ASD FieldSpec 3/4/Pro and SVC HR-1024i) with integrating spheres, leaf clips, and contact probes. We then developed transfer learning-based hybrid models that incorporated the domain knowledge of radiative transfer models (RTMs) through pretraining processes and were well-constrained by fine-tuning with field measurements. Through comparison with other state-of-the-art statistical models, including partial-least squares regression (PLSR) and Gaussian Process Regression (GPR), as well as pure physical models, we found that the proposed transfer learning models achieved better predictive performance and higher transferability. Specifically, compared to other statistical models and pure RTMs, the transfer learning model exhibited higher coefficient of determination (R²) values with range of 0.01 to 0.79, lower normalized root mean square error (NRMSE) with range of 0.06 % to 33.25 % in model performance. Additionally, the models exhibited improved transferability, with higher R² values range from 0.04 to 0.32, lower NRMSE range from 0.08 % to 30.81 %. The findings underscore that transfer learning models through integrating domain knowledge from RTMs and limited observations, can harness the advantages of both RTMs and statistical models and serve as a promising approach for effectively predicting leaf traits.
Why it matches plant phenotyping methods分光計測と転移学習モデルにより葉形質を推定する手法の開発・比較検証が研究の中心であり、植物フェノタイピング手法に該当する。
abstractThis study proposes robust and highly transferable models for better predicting leaf traits with hyperspectral reflectance.
More of the Amazon rainforest is disturbed each year than completely deforested, but the impact of these disturbances on the carbon cycle remains poorly understood. Recent algorithmic advances using optical and radar remote sensing have improved detection of disturbances at fine spatiotemporal resolution, but quantifying changes in forest structure and biomass associated with these detected disturbances has proven challenging. The Global Ecosystem Dynamics Investigation (GEDI), a spaceborne lidar mission collecting data from 2019 to 2023, provides an opportunity to address this problem. GEDI captured billions of measurements of forest height, plant area, and understory structure within ~25-m diameter footprints scattered across the tropics. Though the instrument had no guaranteed repeat cycle, it sometimes sampled nearby locations twice; some of these spatially near-coincident footprints happened to measure forest structure before and after a detected disturbance, providing information on how disturbance affected aboveground biomass. In this study, we developed an efficient general-purpose open-source pipeline for identifying spatially coincident footprints, which is a computationally complex task, and used the pipeline to find over 13,700 footprint pairs with intervening disturbance events across the Amazon biome. We also identified a set of ~65,000 spatially near-coincident footprint pairs that lacked an intervening disturbance but came from regions with similar disturbance threat; these provided a control dataset to evaluate the effectiveness of estimating forest structure and biomass changes with nearby footprints. Analysis of this Amazon-wide dataset demonstrated that GEDI was able to measure statistically significant canopy height and biomass deficits following non-stand-replacing disturbances as small as 900 m² (30 m × 30 m). GEDI's unique three-dimensional view of forest structure also reflected the effects of different intensities of fire disturbance, including 20% of burned areas where the upper canopy retained most of its height, but the understory suffered substantial foliage losses. Finally, we modeled the relationship between Landsat and Sentinel-1 disturbance detection parameters and GEDI-estimated percent biomass loss, showing that certain satellite-derived intensity metrics are correlated with increasing biomass loss and identifying temporal trends in biomass loss and recovery following disturbance. This work represents an important step towards the development of a pan-tropical, spatially explicit system for tracking carbon losses and structural changes arising from forest disturbance.
Why it matches plant phenotyping methodsGEDIの反復フットプリントを用いて森林の樹冠高・バイオマス・構造変化を推定するオープンソース解析パイプラインを開発し、対照データセットで性能を評価しており、植物形質推定法が研究の中心です。
abstractwe developed an efficient general-purpose open-source pipeline for identifying spatially coincident footprints
Leaf chlorophyll content (LCC) is an important indicator of photosynthetic capacity. Sun-induced chlorophyll fluorescence (SIF) is an optical signal emitted from the leaf interior, providing a unique technique for accurately estimating LCC. The far-red to red ratio of chlorophyll fluorescence (Fᵣₐₜᵢₒ) has been used to empirically estimate LCC in some previous studies. While these studies support the use of the Fᵣₐₜᵢₒ for LCC estimation, its theoretical underpinning remains less well-defined and its effectiveness across a wider range of scenarios remains unclear. In this study, we established the relationship between the Fᵣₐₜᵢₒ and LCC using the light use efficiency (LUE)-based SIF model and spectral invariant radiative transfer theory. Firstly, the LUE-based SIF model demonstrates that the change in the leaf Fᵣₐₜᵢₒ is controlled by the ratio of the fluorescence escape fraction (i.e., fₑₛc from the photosystem to the leaf surface) at the corresponding bands. Secondly, a fₑₛc modeling approach is presented using the spectral invariant theory and thus the fₑₛc ratio is linked to LCC. Theoretical analysis shows that the Fᵣₐₜᵢₒ has a strong correlation with LCC, which explains over 90 % of the variation in Fᵣₐₜᵢₒ. Both experimental measurements and model simulations from a radiative transfer model Fluspect were used to validate the relationship between LCC and three Fᵣₐₜᵢₒ (i.e., Fratio↑, Fratio↓ and Fratiotot), which were derived from the upward and downward SIF of leaves, as well as the total SIF observed from both sides. The Fluspect simulations were used to assess the sensitivity of the Fᵣₐₜᵢₒ-LCC relationship to the leaf structure. Two types of experimental measurements, including the field measurements of three crops and the laboratory measurements of 20 tundra plants, were employed to examine the species dependence of the Fᵣₐₜᵢₒ-LCC relationship. The performance of Fᵣₐₜᵢₒ for LCC estimation was evaluated and compared with spectral indices and the PROSPECT model using the experimental measurements and leave-one-out cross-validation (LOOCV) approach. Both the Fluspect simulations and the experimental measurements indicate that the Fᵣₐₜᵢₒ is strongly correlated with LCC for a wide range of leaf scenarios. The Fᵣₐₜᵢₒ-LCC relationship remains relatively stable across different leaf structures and plant species, since the relationship is almost consistent. The LOOCV of experimental measurements shows that the Fᵣₐₜᵢₒ provides promising and robust LCC estimates, with the Fratiotot performing the best. The Fratiotot outperforms spectral indices, reducing the RMSE for LCC estimation by 19.5 %-93.9 %. Furthermore, compared to the PROSPECT model, the Fᵣₐₜᵢₒ achieves a reduction in RMSE by 30.4 %-77.8 %. These results demonstrate that the Fᵣₐₜᵢₒ is effective for estimating LCC of diverse plant species. This study advances our understanding of the relationship between the Fᵣₐₜᵢₒ and LCC, supporting the use of SIF signals for remote sensing of LCC.
Why it matches plant phenotyping methodsSIFの遠赤色/赤色比から葉クロロフィル含量を推定する理論・測定手法を開発し、放射伝達シミュレーションと複数植物種の実測で検証しており、植物形質取得が中心である。
abstractSun-induced chlorophyll fluorescence (SIF) is an optical signal emitted from the leaf interior, providing a unique technique for accurately estimating LCC.
In spite of the efforts made for canopy water content (CWC) mapping in the community, including vegetation water proxy from microwave-based vegetation optical depth (VOD) as well as optical-based indices, there is still no operational CWC product from optical satellites up to now. To fill this gap, this study proposes a unified algorithm for CWC mapping at both decametric and coarse spatial resolution from several widely used optical satellites. Based on machine learning trained on radiative transfer model simulations, we comprehensively parameterized the distribution of the canopy and vegetation input variables (i.e., leaf traits and soil background) of the PROSAIL model, by relying on the largest open integrated global plant and soil spectral databases. We investigated the impact of diverse band combinations as well as the inclusion of optical indices for CWC estimation using RTMs. The performances of this algorithm were first evaluated at decametric resolution based on ground measurements distributed over five ground campaigns corresponding to diverse climate and biome types. The retrieved CWC from Sentinel-2 and Landsat-8 exhibits satisfactory performance, with coefficient R of 0.81 and RMSE of 0.046 g/cm². We then evaluated CWC at 500 m resolution from MODIS by comparing it with Landsat-8 and Sentinel-2 aggregated values over a globally distributed selection of LANDVAL sites, representative of the existing biome types combined with a range of precipitation, soil moisture and vegetation density conditions. The MODIS CWC global maps show reasonable seasonal and spatial patterns compared to multi-frequencies microwave-based VOD, and improvements compared to the conventionally and extensively used optical indices such as NDWI. The CWC product developed in this study is expected to provide new insights for global or regional vegetation water variations monitoring from optical satellites, with the strength of high spatial resolution compared to the microwave passive VOD (i.e., 20-500 m vs 22.5 km). These two products could be further combined for more accurate global vegetation water and biomass mapping in the future to improve our understanding of carbon uptake and hydrological applications.
Why it matches plant phenotyping methods光学衛星画像から植物群落の含水量（CWC）を推定する統合アルゴリズムを開発し、複数の衛星・地上測定で性能評価しているため、植物形質推定法が中心である。
abstractthis study proposes a unified algorithm for CWC mapping at both decametric and coarse spatial resolution from several widely used optical satellites.
Plant functional traits are key drivers of ecosystem processes. However, plot-based monitoring of functional composition across both large spatial and temporal extents is a time-consuming and expensive undertaking. Airborne and satellite remote sensing platforms collect data across large spatial expanses, often repeatedly over time, raising the tantalising prospect of detection of biodiversity change over space and time through remotely sensed methods. Here, we test the degree to which in situ measurements of taxonomic and functional β-diversity, defined as pairwise dissimilarity either between sites, or between years within individual sites, is detectable in airborne hyperspectral imagery across both space and time in an alpine vascular plant community in the Front Range, Colorado, USA. Functional and taxonomic dissimilarity were significantly related to spectral dissimilarity across space, but lacked robust relationships with spectral dissimilarity over time. Biomass showed stronger relationships with spectral dissimilarity than either taxonomic or functional dissimilarity over space, but exhibited no significant associations with spectral dissimilarity over time. Comparative analyses using NDVI revealed that NDVI alone explains much of the variation explained by the full-range spectra. Our results support the use of hyperspectral data to detect fine-scale changes in vascular plant β-diversity over space, but suggest that methodological limitations still preclude the use of this technology for long-term monitoring and change detection.
Why it matches plant phenotyping methods航空・衛星ハイパースペクトル画像を用いて植物群集の機能的・分類学的β多様性を推定できるかを空間・時間で検証しており、植物状態の取得手法が研究の中心です。
abstractHere, we test the degree to which in situ measurements of taxonomic and functional β-diversity, defined as pairwise dissimilarity either between sites, or between years within individual sites, is detectable in airborne hyperspectral imagery across both space and time
Near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) is important for gross primary production (GPP) estimation. While NIRvP is a useful indicator of canopy structure and solar radiation, its association with heat or moisture stress is not fully understood. Thus, this research aimed to explore the impact of air temperature (Ta) and vapor pressure deficit (VPD) on the NIRvP-GPP relationship. Using Moderate Resolution Imaging Spectroradiometer (MODIS) observations, eddy-covariance measurements, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) data, we found that NIRvP cannot fully explain the response of plant photosynthesis to Ta and VPD at both seasonal and daily scales. Therefore, we incorporated a polynomial function of Ta and an exponential function of VPD to correct its seasonal response to stress and calibrated the GPP residual via a linear function of Ta and VPD time-varying derivatives to account for its daily response to stress. Leave-one-site-out cross-validation suggested that the improvements relative to its original version were especially noteworthy under stress conditions while less significant when there was no water or heat stress across grasslands and croplands. When compared to six other GPP models, the enhanced NIRvP model consistently outperformed them or performed comparably with the best model in terms of bias, RSME, and coefficient of determinant against measurements in grasslands and croplands. Moreover, we found that parameterizing the fraction of photosynthetically active radiation term using NIRv notably improved the performance of the classic MOD17 and vegetation photosynthesis model, with an average RMSE reduction of 13 % across grasslands and croplands. Overall, this study highlights the need to consider environmental stressors for improved NIRvP-based GPP and shed light on future improvements of LUE models.
Why it matches plant phenotyping methodsMODIS観測から植物群落のGPP（光合成）を推定するモデルを開発・補正し、交差検証と他モデル比較で性能評価しており、植物生理状態の取得手法が研究の中心である。
abstractwe incorporated a polynomial function of Ta and an exponential function of VPD to correct its seasonal response to stress and calibrated the GPP residual via a linear function of Ta and VPD time-varying derivatives to account for its daily response to stress.
Protected areas (PAs) in Amazon forests are vital in preserving tropical forest ecosystems and mitigating forest degradation. However, the increasing frequency and severity of fires in these regions necessitate a comprehensive understanding of post-fire vegetation recovery trajectories, which is essential to evaluate the effectiveness and resilience of PAs in the face of ongoing climate change. Recovery trajectories under natural conditions remain uncertain, as unregulated human settlements often interfere with or influence the recovery process, skewing the actual recovery rates detected by satellite remote sensing. To tackle this issue, we examined 2990 MODIS-derived fire events in eastern Amazon PAs from 2001 to 2020. We assessed the effectiveness of multi-source Earth observation data and the eXtreme Gradient Boost machine learning model to distinguish burned areas undergoing natural recovery (natural recovery areas) from areas that are permanently converted to other uses (permanently converted areas). We then analyzed greenness recovery rates and canopy structure recovery trajectories across all burned areas, natural recovery areas, and permanently converted areas. Greenness recovery rates were derived from Landsat data, while canopy structure recovery was assessed using GEDI lidar-derived metrics and the space-for-time substitution approach. Our model achieved an overall classification accuracy of 86.53 %, accurately differentiating natural recovery areas (n = 1942) from permanently converted areas (n = 1048). The differing patterns of post-fire greenness recovery rates and structure recovery trajectories highlight the importance of this distinction. In natural recovery areas, significant recovery of structural traits such as relative heights (RHs), canopy cover (CC), and plant area index (PAIs), was observed, returning to their pre-disturbance levels over a 20-year period. Notably, metrics related to understory recovery and plant vertical space use, such as PAI values across the entire vertical strata, exhibited stronger recovery rates than height-related metrics like RHs, highlighting their utility in characterizing complex ecosystem recovery processes. These findings demonstrate the potential and necessity of using multi-source Earth observation data to distinguish different post-fire vegetation recovery processes. This distinction improves our understanding of ecological recovery rates and the successional dynamics of post-fire forests under natural conditions, offering new opportunities to explore their biogeographical distribution, recovery rate variabilities, and impacts on carbon sequestration and ecosystem resilience.
Why it matches plant phenotyping methods衛星・LiDARデータと機械学習を統合し、森林キャノピーの構造形質や緑度の回復軌跡を大規模に推定・評価しており、植物状態の取得・抽出が研究の中心です。
abstractWe assessed the effectiveness of multi-source Earth observation data and the eXtreme Gradient Boost machine learning model to distinguish burned areas undergoing natural recovery
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsUAV LiDARと多視点画像から個体樹冠を分割する新規手法の開発が主題で、樹冠という植物形態の抽出に直接関わるため。
titleA novel self-similarity cluster grouping approach for individual tree crown segmentation using multi-features from UAV-based LiDAR and multi-angle photogrammetry data
Sun-induced chlorophyll fluorescence (SIF) has recently emerged as a proxy for canopy photosynthesis of vegetation and offers a promising approach for scalable remote crop monitoring. Effective application of SIF for crop monitoring requires better understanding of the processes that cause SIF-photosynthesis decoupling at leaf and canopy scales. To answer this challenge, we developed a novel automated multi-targeting hyperspectral spectrometer (OctoFlox). First, we evaluated the performance of OctoFlox and found high stability and cross-channel comparability. Second, we performed an evaluation of different SIF retrieval methods to identify the best suited retrieval method for our system configuration for both red (SIFʀᴇᴅ) and far-red SIF (SIFꜰʀ). We then deployed OctoFlox within Soil-Plant Atmosphere Research (SPAR) controlled-environment chambers that enable measurement of canopy-scale SIF and photosynthesis with matching footprints. We analyzed the effect of the SPAR chamber tops on the light environment and found minimal impact on the spectral response. Lastly, we examined the response of SIF and canopy photosynthesis using the SPAR chambers. Soybean plants were evaluated at pre-drought, drought (irrigated at 100 % field capacity vs. 33 % field capacity for 2 weeks) and after 1 week recovery from drought. During early growing season, SIFꜰʀ and SIFʀᴇᴅ exhibited similar responses. At peak growing season (R2 growth stage), SIFꜰʀ increased during afternoon depression of photosynthesis, but SIFʀᴇᴅ decreased. We demonstrate that pairing SIF instrumentation with SPAR chambers can accelerate understanding SIF-photosynthesis relationships from diurnal to seasonal scales in relation to crop physiological responses to abiotic stress. We provide user recommendations for future applications using OctoFlox and SPAR chambers for co-measuring SIF and GPP.
Why it matches plant phenotyping methodsOctoFlox分光計の開発・性能評価、SIF検索法の比較、SPARチャンバーとの統合および作物ストレス時のSIF・光合成計測を中心とする植物フェノタイピング手法研究である。
abstractwe developed a novel automated multi-targeting hyperspectral spectrometer (OctoFlox).
Forest canopy structural complexity (CSC) describes the three-dimensional (3D) arrangement of canopy elements, and has become an emergent forest attribute mediating forest ecosystem functioning along with species diversity. Light detection and ranging (lidar), especially the emerging near-surface lidar platforms (e.g., terrestrial laser scanning/TLS, backpack laser scanning/BLS, unmanned aerial vehicle laser scanning/ULS), can depict 3D canopy information with high efficiency and accuracy, providing an ideal data source for forest CSC quantification. However, current existing lidar-based CSC quantification indices may share common limitations of getting saturated in structurally complex forest stands and not fully capturing within-canopy structural variations. In this study, we introduced the concept of entropy into forest CSC quantification, and proposed a new forest CSC index, namely canopy entropy (CE). Two major bottlenecks were addressed in the CE calculation procedure, including (1) using a Mann-Kendall (MK) test-based resampling strategy to address the issue of incongruent sampling chances of canopy elements at different locations from different lidar systems, and (2) using a kernel density estimation (KDE)-based method to reduce its dependence on point density. The effectiveness and generality of CE were evaluated by simulating TLS and ULS point clouds from nine forest stands and collecting TLS, BLS, and ULS point clouds from 110 field plots distributed in five forest sites, covering a large variety of forest types and forest CSC conditions. The results showed that CE was an effective forest CSC quantification index that successfully captured CSC variations caused by both tree density and the number of vertical canopy layers. It had significant positive correlations with four widely used CSC indices (i.e., canopy cover, foliage height diversity, canopy top rugosity, and fractal dimension; R²: 0.32 to 0.67), but outperformed them by overcoming their common limitations. CE estimates from multiplatform lidar point clouds agreed well with each other (R² ≥ 0.70, RMSE ≤0.10), indicating it has generality in cross-platform forest CSC quantification practices. We believe the proposed CE index has great potential to help us unravel the correlations among forest CSC, species diversity, and forest ecosystem functions, and therefore improve our understanding on forest ecosystem processes.
Why it matches plant phenotyping methods森林キャノピー構造複雑性という植物群落形質を、マルチプラットフォームLiDARから定量化する新規指標を開発し、複数データで有効性・一般性を検証しているため、方法開発・検証が中心である。
abstractwe introduced the concept of entropy into forest CSC quantification, and proposed a new forest CSC index, namely canopy entropy (CE).
Information on crop phenology is essential when aiming to better understand the impacts of climate and climate change, management practices, and environmental conditions on agricultural production. Today's novel optical and radar satellite data with increasing spatial and temporal resolution provide great opportunities to derive such information. However, so far, we largely lack methods that leverage this data to provide detailed information on crop phenology at the field level. We here propose a method based on dense time series from Sentinel-1, Sentinel-2, and Landsat 8 to detect the start of seven phenological stages of winter wheat from seeding to harvest. We built different feature sets from these input data and compared their performance for training a one-dimensional temporal U-Net. The model was evaluated using a comprehensive reference data set from a national phenology network covering 16,000 field observations from 2017 to 2020 for winter wheat in Germany and compared against a baseline set by a Random Forest model. Our results show that optical and radar data are differently well suited for the detection of the different stages due to their unique characteristics in signal processing. The combination of both data types showed the best results with 50.1% to 65.5% of phenological stages being predicted with an absolute error of less than six days. Especially late stages can be predicted well with, e.g., a coefficient of determination (R²) between 0.51 and 0.62 for harvest, while earlier stages like stem elongation remain a challenge (R² between 0.06 and 0.28). Moreover, our results indicate that meteorological data have comparatively low explanatory potential for fine-scale phenological developments of winter wheat. Overall, our results demonstrate the potential of dense satellite image time series from Sentinel and Landsat sensor constellations in combination with the versatility of deep learning models for determining phenological timing.
Why it matches plant phenotyping methods衛星光学・SAR時系列と深層学習を用いて冬コムギの7つの生育期を圃場レベルで推定する手法を開発し、大規模参照データで評価・比較しており、植物表現型取得が中心である。
abstractWe here propose a method based on dense time series from Sentinel-1, Sentinel-2, and Landsat 8 to detect the start of seven phenological stages of winter wheat from seeding to harvest.
Understanding the impacts of climate change on plant phenology is crucial for predicting ecosystem responses. However, accurately tracking the flowering phenology of individual plant species in grassland species mixtures is challenging, hindering our ability to study the impacts of biotic and abiotic factors on plant reproduction and plant-pollinator interactions. Here, we present a workflow for extracting flowering phenology from grassland species mixtures using near-surface time-lapse cameras. We used 89 image series acquired in plots with known species composition at the Jena trait-based experiment (Germany) to develop random forest classifiers, which were used to classify images and compute time series of flower cover for each species. The high temporal resolution of time-lapse cameras allowed to select images in proper light conditions, and to extract vegetation indices and texture metrics to improve discrimination among flowering species. The random forest classifiers showed a high accuracy in predicting the cover of Leucanthemum vulgare, Ranunculus acris, and Knautia arvensis flowers, whereas graminoid flowers were harder to predict due to their green-to-brownish colours. The proposed workflow can be applied in climate change studies, ecosystem functioning, plant community ecology, and biodiversity change research, including the investigation of effects of species richness on individual species' flowering phenology. Our method could be a valuable tool for understanding the impacts of climate change on plant reproduction and ecosystem dynamics.
Why it matches plant phenotyping methods近接タイムラプス画像とランダムフォレストを用いて、種別の花被覆率および開花フェノロジーを抽出するワークフローを開発しており、植物形質取得法が研究の中心である。
abstractHere, we present a workflow for extracting flowering phenology from grassland species mixtures using near-surface time-lapse cameras.
The bacterium Xylella fastidiosa (Xf) is a plant pathogen that can block the flow of water and nutrients through the xylem. Xf symptoms may be confounded with generic water stress responses. Here, we assessed changes in biochemical, biophysical and photosynthetic traits, inferred using biophysical models, in Xf-affected almond orchards under rainfed and irrigated conditions on the Island of Majorca (Balearic Islands, Spain). Recent research has demonstrated the early detection of Xf-infections by monitoring spectral changes associated with pigments, canopy structural traits, fluorescence emission and transpiration. Nevertheless, there is still a need to make further progress in monitoring physiological processes (e.g., photosynthesis rate) to be able to efficiently detect when Xf-infection causes subtle spectral changes in photosynthesis. This paper explores the ability of parsimonious machine learning (ML) algorithms to detect Xf-infected trees operationally, when considering a proxy of photosynthetic capacity, namely the maximum carboxylation rate (Vcₘₐₓ), along with carbon-based constituents (CBC, including lignin), and leaf biochemical traits and tree-crown temperature (Tc) as an indicator of transpiration rates. The ML framework proposed here reduced the uncertainties associated with the extraction of reflectance spectra and temperature from individual tree crowns using high-resolution hyperspectral and thermal images. We showed that the relative importance of Vcₘₐₓ and leaf biochemical constituents (e.g., CBC) in the ML model for the detection of Xf at early stages of development were intrinsically associated with the water and nutritional conditions of almond trees. Overall, the functional traits that were most consistently altered by Xf-infection were Vcₘₐₓ, pigments, CBC, and Tc, and, particularly in rainfed-trees, anthocyanins, and Tc. The parsimonious ML model for Xf detection yielded accuracies exceeding 90% (kappa = 0.80). This study brings progress in the development of an operational ML framework for the detection of Xf outbreaks based on plant traits related to photosynthetic capacity, plant biochemistry and structural decay parameters.
Why it matches plant phenotyping methods植物の生理・生化学形質を航空ハイパースペクトル／熱画像から推定し、Xylella感染を検出する機械学習フレームワークの開発が中心であるため。
abstractThe ML framework proposed here reduced the uncertainties associated with the extraction of reflectance spectra and temperature from individual tree crowns using high-resolution hyperspectral and thermal images.
Timely knowledge of phenological development and crop growth is pivotal for evidence-based decision making in agriculture. We propose a near real-time approach combining radiative transfer model inversion with physiological and phenological priors from multi-year field phenotyping. Our approach allows to retrieve Green Leaf Area Index (GLAI), Canopy Chlorophyll Content (CCC) and hence Leaf Chlorophyll Content (Cab) from Sentinel-2 optical satellite imagery to quantify winter wheat growth conditions in a physiologically sound way. Phenological macro stages are based on accumulated growing degree day thresholds obtained from multi-year field phenotyping covering more than 2400 ratings from roughly 300 winter wheat varieties and reflect important physiological transitions. These include the transition from vegetative to reproductive growth and the onset of flowering, which is important information for agricultural decision support. Validation against a large data set of on-farm trials in Switzerland collected in 2019 and 2022 revealed high accuracy of our approach that produced spatio-temporally consistent results. Phenological macro stages were predicted for 970 Sentinel-2 observations reaching a weighted F1-score of 0.96. Sentinel-2 derived GLAI and CCC explained between 77 to 84% and between 79 to 84% of the variability in in-situ measurements, respectively. Here, the incorporation of phenological priors clearly increased trait retrieval accuracy. Besides, this work highlights that physiological priors, e.g., obtained by field phenotyping, can help enhancing landscape scale observations and hold potential to advance the retrieval of remotely sensed vegetation traits and in-season phenology.
Why it matches plant phenotyping methods衛星画像と放射伝達モデル、圃場フェノタイピング由来の事前情報を統合し、冬コムギの生育形質・葉面積指数・クロロフィル量・フェノロジーを推定する手法を開発・検証しており、フェノタイピング手法が中心である。
abstractWe propose a near real-time approach combining radiative transfer model inversion with physiological and phenological priors from multi-year field phenotyping.
Leaf chlorophyll content (LCC) is an important indicator of foliar nitrogen status and photosynthetic capacity. Compared to physical models, the generality of empirical models based on vegetation indices is often questioned when they are used to estimate LCC due to the influence from canopy structure, such as leaf area index (LAI). A recent study developed the LAI-insensitive chlorophyll index (LICI) and established a semi-empirical LICI-based LCC quantification model, which inherits both the robustness of physical models and the simplicity of empirical models. However, it is unclear whether such a simple model is as accurate and generic as physical models. Here, we adopted an innovative approach to disentangle the confounding effects of LAI and LCC on LICI and found that LICI was strongly correlated to LCC but only marginally sensitive to LAI. Moreover, we also found that LICI was sensitive to the soil background and thus proposed a spectral separation of soil and vegetation (3SV) algorithm, which is automatic and does not require prior information of soil background. After implementing the 3SV algorithm to remove the contributed reflectance of soil, we then obtained the contributed reflectance of vegetation (CRv). Model simulations showed that the soil background effect on the CRv-derived LICI was largely eliminated and hence this index was viewed to be soil-removed. As a result, the accuracy and generality of the soil-removed LICI-based model for LCC estimation was evaluated using comprehensive datasets from multiple vegetation types, years, sites, and observation platforms and compared to that of a MatrixVI-based physical model and a MERIS terrestrial chlorophyll index (MTCI)-based semi-empirical model. The root-mean-square error (RMSE) for LCC estimated by the soil-removed LICI-based model was 6.22–6.87 μg/cm² for the crop datasets and 10.68 μg/cm² for the multi-ecosystem dataset when the equivalent wet soil fraction was <0.7. Although further efforts are required to mitigate the effects of soil on the LICI-based model over sparse vegetation, this research is highly beneficial for extending its potential applications to the globe and advancing the development of an operational LCC monitoring system in the emerging satellite hyperspectral era.
Why it matches plant phenotyping methods葉クロロフィル含量という植物形質を推定するスペクトル分離アルゴリズムと指標ベースモデルを開発し、複数データセット・観測プラットフォームで精度と汎用性を評価しており、フェノタイピング手法が中心である。
abstractproposed a spectral separation of soil and vegetation (3SV) algorithm, which is automatic and does not require prior information of soil background.
Accurate monitoring of crop nitrogen (N) across spatial and temporal scales is a fundamental goal for meeting precision agriculture requirements and promoting sustainable agriculture. The planning and implementation of several spaceborne imaging spectroscopy missions in recent years holds great promise for such large scale and intricate monitoring. Several N retrieval models have been developed for specific crop species, but a generalized model across diverse species is lacking. By leveraging imaging spectroscopy data collected by the Global Airborne Observatory (GAO), and leaf samples collected from commercial and research farms, we used partial least squares regression to calibrate and validate the retrieval of mass-based crop canopy N concentrations across diverse species and several major agricultural regions in the contiguous United States. The performance statistics indicated high precision and accuracy of the model results, suggesting that the development of a generalized N retrieval model is possible (R²: 0.78; RMSE: 0.49% N). Maps derived from GAO data provided quantitative crop N information at fine spatial resolution (i.e., 0.6 m), capturing both inter- and intra-species variations across agricultural locations. The algorithm was also successfully tested on simulated moderate resolution (i.e., 30 m) imagery, corresponding to data to be collected by forthcoming spaceborne imaging spectroscopy missions. Imaging spectroscopy offers an effective approach to quantify crop N concentration that could be incorporated to promote sustainable agriculture and improve global food security.
Why it matches plant phenotyping methods航空イメージング分光とPLS回帰により、作物キャノピー窒素濃度を定量する汎用的な検索モデルを開発・検証しており、植物形質の取得手法が中心である。
abstractwe used partial least squares regression to calibrate and validate the retrieval of mass-based crop canopy N concentrations across diverse species and several major agricultural regions in the contiguous United States.
Spatial predictions of biomass production and biodiversity at regional scale in grasslands are critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can predict these grassland characteristics with varying accuracy. However, such studies frequently fail to cover a sufficiently broad range of environmental conditions, and their prediction models are often case-specific. To address this gap, we have modelled above-ground biomass and species richness in 150 spatially independent grassland plots of three geographical regions in Germany. These regions follow a North-South climate gradient and differ in soil types, topography, elevation, climatic conditions, historical contexts, and management intensities. The predictors tested in this study are Sentinel-1 backscatter, Sentinel-2 time series of surface reflectance along with derived vegetation indices and Rao's Q, and a set of topoedaphic variables. We compared the performance of a feed-forward deep neural network (DNN) with a random forest (RF) regression algorithm. The DNN achieved the best estimations of biomass (r² = 0.45) when trained with Sentinel-2 surface reflectance only. Moreover, the DNN showed a higher generalizability than RF during spatial cross-validations (i.e., calibrating and validating in different regions, r² = 0.38 vs. 0.26). Species richness predictions by both algorithms improved when the full time series of Sentinel-2 surface reflectance values were used (highest r² = 0.42 achieved by the DNN), but both performed poorly during spatial cross-validations. Overall, the DNN-based models were more robust than RF models, showed a lower bias and lower systematic error, and required fewer inputs. Explainability analysis indicated that red-edge and near infrared information from May and October was the most relevant to predict species richness. This study presents an important step forward in generating robust spatially explicit predictions of grassland attributes and biodiversity variables across large areas, environmental gradients, and phenological stages.
Why it matches plant phenotyping methodsリモートセンシングと深層学習により草地の地上部バイオマスおよび植物種数を推定し、地域間空間検証でモデル性能を比較しているため、植物形質推定手法が中心である。
abstractwe have modelled above-ground biomass and species richness in 150 spatially independent grassland plots of three geographical regions in Germany.
Monitoring plant phenology is vital to maintaining the global carbon balance and management under climate change. Bamboo forest is an essential forest type in subtropical China with a strong carbon sequestration capacity. In recent years, vegetation indices (VIs), which characterize canopy structural parameters, and solar-induced chlorophyll fluorescence (SIF), indicating the photosynthetic activity of vegetation, have provided new perspectives on plant phenology at regional and global scales. However, the best data sources and methods for extracting the phenology of bamboo forests remain to be explored. In this study, new vegetation indices were innovatively constructed by normalizing the VIs (enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2), and near-infrared reflectance of vegetation (NIRv)) based on Moderate Resolution Imaging Spectroradiometer (MODIS) products and SIF products (GOSIF) based on OCO-2 satellites and then taking the mean values of the normalized VIs (EVI, EVI2 and NIRv) and SIF. We called the new indices SVs (SIF and VIs combined indices, including Se (SIF and EVI combined index), Se2 (SIF and EVI2 combined index), or Sn (SIF and NIRv combined index)). Two time series reconstruction methods (asymmetric Gaussian (AG) function fitting and double logistic (DL) function) and two extractive phenology parameter methods (dynamic threshold method (DT) and comparative threshold method (CT)) were employed to extract phenological information. The advantages of SVs for extracting bamboo forest phenology (BFP) were verified by comparing the extraction performance of VIs, SIF, and SVs on the SOS and EOS of bamboo forests. Thus, the best way to extract BFP was explored, and the spatial distribution and spatial-temporal variation characteristics of BFP in China from 2011 to 2020 were analyzed. The results are described as follows: (1) SVs are better able to extract BFP parameters compared with VIs and SIF, especially in bamboo forest-specific off-years and on-years; (2) SIF has better accuracy than VIs in extracting BFP, where both SOS and EOS values obtained from VIs are overestimated, and SIF can reflect BFP information earlier; and (3) the best data sources for extracting SOS and EOS in bamboo forests are Sn and Se, respectively, and the optimal methods are AG_CT and DL_DT, respectively. Compared with SIF, the R² values of Sn and Se extracted SOS and EOS are improved by 40.7% and 7.7%, and the RMSE values are reduced by 24.7% and 0.7%, respectively; and (4) the SOS for bamboo forests in China from 2011 to 2020 was mainly concentrated in 80–100 days, with an overall advancing trend; the EOS was mainly concentrated in 300–320 days, with an overall delay. The results show that the SVs obtained by coupling VIs and SIF can better track the BFP information, providing a practical reference for macroscopic monitoring of BFP based on medium-resolution time series data.
Why it matches plant phenotyping methodsSIFと植生指数を統合した指標、時系列再構成、閾値法による竹林フェノロジー抽出を開発・比較検証しており、植物状態の測定手法が研究の中心である。
abstractThe advantages of SVs for extracting bamboo forest phenology (BFP) were verified by comparing the extraction performance of VIs, SIF, and SVs
Cover cropping between cash crop growing seasons is a multifunctional conservation practice. Timely and accurate monitoring of cover crop traits, notably aboveground biomass and nutrient content, is beneficial to agricultural stakeholders to improve management and understand outcomes. Currently, there is a scarcity of spatially and temporally resolved information for assessing cover crop growth. Remote sensing has a high potential to fill this need, but conventional empirical regression operated with coarse-resolution multispectral data has large uncertainties. Therefore, this study utilized airborne hyperspectral imaging techniques and developed new process-guided machine learning approaches (PGML) for cover crop monitoring. Specifically, we deployed an airborne visible to shortwave infrared (400–2400 nm) hyperspectral system to acquire high spatial (0.5 m) and spectral (3–5 nm) resolution reflectance over 23 cover crop fields across Central Illinois in March and April of 2021. Airborne hyperspectral surface reflectance with high spectral and spatial resolution can be well matched with field data to quantify cover crop traits. Furthermore, the PGML models were pre-trained by synthetic data from soil-vegetation radiative transfer modeling (1,000,000 records), and then fine-tuned with field data of cover crop biomass and nutrient content. Results show that airborne hyperspectral data with PGML can achieve high accuracy to predict cover crop aboveground biomass (R² = 0.72, relative RMSE = 15.16%) and nitrogen content (R² = 0.69, relative RMSE = 16.59%) through leave-one-field-out cross-validation. Unlike the pure data-driven approach (e.g., partial least-squares regression), PGML incorporated radiative transfer knowledge and obtained higher predictive performance with fewer field data. Meanwhile, with field data for model fine-tuning, PGML predicted biomass more accurately than the numerical inversion of radiative transfer models. We also found that the red edge has a high contribution in quantifying aboveground biomass and nitrogen content, followed by green and shortwave spectra. This study demonstrated the first attempt of utilizing hyperspectral remote sensing to accurately quantify cover crop traits. We highlight the strength of PGML in exploiting sensing data to advance agroecosystem monitoring for sustainable agricultural management.
Why it matches plant phenotyping methods航空ハイパースペクトル画像と放射伝達モデル誘導型機械学習を開発・検証し、被覆作物のバイオマスおよび窒素含量という植物形質を定量しているため、フェノタイピング手法が中心である。
abstractthis study utilized airborne hyperspectral imaging techniques and developed new process-guided machine learning approaches (PGML) for cover crop monitoring.
Accurately monitoring forest insects and diseases disturbances is important for managing forests and implementing effective forest quality improvement measures. However, monitoring disturbances in the lower and middle parts of the forest canopy with traditional remote sensing technologies is difficult. A new sensor called hyperspectral LiDAR (HSL) makes the monitoring of these forest disturbances possible, however, its applicability to canopy scale has not been fully studied due to the current hardware limitation. This paper assessed the potential of airborne hyperspectral LiDAR (AHSL) for monitoring forest insects and diseases stress using 3D radiative transfer modeling and in-situ measurements. A virtual 3D forest scene with explicitly described structures was first reconstructed from terrestrial laser scanning and field measurement data, upon which a number of different insects and diseases disturbance scenarios with different damage locations and stress levels were defined. AHSL point cloud and the corresponding hyperspectral image (HI) were then simulated with large-scale and remote sensing image simulation (LESS) model for each combination of the different damage locations and stress levels. LiDAR point cloud from different layers of the simulated AHSL point cloud were then extracted and rasterized into images with 3-m spatial resolution, which, along with the hyperspectral images, were used to test the insect and disease monitoring ability by using a random forest model. Results show that AHSL has significant higher overall accuracies (OA: 65.95% ∼ 89.45%) than HI (OA: 33.99% ∼ 57.02%) for predicting insects and diseases stress levels. Compared to AHSL, HI is affected by a variety of factors such as soil and shadows, resulting in poorer monitoring capability for different damage locations, with the highest classification accuracy in the case of entire canopy damage (OA: 57.02%). For AHSL, it has good classification accuracy for all damage locations, with the lowest accuracy in the case of lower canopy damage (OA: 65.95%). This study demonstrates that AHSL is a promising and reliable tool to monitor forest disturbances, especially for structural and spectral changes in the lower and middle parts of canopy, which may have great potential for early detecting forest insects and diseases.
Why it matches plant phenotyping methods航空ハイパースペクトルLiDARを用いて森林キャノピーの昆虫・病害ストレスを推定する手法をシミュレーションと実測で評価しており、植物の病害状態を測定するセンシング手法が研究の中心である。
abstractThis paper assessed the potential of airborne hyperspectral LiDAR (AHSL) for monitoring forest insects and diseases stress using 3D radiative transfer modeling and in-situ measurements.
Monitoring in-vivo stomatal conductance (gₛ) dynamics is essential for predicting crop water usage and yield sensitivity in response to climate change. Leaf and canopy spectroscopy offer a non-destructive method for gₛ monitoring; however, the underlying mechanisms connecting leaf spectra with stomatal anatomical and behavioral traits, and their subsequent impacts on gₛ, remain underexplored. In this study, we conducted a wheat field trial, collecting comprehensive measurements of stomatal anatomical (i.e., size, density) and behavioral (i.e., opening ratio, pore area) traits by a customized, high-resolution microscope, leaf spectra via a handheld spectroradiometer, and gₛvia a handheld AP4 Leaf Porometer across various genotypes, nitrogen treatments, growth stages, and diurnal environments. We observed substantial gₛ variability, with stomatal anatomical and behavioral traits jointly accounting for 79% of this variability. We further examined the relationship between leaf spectra and stomatal traits/conductance using a partial least square regression (PLSR) model and discovered that a single PLSR spectral model accurately predicted the variability of each of these traits and gₛ across our datasets. Furthermore, we demonstrated a strong correspondence between spectral variations resulting from gₛ and spectral alternation induced by stomatal anatomical and behavioral traits. By analyzing the diurnal association between spectral and gₛ variability, we revealed important biophysical mechanisms underlying relationships among spectra, stomatal anatomical and behavioral traits, and gₛ. Collectively, our findings highlight the potential of leaf spectroscopy in advancing crop physiology monitoring, contributing to enhanced food security and sustainability.
Why it matches plant phenotyping methods小麦の気孔形質と気孔コンダクタンスを分光計測・PLSRで非破壊推定する手法を中心に検証しており、植物フェノタイピング手法の開発・検証に該当する。
abstractLeaf and canopy spectroscopy offer a non-destructive method for gₛ monitoring
There is a pressing need for well-informed management to reduce wildfire hazard and restore fire's beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California. Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R² 0.69–0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R² 0.49–0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring. These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs.
Why it matches plant phenotyping methodsUAS-SfMと機械学習を用いて、森林キャノピーのバイオマス、樹冠高、LAI、樹冠被覆率などの植物構造形質を推定・検証する手法が研究の中心であるため。
abstractRemote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application
Photosynthesis is the material basis for crop growth and yield formation. Rapid and real-time monitoring of photosynthetic parameters is essential for crop stress monitoring, light use efficiency assessment, and yield prediction. Methods based on passive optical remote sensing can accurately monitor the photosynthetic traits of crops. However, they are limited in capturing one- or two-dimensional information, and cannot obtain three-dimensional (3D) photosynthetic traits in crops. To address this issue, this study quantifies the 3D photosynthetic trait of wheat based on a digital surface model which fused a light detection and ranging (LiDAR) point cloud with multispectral imagery, both of which were acquired using an unmanned aerial vehicle (UAV). First, we constructed a model to estimate the key photosynthetic parameters (canopy chlorophyll content, fraction of absorbed photosynthetically active radiation, and canopy net photosynthetic rate) based on selected multi-spectral indices in wheat. Second, we applied the model to a point cloud that had been fused with spectral reflectance imagery to generate the 3D spatial and vertical distributions of the above three photosynthetic parameters, and then estimated the 3D photosynthetic traits. Third, we analyzed the 3D photosynthetic traits of wheat in terms of cultivars, growth stages, and cultivation management, and assessed the ability of the derived metrics based on the 3D photosynthetic traits to predict yield. The results showed that the LiDAR point cloud fused with multispectral images could accurately characterize the 3D spatial distribution of the photosynthetic parameters of wheat under different conditions. The photosynthetic traits of the different horizontal layers (upper, middle, and lower) of the canopy were statistically different. In addition, two new developed 3D metrics (CPn_P₇₅ₜₕ and CCC_P₇₅ₜₕ), which were derived from the 3D photosynthetic parameters based on the fusion of point cloud and multispectral data, could predict yield with a higher accuracy and greater robustness than traditional methodology of agronomic traits or remote sensing. This study demonstrate that the fusion of LiDAR point cloud and multispectral imagery is feasible for the accurate quantification of 3D photosynthetic traits and the screening of high-yielding genetic cultivars in crops. In addition, we found that multispectral point clouds generated from UAV multispectral imagery using structure from motion (SfM) combined with multi-view stereo (MVS) algorithms (SfM-MVS) can also be used to estimate the 3D spatial distributions of photosynthetic parameters in crops. Despite the fact that the multispectral point cloud by SfM-MVS is sparse and incomplete, this method has potential application in the study of 3D spatial distribution of crop parameters.
Why it matches plant phenotyping methodsUAV LiDARとマルチスペクトル画像の融合により、コムギの3D光合成形質を推定する取得・解析手法を開発し、精度と収量予測性能を評価しているため、方法が研究の中心である。
abstractthis study quantifies the 3D photosynthetic trait of wheat based on a digital surface model which fused a light detection and ranging (LiDAR) point cloud with multispectral imagery
As an emerging subject of the implication on revolutionizing many fields from botany to life science, plant intelligence (PI) has been actively studied but also trapped in debate. Inspired by those earlier botanists such as Darwin conceiving this concept when observing plants outdoors, we propose to track Darwin's footprints – go again to the wild where plants show higher-fold adapting performance than in labs for arousing a re-cognition of PI. However, this plan must face a basic challenge on in-situ plant phenotyping, especially in structure, which serves as the three-dimensional (3D) phenomenological display of varying PI behaviors. Aiming at this core bottleneck, we suggest to go but with 3D remote/proximal sensing (R/PS) devices such as Light Detection and Ranging (LiDAR) – a state-of-the-art technology of fully but fine mapping plants, for starting a 3D cognition of PI. Further, to decode the mechanism of PI occurring, we preview the next-generation (e.g., hyperspectral, fluorescence, and polarization) LiDAR with the latent capacity on all-round phenotyping of plants. Their derived 3D biochemical, physiological, and biophysical functional traits can arouse a beyond-3D cognition of PI. Overall, this theoretical prospect, with the available R/PS technology traced for upgrading PI from conceptual debating to mechanistic understanding, can advance the PI field into its 3D and even beyond-3D times and bring the PI and PI-relevant sciences such as sustainability cognition to breathe new life.
Why it matches plant phenotyping methodsLiDARや次世代リモートセンシングによる植物の3D・機能形質計測を中心に論じる方法論的展望であり、植物フェノタイピングが中核です。
abstractwe suggest to go but with 3D remote/proximal sensing (R/PS) devices such as Light Detection and Ranging (LiDAR) – a state-of-the-art technology of fully but fine mapping plants, for starting a 3D cognition of PI.
Reliable prediction of field-level crop yield over large regions is a prerequisite for informed decision-making in precision crop management. One of common Earth observation approaches is to predict crop yield through the estimation of gross primary productivity (GPP) and a fixed crop-specific harvest index (HI), but few studies have considered the spatio-temporal dynamics of HI. Although some studies have used two-leaf light use efficiency (TL-LUE) models to reduce GPP estimation uncertainties by distinguishing sunlit and shaded leaves, it remains unclear about the physical mechanism underlying the incorporation of environmental regulations into TL-LUE. This study proposed a high-resolution GPP and dynamic HI based yield mapper (HIDYM), which incorporated the generation of 10-m resolution GPP product via a modified TL-LUE (mTL-LUE) model and the estimation of dynamic HI from Sentinel-2 imagery. The mTL-LUE was developed to account for the effect of environmental factors on GPP. Dynamic HI was estimated per pixel and per year by combining the phenological difference ratio and tasseled cap transformation of Sentinel-2 imagery at three critical stages of crop growth. The results demonstrated that HIDYM could capture the spatial and interannual variations of field-level rice and winter wheat yields. The improvement of HIDYM over the fixed HI strategy was more pronounced for rice (R²: 0.64–0.72 vs 0.34–0.48 for 2019–2022) than for winter wheat (R²: 0.72 vs 0.66 for 2021–2022 and 0.71 vs 0.57 for 2022–2023). The proposed methodology has great potential for the routine prediction of crop yields over large-scale croplands, especially in smallholder farming systems.
Why it matches plant phenotyping methodsSentinel-2画像とGPP・動的収穫指数を統合し、圃場レベルの作物収量を推定するHIDYM手法の開発と検証が研究の中心であるため、植物表現型の計測・推定手法として収録する。
abstractThis study proposed a high-resolution GPP and dynamic HI based yield mapper (HIDYM)
There is a pressing need for well-informed management to reduce wildfire hazard and restore fire's beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California. Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69–0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49–0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring. These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs.
Why it matches plant phenotyping methodsUAS-SfMと機械学習を用いて、森林キャノピーの高さ、LAI、被覆率、バイオマスなどの植物構造形質を推定・検証する手法が研究の中心である。
abstractRemote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsUAV LiDAR点群とマルチスペクトル画像の融合により小麦の3D光合成形質を導出する手法が題名上の中心であり、植物フェノタイピング手法に該当する。
titleNovel 3D photosynthetic traits derived from the fusion of UAV LiDAR point cloud and multispectral imagery in wheat
Advances in retrieval of solar-induced chlorophyll fluorescence (SIF) provide a promising and independent approach for quantifying gross primary production (GPP) across spatial scales. Recent studies have highlighted the prominent role of qL, the fraction of open Photosystem II (PSII) reaction centers, in mechanistically modeling GPP from remote sensing SIF. However, due to the limited availability of simulated and experimental data, a comprehensive understanding of qL responses to environmental and physiological variations has yet to emerge, and as a consequence, prediction of qL across leaf and canopy scales is still in an early stage. Based on a global sensitivity analysis of a recently developed mechanical model of photosynthesis, we find that the broadband total SIF emitted from PSII (SIFTOT_FULL_PSII) and leaf temperature (TLₑₐf) are the two major predictors of qL. A leaf-level instrument is designed to obtain concurrent measurements of qL, SIFTOT_FULL_PSII, and TLₑₐf over a wide range of environmental conditions. From these measurements, we show that qL can be modelled as a hyperbolic function of SIFTOT_FULL_PSII with only one temperature-related parameter m which increases with temperature, but decreases rapidly as temperatures exceed the optimum temperature. It is suggested that m can be mathematically modelled by a peaked function. The results of the leaf-level experiments on winter wheat demonstrate that the proposed model predicts qL with high accuracy (R² ≥ 0.91, rRMSE ≤ 8.46%) under diverse light and temperature conditions. The essential steps necessary to apply it at canopy scale, including estimating the escape fraction, removing fluorescence emitted from Photosystem I, and reconstructing SIFTOT_FULL_PSII from top-of-canopy (TOC) narrowband SIF, are also presented. Our results confirm that estimated GPP using SIF-informed qL agrees well with measured GPP at a winter wheat site (R² = 0.81, rRMSE = 12.03%). The key benefit of SIF-informed approach is that SIFTOT_FULL_PSII provides critical information on the collective influence of the sub-canopy light environment on qL, avoiding the requirement to explicitly estimate qL at different canopy depths, potentially promoting the ability of SIF to mechanistically quantify photosynthetic CO₂ assimilation at large scales.
Why it matches plant phenotyping methodsSIFと温度の同時計測およびモデル化により、植物の生理状態qLとキャノピー光合成を定量推定する手法を開発・検証しており、表現型取得が研究の中心である。
abstractA leaf-level instrument is designed to obtain concurrent measurements of qL, SIFTOT_FULL_PSII, and TLₑₐf over a wide range of environmental conditions.
Functional diversity is a critical component driving ecosystem functioning. Spatially explicit data of plant functional traits and diversity are essential for understanding biodiversity effects on ecosystem functioning. Here we retrieved three morphological traits (95th quantile height, leaf area index, foliage height diversity) and three physiological traits (chlorophyll a + b content, specific leaf area, equivalent water thickness) from airborne laser scanning and multispectral Sentinel-2 data, respectively. We found airborne LiDAR-derived parameters correlated well with in-situ plot-level morphological data (R² ≥ 0.67). For satellite-derived physiological traits, partial least squares regression (PLSR) obtained higher prediction accuracy (R² = 0.26–0.43, cross-validation with in-situ community-weighted mean (CWM) leaf physiological trait data) than a vegetation index (VI) approach. The remotely-sensed traits were used as input to estimate multi-trait functional diversity (FD) indices in a species-rich subtropical mountainous forest. Finally, we investigated the influence of single-trait CWMs, multi-trait FD indices and environmental variables on remotely-derived aboveground ecosystem carbon stocks (aboveground biomass, AGB) and primary productivity (kernel normalized difference vegetation index, kNDVI). CWMs of all functional traits were significant predictors of AGB and kNDVI, as suggested by the mass-ratio hypothesis. Morphological FD indices were also important predictors of AGB and kNDVI, indicating effects of complementarity in crown architectures. In best-fit multivariate models, the first principal component CWM of morphological traits and that of physiological traits were the most important predictors of AGB and kNDVI, respectively. The FD index of morphological richness was additionally selected in the best-fit models for AGB and kNDVI at ecosystem and landscape scales. Our work highlights the potential of using remotely-sensed functional traits to assess the relationship between trait diversity and ecosystem functioning across large, contiguous areas.
Why it matches plant phenotyping methods航空LiDARとSentinel-2から植物の形態・生理形質を推定し、実測値との検証およびPLSRによる予測精度評価を行っており、リモートセンシングによる植物形質取得が中心的である。
abstractHere we retrieved three morphological traits (95th quantile height, leaf area index, foliage height diversity) and three physiological traits (chlorophyll a + b content, specific leaf area, equivalent water thickness) from airborne laser scanning and multispectral Sentinel-2 data, respectively.
Foliar functional traits are essential for understanding plant adaptation strategies and ecosystem function. Due to limited in-situ observational data, there is a growing interest in upscaling these traits from field sites to regional and global levels. However, limitations persist: (1) global/national scale upscaling that relies on plant functional type (PFT) maps, environmental variables or coarse resolution multispectral images, which fail to capture local-scale trait variability; (2) airborne imaging spectroscopy that enables high-resolution and accurate mapping but is restricted to site scale and is costly; and (3) multispectral satellites like Sentinel-2 that offer global coverage but have limited spectral bands and resolution. While previous research has demonstrated the connection between traits and vegetation phenology, our study seeks to build upon this foundation by further exploring the integration of phenological information for large-scale trait prediction. We examined the integration of Sentinel-2 data with its time series (for phenology information) to map 12 foliar functional traits across 14 National Ecological Observatory Network (NEON) sites in the eastern United States. Our results show that time-series Sentinel-2 models effectively capture the variance in these 12 traits (R² = 0.60–0.80) when compared with benchmark trait data generated by state-of-the-art airborne imaging spectroscopy. The models adequately capture considerable trait variations observed within sites and PFTs. Our approach outperforms existing methods that rely on environmental variables, or a single Sentinel-2 image as predictors across examined NEON sites in eastern United States. Interestingly, including environmental variables in our models does not significantly improve predictive power. Further analysis reveals that a ‘fast-slow’ principal axis predominantly explains the covariation in Enhanced Vegetation Index amplitude (a proxy for leaf longevity), leaf mass per area, and leaf nitrogen content across PFTs. This finding highlights the importance of incorporating phenological information for trait mapping and suggests a potential mechanism underlying these spectra-based models. Our proposed method, which simultaneously achieves high accuracy, large-scale scalability, and high spatial resolution, represents a promising avenue for future global trait mapping. Validation on a larger scale to fully realize its potential in addressing fundamental ecological questions will be a key future focus.
Why it matches plant phenotyping methodsSentinel-2時系列データとフェノロジー情報を統合し、12種類の葉機能形質を大規模・高解像度に推定する手法を構築し、航空機画像分光データを基準に検証しているため、形質取得・推定法が中心的です。
abstractour study seeks to build upon this foundation by further exploring the integration of phenological information for large-scale trait prediction.
Monitoring plants' responses to water deficit using remote sensing still faces large uncertainty, mostly due to the inaccurate characterization of plants' physiological responses. Solar induced chlorophyll fluorescence (SIF) contains information on plants' physiological processes which regulates the energy partitioning after solar radiation is absorbed by chlorophyll, providing new opportunities to monitor plant response to drought stress. However, the drought-induced physiological, biochemical, and structural changes are strongly coupled, hindering the mechanistic understanding of drought impacts on plants. Here, using tower-based observations of SIF together with high spectral resolution reflectance measurements, we derived the time series of the fraction of absorbed photosynthetically active radiation by canopy, chlorophyll content, and fluorescence efficiency using two radiative transfer model-based decomposition methods, and evaluated their responses to two consecutive dry spells at a tall-grass prairie site in the USA (34°59′05.0″ N, 97°31′20.6″ W). We observed a robust signal of afternoon depression based on the fluorescence efficiency estimates during the second dry spell, which had much lower soil moisture than the first one. The strong decline in fluorescence efficiency in the afternoon was likely caused by the high temperature and atmospheric dryness when the soil was dry. Such a direct physiological response contributed to 14.4% to 36.0% of seasonal variation of afternoon SIF, depending on the decomposition method used. Sustained water stress also caused lagged responses. Despite the subsequent rainfall after the dry spell, we observed a continued decline of SIF due to the lagged decline of chlorophyll content and green canopy coverage. Our study demonstrates the use of continuous SIF measurements to understand the development of drought effects on plants, and highlights the importance of afternoon SIF measurements for physiological stress detection.
Why it matches plant phenotyping methods連続SIF・高分解能反射測定と放射伝達モデル分解により、植物の生理状態や乾燥ストレス関連形質を抽出する手法の実質的応用であり、単なる生物学的測定ではない。
abstractusing tower-based observations of SIF together with high spectral resolution reflectance measurements, we derived the time series of the fraction of absorbed photosynthetically active radiation by canopy, chlorophyll content, and fluorescence efficiency using two radiative transfer model-based decomposition methods
Shrub encroachment, characterized by the proliferation of shrubs into grasslands, is a challenge faced by grasslands worldwide that significantly impacts livestock production and ecosystem functions. Rapid and accurate estimation of shrub dominance is important for understanding changes in plant community structures and formulating grassland management policies. However, the limited spatial resolution of commonly used satellite imagery poses a challenge when estimating shrub dominance at the landscape scale. The rapid development of Unoccupied Aerial Vehicles (UAVs) has opened up new opportunities for cross-scale observations of shrub encroachment in grasslands by effectively bridging the scale gap between ground sampling and satellite image pixels while reducing the required groundwork. This study utilized ground reference data, UAV data (RGB, hyperspectral, and LiDAR), and satellite data (Sentinel-1 and Sentinel-2) to estimate shrub and total above-ground biomass (AGB) in temperate grasslands to map the shrub dominance. First, UAV data were applied at the plot scale for the classification of shrub and herbaceous vegetation using the maximum entropy model (MaxEnt), estimation of shrub AGB by employing the vegetation index weighted canopy volume model (CVMVI), and estimation of herbaceous AGB based on the partial least squares regression (PLSR). Second, UAV AGB mapping results were upscaled as samples at the landscape scale and integrated with satellite imagery to establish the shrub and total AGB models using the extreme gradient boosting (XGBoost). Finally, shrub dominance, represented as shrub AGB/total AGB, was mapped across the study area. We found that at the plot scale, the MaxEnt model achieved an overall accuracy of 0.990 for object-based classification. The CVMVI combined with canopy height model and narrow-band vegetation index achieved the highest accuracy for estimating shrub AGB (R² = 0.821, RMSE = 30.1 g). The PLSR combined with features derived from all UAV data achieved the highest accuracy for estimating herbaceous AGB (R² = 0.856, RMSE = 9.1 g/m²). At the landscape scale, the XGBoost achieved high accuracy for estimating both the shrub AGB (R² = 0.719, RMSE = 4.2 g/m²) and total AGB (R² = 0.961, RMSE = 5.0 g/m²). The high-precision mapping results further facilitate the generation of shrub dominance maps at a landscape scale. This study presents a more accurate and efficient framework for mapping shrub AGB, total AGB, and shrub dominance using multi-scale remote sensing data, which offers new approaches for large-scale grassland AGB mapping and monitoring of shrub encroachment in grasslands.
Why it matches plant phenotyping methodsUAV・衛星データから低木および草本の地上部バイオマスと低木優占度を推定する取得・解析フレームワークが研究の中心であり、複数手法の精度検証も行っている。
abstractThis study presents a more accurate and efficient framework for mapping shrub AGB, total AGB, and shrub dominance using multi-scale remote sensing data
Solar-induced chlorophyll fluorescence (SIF) emitted from photosystem I (PSI) and photosystem II (PSII) is characterized by two peaks centered in the red and far-red spectral regions. SIF provides a unique remotely sensible signal to track plant photosynthetic dynamics. Compared with far-red SIF, red SIF (RSIF) is more strongly linked to PSII and thus with plant photosynthetic activity, but is subject to stronger reabsorption within leaves and canopies. This hinders the understanding and use of canopy RSIF observations (RSIFₒbₛ), which is only a small fraction of the total RSIF emitted by the photosystems (RSIFₜₒₜₐₗ). Deriving RSIFₜₒₜₐₗ from RSIFₒbₛ is still challenging due to retrieval uncertainty, limited availability of RSIFₒbₛ and spectral overlap with chlorophyll absorption. To address the challenges associated with deriving RSIFₜₒₜₐₗ, we propose an exploratory method framework that combines canopy far-red SIF observations (FRSIFₒbₛ) and leaf chlorophyll content (LCC) to derive RSIFₜₒₜₐₗ. We first downscale FRSIFₒbₛ from canopy to leaf, and then leverage LCC information to estimate RSIF at the leaf level. Finally, we incorporate LCC information in the subsequent downscaling of RSIF from leaf to photosystem. To evaluate our approach, we use ground-based observation data in three crop types (rice, wheat, and maize) and SCOPE model simulations. Our results demonstrate that the seasonal patterns of RSIFₜₒₜₐₗ show a close agreement with the seasonal patterns of gross primary production (GPP) and absorbed photosynthetic active radiation (APAR). More importantly, RSIFₜₒₜₐₗ slightly outperforms FRSIFₒbₛ in estimating GPP for the three crop types. Our study has also revealed a strong linear relationship between the escape probability of RSIFₜₒₜₐₗ (fₑₛc_R) and the RSIFₒbₛ/FRSIFₒbₛ ratio affected by LCC. The simplicity and robustness of our approach, along with its potential application in satellite remote sensing, will contribute to the improvement of large-scale GPP estimation and photosynthetic phenology detection. Moreover, our investigation of fₑₛc_R will contribute to a better understanding the physiological and non-physiological dynamics of RSIFₒbₛ.
Why it matches plant phenotyping methods葉・キャノピーの蛍光観測とクロロフィル量を統合し、光合成関連の植物生理状態を推定する方法枠組みを提案・評価しており、表現型取得・推定が研究の中心である。
abstractwe propose an exploratory method framework that combines canopy far-red SIF observations (FRSIFₒbₛ) and leaf chlorophyll content (LCC) to derive RSIFₜₒₜₐₗ
Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m²]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 – December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g., AMSR-E), current (e.g., AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers.
Why it matches plant phenotyping methods衛星・レーダー・ライダー等を統合し、植物の水分状態（LFMC）を推定する物理ベース手法を提案・検証しており、植物生理形質の取得方法が研究の中心である。
abstractIn this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit).
Rice blast (RB, caused by the fungus Magnaporthe oryzae) is the most devastating disease in global rice production, and can cause significant yield losses and increasingly threaten global food security. Accurate detection of RB occurrence with a universal metric is crucial to facilitate early disease prevention and curtailing the disease expansion but has not been addressed to date. This study aimed to design a rice blast index (RIBI) for quantifying the disease index (DI) and tracking the smallholder rice blast dispersal over multiple spatial scales. To achieve this goal, a large dataset including leaf- and canopy-scale reflectance spectra and satellite imagery was acquired within the framework of seven independent campaigns over four years (2018–2021). Specifically, an extensive collection of Magnaporthe oryzae infected samples were analyzed to examine the specific spectral response to pathogen infection in paddy rice from leaf to near-ground canopy scales. Two variants of the RIBI were developed, which were RIBIₙᵢᵣ ₌ (R₇₅₃-R₁₁₀₂)/(R₆₆₅ + R₁₁₀₂) and RIBIᵣₑd = (R₇₅₃-R₁₁₀₂)/(R₆₆₅ + R₁₁₀₂) based on the single-band separability and exhaustive search of band combinations. They were subsequently evaluated for quantifying the RB occurrence from ground to space. Spatial cluster analysis was then integrated with the superior RIBI adjusted for Sentinel-2 imagery to explore the spatio-temporal dynamics of pathogen infection, and to reveal the within-field hotspots of potential rice blast dispersal in smallholder farms. The results demonstrated that both RIBIₙᵢᵣ and RIBIᵣₑd exhibited high overall accuracies for the classification of infected and healthy samples at the leaf scale under greenhouse in 2018 (RIBIₙᵢᵣ: 81.41%; RIBIᵣₑd: 84.62%) and 2019 (RIBIₙᵢᵣ: 81.30%; RIBIᵣₑd: 90.37%) and field conditions in 2020 (RIBIₙᵢᵣ: 86.36%; RIBIᵣₑd: 89.39%). Compared with traditional VIs (Near-ground: R² < 0.47, satellite: R² < 0.54), the RIBIₙᵢᵣ yielded improved R² in quantifying the DI from in situ spectra to satellite imagery (Near-ground: R² = 0.73, satellite: R² = 0.78). The strongest DI-RIBIₙᵢᵣ relationship was attributed to the use of two near-infrared (NIR) bands that helped enhance the unique spectral responses in the NIR region induced by pathogen infection, in contrast to the extensively studied visible region. Multi-temporal analysis of Sentinel-2A derived RIBIₙᵢᵣ successfully captured the temporal dynamics of RB infection and recovery and yielded compelling maps showing the spatial propagation and attenuation of disease over time. This research opens new opportunities towards quantifying field disease occurrence and detecting the within-field hotspots of potential disease dispersal in a timely manner from publicly available satellite imagery.
Why it matches plant phenotyping methodsイネの病害状態（感染・発病度）をスペクトル指標で定量化する手法を開発し、葉・群落・衛星スケールで検証・適用しており、植物表現型取得が研究の中心である。
abstractThis study aimed to design a rice blast index (RIBI) for quantifying the disease index (DI) and tracking the smallholder rice blast dispersal over multiple spatial scales.
Various satellite sensors have provided a huge amount of observations of Earth's environment at variable spatial and temporal resolutions. Many global coarse-resolution land products have been generated from single-satellite data, but global temporally regular land products at fine spatial resolutions (say 10-30 m) are scarce because of infrequent observations. An ideal inversion framework can estimate multiple global continuous land variables at different spatial resolutions by combining all sources of satellite data. This paper proposes a new framework that can estimate five land variables simultaneously from the top-of-atmosphere (TOA) reflectance acquired by seven satellite sensors based on a multi-scale and multi-depth convolutional neural network (MSDCNN). This framework enables us to estimate temporally regular land variables at as fine as 10 m spatial resolution by transforming information from satellite data at coarser spatial resolutions. The estimated land variables include Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), shortwave albedo, visible albedo, and spectral reflectance. Seven sensors that acquire data at different spatial resolutions, include Visible Infrared Imaging Radiometer Suite (VIIRS) (750 m), Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m), Fengyun-3 (FY-3) Medium Resolution Spectral Imager (MERSI) (250 m), China-Brazil Earth Resources Satellite 04 (CBERS-04) Wide Field Imager (WFI) (73 m), Landsat 8 Operational Land Imager (OLI) (30 m), Gaofen-1 (GF-1) Wide-Field-of-View (16 m) and Sentinel 2 A/B Multispectral Imager (MSI) (10 m). This framework is mainly composed of four steps. First, a Shuffled Complex Evolution (SCE) optimization method is adopted to estimate these variables from VIIRS TOA reference. Second, a joint-output random forest regression (RF) method is used to link the satellite observations and the estimated values from step 1. Third, the six-type multi-resolution satellite observations are used to downscale the VIIRS TOA reflectance to six different spatial resolutions by using the MSDCNN. Finally, the downscaled six-resolution VIIRS TOA reflectance is fed into the multiple-variable RF model to estimate land variables. The framework was validated, and the results based on high-resolution reference maps from ImagineS network and time series shortwave albedo field values from Surface Radiation (SURFRAD) and Integrated Carbon Observation System (ICOS) network show that the retrieved variables had high validation accuracy, with root mean square error (RMSE) ranges of 0.361–0.489 (LAI), 0.023–0.120 (FAPAR), 0.013–0.026 (snow-free shortwave albedo), respectively. Comparison results of the retrieved multi-scale variables with the Sentinel 2 A/B (10 m), Landsat 8 (30 m), Global LAnd Surface Satellite (GLASS, 250 m, 500 m), MODIS (500 m), and VIIRS (750 m) products show that their values were close, with RMSE ranges of 0.107–0.273 (LAI), 0.015–0.027 (FAPAR), 0.003–0.007 (shortwave albedo), 0.001–0.007 (visible albedo), and 0.003–0.025 (spectral reflectance), respectively. The results of the direct validation as well as the product intercomparison show that this novel framework has the potential to be used in estimating global land variables at various spatial scales using a variety of satellite data sources.
Why it matches plant phenotyping methods衛星データとMSDCNN・RFを組み合わせ、LAIやFAPARなどの植物キャノピー形質を推定する手法を開発し、複数の高解像度参照データで検証しているため、方法が研究の中心である。
abstractThis paper proposes a new framework that can estimate five land variables simultaneously from the top-of-atmosphere (TOA) reflectance acquired by seven satellite sensors based on a multi-scale and multi-depth convolutional neural network (MSDCNN).
PROSPECT is the most widely used optical leaf model for a wide range of remote sensing applications on vegetation and has been developed and parameterised based on empirical data measured almost exclusively on terrestrial plant leaves. As aquatic plants differ substantially from terrestrial plants in leaf morphology and physiology, the validity of the relationships underlying PROSPECT in aquatic plants needs to be verified empirically. To this end, we compiled a comprehensive dataset of leaf spectra and biochemical-structural parameters sampled along a water affinity gradient, including floating and emergent hydrophytes, helophytes and riparian species, and terrestrial plants. In parallel, we designed a multidimensional experiment to explore the performance of PROSPECT across different groups and to characterise sources of modelling error, focusing on aquatic plants. Our results showed that estimates of most leaf parameters from PROSPECT inversions diverged increasingly from measured traits when moving from terrestrial to aquatic species. The suboptimal performance of PROSPECT on aquatic plants appears to be driven by three main factors: difficulties in disentangling leaf dry matter components (particularly proteins), unresolved issues related to the overlap of primary and secondary pigment mixtures and absorption, and the peculiarities of internal leaf structure (i.e. the presence of ‘aerenchyma’). These findings highlight the need for careful preliminary evaluation of the applicability and limitations of PROSPECT when applied to vegetation types that differ significantly from the typical terrestrial trees and grasses used for model calibration, including aquatic plants. Such evaluation should be preferably based on empirical data covering natural heterogeneity, so that future applications of remote sensing for mapping aquatic and wetland vegetation characteristics can be improved in terms of robustness and transferability.
Why it matches plant phenotyping methodsPROSPECT光学モデルによる葉形質推定の適用性と誤差要因を、水生植物を含む実測データで検証しており、植物表現型取得・推定手法の技術的評価が中心である。
abstractthe validity of the relationships underlying PROSPECT in aquatic plants needs to be verified empirically
Northern peatlands store a large amount of carbon in the form of partially decomposed organic matter. Because the majority of northern peatlands are located in remote areas, remote sensing serves as a suitable alternative to traditional surveys, enabling to enhance our understanding of peatland vegetation. Among various optical remote sensing signals, sun-induced fluorescence (SIF) is the most directly connected to carbon assimilation by plants, making it a promising early-response indicator for assessing the impact of climate change on natural ecosystems. However, the behavior of SIF throughout the season and the strength of the relationship between SIF and carbon assimilation for peatland vegetation, which consists of peat mosses and vascular plants with diverse anatomy, morphology, physiology, and phenology, have not yet been studied. Therefore, we conducted the first comprehensive assessment of the full spectrum SIF, reflectance, and gross primary production (GPP) for two distinct peatland vegetation communities under control (C), warming (W), and warming with reduced precipitation (WP) conditions throughout an entire season. While we could detect clear differences in SIF and reflectance between the two vegetation communities for original, C, vegetation during the main growing season, these differences diminished when W and WP were applied. The W and WP conditions caused a more pronounced change in plant biomass for vegetation characterized by a higher proportion of peat mosses and low creeping shrubs, which resulted in significant changes in the SIF and reflectance spectrum. Our findings demonstrate that the domination of peatland by vascular plants that is expected due to future warmer conditions causes stronger seasonal variation of SIF, reflectance, and GPP. We observed that far-red SIF and the spectrally integrated full SIF spectrum strongly correlate (r² > 0.85) with GPP regardless of vegetation community, temperature, and precipitation regime. However, the use of a novel multiple wavelength regression model using ten bands from the full SIF spectrum allowed for higher accuracy in the estimation of GPP compared to the use of single bands or integrated total SIF value. Moreover, such a model has a more stable performance when transferring from one vegetation community to another. Conversely, the correlation strength of traditional vegetation indices like the Normalized Difference Vegetation Index or MERIS Terrestrial Chlorophyll Index depends on the peatland vegetation community. While the SIF:GPP relationship exhibits similar r² values for vegetation communities with different ratios of planophile and electrophile leaves, the slope of the linear model depends on this ratio. This study performed at the ground level shows for the first time the importance of full spectrum SIF for the monitoring of a heterogeneous ecosystem like peatland, which will help to better utilize the SIF products obtained through current and future satellite missions.
Why it matches plant phenotyping methodsSIF・反射スペクトルを用いて植物群落のGPP（生産性）を推定するセンサーおよび回帰モデルを評価・開発しており、植物状態の測定手法が中心的である。
abstractsun-induced fluorescence (SIF) is the most directly connected to carbon assimilation by plants, making it a promising early-response indicator
Green area index (GAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) are key variables that are closely related to crop growth. Concurrent and continuous monitoring of GAI, LCC and CCC is critical to keep consistency among variables and make decisions for field precision managements. Previous studies have developed several instruments and algorithms to monitor continuous GAI, while the autonomous monitoring of three variables simultaneously has been lacking. This study presents a novel algorithm to retrieve daily GAI, LCC and CCC from continuous directional observations acquired by a fixed and economic affordable multi-band spectrometer (6 bands covering red, red-edge and near infrared domains) and a photosynthetically active radiation (PAR) sensor in the field. It is composed of three main steps, corresponding to three crucial questions when retrieving variables under natural environments using multi-band spectrometer installed on a near-surface platform: diffuse fraction in each spectral band, radiometric calibration and diurnal sun variation of daily acquisitions. First, we estimated diffuse fraction in each spectral band from the relationship with PAR diffuse fraction based on simulations of the 6S atmospheric radiative transfer model. Second, we computed the relative value of each band to the reference of mean of measurements on all six bands from near-surface measurements, in place of absolute radiometric calibration to limit the influence of changing illumination conditions. In the third step, we combined PROSAIL canopy radiative transfer model and kernel-driven models to retrieved GAI, LCC and CCC from artificial neural network using above spectral diffuse fraction and diurnal multi-angle relative observations. The algorithm was evaluated over 43 IoTA (Internet of things for Agriculture) systems that were installed in 29 wheat fields in France from March to May 2019. Results showed that our method provides good estimates of GAI with root mean square error (RMSE) of 0.54, relative RMSE (RRMSE) of 26.95%, R² of 0.86, LCC (RMSE = 12.06 μg/cm², RRMSE = 33.34%, R² = 0.52) and CCC (RMSE = 0.23 g/m², RRMSE = 24.58%, R² = 0.93). This study shows great potentials for concurrent estimates of GAI, LCC and CCC from continuous ground measurements. It will be useful over other vegetations or other near-surface platforms for simultaneous estimations of biophysical variables.
Why it matches plant phenotyping methods固定式マルチバンド分光計と放射伝達モデル・ニューラルネットワークを組み合わせ、作物のGAI、LCC、CCCを連続推定する取得・解析アルゴリズムを開発し、複数圃場で評価しているため、植物表現型測定法が中心である。
abstractThis study presents a novel algorithm to retrieve daily GAI, LCC and CCC from continuous directional observations acquired by a fixed and economic affordable multi-band spectrometer (6 bands covering red, red-edge and near infrared domains) and a photosynthetically active radiation (PAR) sensor in the field.
Solar-Induced chlorophyll Fluorescence (SIF) could be used as an indicator of photosynthetic status due to the close relationship between SIF and the photosynthetic apparatus. Terrestrial SIF is emitted throughout the red and near-infrared spectrum and is characterized by two peaks centered around 685 nm and 740 nm, respectively. In this study, we present a data-driven approach to reconstruct the terrestrial SIF spectrum from measurements by TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 precursor mission. This approach makes use of solar Fraunhofer lines in the combined spectral windows devoid of strong atmospheric absorption features to retrieve SIF signal from the solar radiation reflected by the surface and atmosphere system. Information contents are mainly from the two windows close to the red and far-red SIF peaks, 663–686 nm and 743–758 nm. A linear forward model represented as an addition of the SIF-free radiance spectrum and the SIF component is proposed with a proper selection of its parameter settings. The SIF component was simulated as linear combinations of 2 basis SIF spectra. Through inverting the linear forward model, the SIF spectrum was retrieved from the solar radiation reflected by the surface and atmosphere system. The evaluation of the retrieval results is performed by inter-comparison with other SIF datasets. The comparisons display similar spatial distributions for the weekly global SIF composites for the first two weeks in June and December of 2019 and July and December of 2021. Especially the comparison of the far-red SIF datasets with other dedicated far-red SIF retrievals demonstrates close agreement, indicating consistency among the retrieval approaches. The reconstructed TROPOMI red SIF shows improved and more reasonable spatiotemporal distributions. The retrieval uncertainty for the weekly global composite is about 12% and 2% of the peak red and far-red SIF values, respectively, which can be considered as satisfactory error thresholds for global composites of SIF observations. Different spectral features for several typical biomes from reconstructed SIF spectra suggest that red and far-red SIF may carry complementary information on photosynthetic function and biophysical properties of the plant. For the first time, the reconstruction of the SIF spectrum is achieved for spaceborne measurements with the potential to open new applications for better understanding of the ecosystem function.
Why it matches plant phenotyping methodsTROPOMI衛星観測から植物のクロロフィル蛍光スペクトルを推定する取得・復元手法を開発し、他データセットとの比較で検証しているため、植物の光合成状態・機能を測定するフェノタイピング手法が中心である。
abstractIn this study, we present a data-driven approach to reconstruct the terrestrial SIF spectrum from measurements by TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 precursor mission.
The launch of NASA's Ice, Cloud, And Elevation Satellite-2 (ICESat-2) in September 2018 provides the scientific community an opportunity to observe high-resolution and three-dimensional surface elevations with global coverage. ICESat-2's Land and Vegetation Height (ATL08) data product focuses on the along-track terrain and canopy heights observations at a 100 m × 11 m spatial resolution. This work expands on past ATL08 validation studies to assess a higher spatial resolution (30 m × 11 m) version of ATL08's height product. This new dataset enables higher resolution mapping and fusion with Landsat data, but has not previously been validated across large geographic extents. In this paper, we examine the accuracy of multi-resolution ICESat-2 ATL08 across North America boreal forests using Land, Vegetation, and Ice Sensor (LVIS), an airborne laser ranging system as reference datasets. Overall, strong agreements of terrain elevation and canopy height were found between ATL08 and LVIS at both 100 m × 11 m (RMSEₜₑᵣᵣₐᵢₙ = 2.35 m; biasₜₑᵣᵣₐᵢₙ = −0.17 m; RMSEcₐₙₒₚy = 4.17 m; biascₐₙₒₚy = 0.08 m) and 30 m × 11 m (RMSEₜₑᵣᵣₐᵢₙ = 3.19 m; biasₜₑᵣᵣₐᵢₙ = 0.49; RMSEcₐₙₒₚy = 4.75 m; biascₐₙₒₚy = 0.88 m), respectively. We found the accuracy of high-resolution terrain and canopy height measurements were constrained by sensor and external conditions during the time of data acquisition with lower uncertainties observed from samples along high-intensity ground tracks and with low topography/slope variabilities. Through this work, we provide insight into the use of multi-resolution ICESat-2 ATL08 for terrain and canopy heights characterization in northern forests. The results found in our study serve as a benchmark for end users to select high-quality ATL08 for a variety of scientific applications.
Why it matches plant phenotyping methodsICESat-2による森林キャノピー高という植物群落形質を、LVISを基準に複数解像度で検証し、精度・不確実性を評価した方法検証研究である。
abstractThis work expands on past ATL08 validation studies to assess a higher spatial resolution (30 m × 11 m) version of ATL08's height product.
Micronutrient malnutrition is a global challenge affecting >2 billion people, in particular those with a crop-based diet and limited access to nutrient-rich food sources. Conventional methods for measuring the crop nutrients such as wet chemical analysis of grains are time-consuming and cost-prohibitive and, consequently, unsuitable for the consistent quantification of nutrients across space and time. In this study, we propose a new method that is using PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Sentinel-2 images to estimate the nutrient concentrations of crop grains before harvest. We collected grain samples for corn, rice, soybean, and wheat from a farm situated in Italy and measured their nutrient concentrations in the lab. These measurements together with the PRISMA and Sentinel-2 images acquired at the main phases of crop development (vegetative, reproductive, maturity) were used as input for two-band vegetation indices (TBVIs) and Partial Least Squares Regression (PLSR) to predict Calcium (Ca), Iron (Fe), Potassium (K), Magnesium (Mg), Nitrogen (N), Phosphorus (P), Sulphur (S) and Zinc (Zn). Models' performances were assessed using the coefficient of determination (R²) and Root Mean Square Error (RMSE). For PRISMA images, the best prediction results were obtained for P in soybean (R² = 0.69), K in soybean (R² = 0.66), Mg in soybean (R² = 0.58), Fe in soybean (R² = 0.57), K in wheat (R² = 0.57), K in corn (R² = 0.55), P in wheat (R² = 0.51), S in rice (R² = 0.58) using TBVIs. In contrast to PRISMA, PLSR outperformed TBVIs when Sentinel-2 images were used as input. For Sentinel-2, the best predictions were obtained for P in soybean (R² = 0.73), K in wheat (R² = 0.67), Mg in soybean (R² = 0.62), Zn in wheat (R² = 0.56), Fe in soybean (R² = 0.52), P in wheat (R² = 0.52). Our study showed that estimating the nutrient composition of crops using remote sensing images has the potential to change how we approach a cost-effective, timely, and spatially explicit representation of the crops' nutritional quality.
Why it matches plant phenotyping methods衛星ハイパースペクトル・マルチスペクトル画像とTBVI/PLSRを用いて作物穀粒の栄養成分を推定する方法を提案し、実測値で性能評価しており、植物形質取得が中心である。
abstractwe propose a new method that is using PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Sentinel-2 images to estimate the nutrient concentrations of crop grains before harvest.
Rapid and accurate estimation of crop yield using remote sensing technology could be an important tool for improved global food security. As an effective probe measuring photosynthesis, sun-induced chlorophyll fluorescence (SIF) has potential for predicting crop yield, particularly when SIF measurements are integrated over an extended time period. However, few studies have investigated how temporal scale, vegetation structure, physiology and environmental factors affect crop yield prediction using SIF. Therefore, in this study we evaluate uncertainties in the relationship between SIF and wheat yield, associated with changes in leaf area index (LAI), chlorophyll a and b content (Cab), photosynthetic active radiation (PAR), and the timing of measurements over a range of temporal scales. Wheat field experiments were carried out over two years. LAI, Cab, PAR and canopy SIF were measured at several temporal scales. We systematically compared the performance of SIF parameters [near-infrared canopy SIF yield (SIFyNIR), total near-infrared SIF yield at photosystem level (SIFyNIR_ₜₒₜ), and normalized difference fluorescence index (NDFI)] and vegetation indices (VIs) [normalized difference vegetation index (NDVI), and NIR reflectance of vegetation (NIRv)] as predictors of yield estimation. Among the SIF parameters, NDFI appeared to be the most sensitive to LAI and Cab. SIFyNIR_ₜₒₜ at the anthesis stage was the best predictor of wheat yield. SIF outperformed VIs for wheat yield estimation during the late growth period. Moreover, as the temporal scale increased (i.e., as the data values were accumulated over longer intervals of time), the relationship between SIFyNIR and wheat yield tended to be more linear. Overall, the uncertainty in the relationship between SIF and yield was affected more by LAI than Cab, and higher PAR produced a stronger and more stable relationship between SIF and wheat yield. Our findings provide empirical support and an example of an approach for using SIF to predict crop yield, as well as elucidation of the mechanisms underlying the relationship between SIF and production.
Why it matches plant phenotyping methodsSIFを用いたキャノピー生理計測と収量推定の関係を、時間スケールや環境要因を含めて系統的に評価しており、植物形質取得・推定手法の技術的応用が中心である。
abstractwe evaluate uncertainties in the relationship between SIF and wheat yield, associated with changes in leaf area index (LAI), chlorophyll a and b content (Cab), photosynthetic active radiation (PAR), and the timing of measurements over a range of temporal scales.
Accurately monitoring soybean growth stages (SGSs) is crucial for successful crop management and the development of agricultural information systems. This study focused on 18 states that accounted for over 95% of the soybean area in the United States from 2013 to 2020. We proposed an SMFs-APTT method that integrates crop data layers (CDLs), time-series of VIIRS data, and meteorological data. It combines a shape-model function in separate meteorological stages (SMFs) with a crop model that relies on an accumulated photothermal time (APTT). This approach provides both full-season and within-season monitoring of four soybean growth stages (SGSs); i.e., emergence, blooming, pod-setting, and leaf-dropping. Based on the results obtained from the SMFs-APTT method, a long short-term memory (LSTM) model was employed to predict SGSs early. The dates of the detected and predicted SGSs were compared with National Agricultural Statistics Service (NASS) Crop Progress and Condition Report (CPR) statistical data for analysis and verification. Our results show the following. (1) For the full-season extraction of SGSs, the average root mean square error (RMSE) derived using the SMFs–APTT method was 0.86–3.06 days and that obtained using the SMFs method was 1.56–3.26 days. (2) For the within-season monitoring of SGSs, the SMFs–APTT method was also able to accurately track the growth stages as early as ∼30 days after they reach 50% completion with an average RMSE of 2.1 days. (3) For the early prediction of SGSs, the LSTM model that was trained based on the SMFs–APTT results achieved an RMSE of ∼4.3 days at the state scale and could approximately predict SGSs ∼30 days in advance. Our findings suggest that the SMFs–APTT method provides accurate and reliable extraction of SGSs for full-season and within-season monitoring, which is of benefit to crop modeling and management. Furthermore, the LSTM model successfully forecast SGSs, indicating its potential for making early predictions.
Why it matches plant phenotyping methods衛星・気象データと作物モデルを統合してダイズの生育段階を抽出・予測する手法を開発し、RMSEで検証しているため、植物状態の取得方法が中心である。
abstractWe proposed an SMFs-APTT method that integrates crop data layers (CDLs), time-series of VIIRS data, and meteorological data.
Timely knowledge of phenological development and crop growth is pivotal for evidence-based decision making in agriculture. We propose a near real-time approach combining radiative transfer model inversion with physiological and phenological priors from multi-year field phenotyping. Our approach allows to retrieve Green Leaf Area Index (GLAI), Canopy Chlorophyll Content (CCC) and hence Leaf Chlorophyll Content (Cab) from Sentinel-2 optical satellite imagery to quantify winter wheat growth conditions in a physiologically sound way. Phenological macro stages are based on accumulated growing degree day thresholds obtained from multi-year field phenotyping covering more than 2400 ratings from roughly 300 winter wheat varieties and reflect important physiological transitions. These include the transition from vegetative to reproductive growth and the onset of flowering, which is important information for agricultural decision support. Validation against a large data set of on-farm trials in Switzerland collected in 2019 and 2022 revealed high accuracy of our approach that produced spatio-temporally consistent results. Phenological macro stages were predicted for 970 Sentinel-2 observations reaching a weighted F1-score of 0.96. Sentinel-2 derived GLAI and CCC explained between 77 to 84% and between 79 to 84% of the variability in in-situ measurements, respectively. Here, the incorporation of phenological priors clearly increased trait retrieval accuracy. Besides, this work highlights that physiological priors, e.g., obtained by field phenotyping, can help enhancing landscape scale observations and hold potential to advance the retrieval of remotely sensed vegetation traits and in-season phenology.
Why it matches plant phenotyping methods衛星画像から冬コムギのGLAI、CCC、葉緑素含量および生育ステージを推定する手法を開発・検証しており、植物形質の取得と精度評価が研究の中心である。
abstractWe propose a near real-time approach combining radiative transfer model inversion with physiological and phenological priors from multi-year field phenotyping.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods多様な作物種を対象に、航空機載画像分光法で作物群落の窒素状態を定量する一般的方法論を開発する研究であり、植物群落の生理形質推定が中心です。
titleA general methodology for the quantification of crop canopy nitrogen across diverse species using airborne imaging spectroscopy
Crop phenology has been widely detected from multiple historical satellite observations. Conversely, Near-Real-Time (NRT) monitoring of crop progress from timely available remote sensing data is barely investigated because of the lack of high-frequency cloud-free satellite observations and future potential crop development. To address the challenge, this study proposes a novel algorithm for operational NRT monitoring of crop progress at the field scale. This algorithm first fuses the high spatial resolution (30 m) Harmonized Landsat and Sentinel-2 (HLS) data and the high temporal frequent (10 min) Advanced Baseline Imager (ABI) observations to generate cloud-free time series of HLS-ABI EVI2 (two-band Enhanced Vegetation Index) with a Spatiotemporal Shape-Matching Model (SSMM). It then predicts future potential EVI2 values at a given pixel using a reference EVI2 time series obtained from the neighboring pixels in the preceding year. Integrating the currently available HLS-ABI observations and the predicted future EVI2 values to generate annual EVI2 time series, the algorithm finally detects six crop phenometrics including greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset. The NRT monitoring, which are separated as near-real-time prediction (phenological event detected after the occurrence), real-time prediction (phenological event detected around the occurrence), and short-term prediction (phenological event detected before the occurrence), are continuously updated and improved with new HLS and ABI observations at a weekly basis throughout the growing season. We evaluate the NRT monitoring against standard phenology products, PhenoCam observations, as well as the weekly Crop Progress Reports (CPRs) released from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) in 2020 across Iowa. The evaluation demonstrates the robustness of the developed algorithm in NRT monitoring of crop phenology. Although the uncertainties are relatively large for short-term prediction compared with standard detections, the real-time prediction shows that the Mean Absolute Difference (MAD) is 0.85, P < 0.001) for various phenological stages of corn and soybean. These results prove that the algorithm could be implemented for NRT monitoring of various crop phenometrics from field, state, to national scales.
Why it matches plant phenotyping methods衛星時系列を融合して作物のフェノロジー形質を抽出するアルゴリズムを開発し、複数の標準データで検証しており、植物フェノタイピング手法が中心である。
abstractWe evaluate the NRT monitoring against standard phenology products, PhenoCam observations, as well as the weekly Crop Progress Reports (CPRs)
The novel modification in the WCM (mWCM) is proposed in this study to simulate total backscattering contribution and to improve Leaf Area Index (LAI) and Soil Moisture (SM) retrieval using Sentinel-1 Single Look Complex (SLC) datasets at VV and VH polarizations for the wheat crop. The intended modification was achieved through two steps; (1) Proposing the scaling constants of vegetation (fveg), soil (fsoil) and vegetation-soil interaction (finter) within the traditional WCM. The scaling constants are dimensionless quantity and were derived utilizing the degree of polarization (which were computed using the Hermitian covariance matrix); (2) Incorporation of the first order scattering component derived from the novel Vegetation-Soil Scattering Model (VSSM) to the total backscattering within the traditional WCM. The aim of including the vegetation-soil interaction in land surface models is to predict the combined effect of vegetation and soil on the total backscattering. The model parameters (i.e., A, B, C, and E) were calibrated using a non-linear least square regression algorithm. The accuracy of the retrieved and measured LAI and SM is evaluated using the different statistical indicators, e.g., coefficient of determination (R²), Root Mean Square Error (RMSE), and Nash Sutcliffe Efficiency (NSE). The retrieval from mWCM produced better accuracy with lower error than traditional WCM. The forward simulation results of mWCM revealed a notably higher accuracy for the total simulated radar backscattered coefficient at the VH polarization (σ0VH). The VH results showed a high R² = 0.86, a high NSE = 0.85, and a low RMSE = 0.51 dB, outperforming the simulated σ0VV with R² = 0.84, NSE = 0.84, RMSE = 0.66 dB. Consequently, the inversion of the mWCM yielded significantly improved accuracy in retrieving LAI at the VH polarization. The VH retrieval results exhibited a R² = 0.80, NSE = 0.78, and RMSE = 0.44 m²m⁻², while the VV polarization achieved an R² = 0.78, NSE = 0.77, and RMSE = 0.53 m²m⁻² for LAI estimation. In SM retrieval, higher accuracy was observed at VV polarization with R² value of 0.77, NSE value = 0.79, and RMSE = 0.048 m³m⁻³ than the VH polarization with R² = 0.75, NSE = 0.76, and RMSE = 0.050 m³m⁻³.
Why it matches plant phenotyping methodsSentinel-1 SARを用いてLAIという植物形質を推定する新規改良モデルを開発し、従来法と精度比較・検証しているため、植物フェノタイピング手法が中心である。
abstractThe novel modification in the WCM (mWCM) is proposed in this study to simulate total backscattering contribution and to improve Leaf Area Index (LAI) and Soil Moisture (SM) retrieval using Sentinel-1 Single Look Complex (SLC) datasets at VV and VH polarizations for the wheat crop.
Sun-induced fluorescence (SIF) as a close remote sensing based proxy for photosynthesis is accepted as a useful measure to remotely monitor vegetation health and gross primary productivity. It is therefore important to develop methods that allow for its precise and reliable retrieval from radiance measurements with spectral resolutions that have been increasing over the past few years. Retrieval methods are catching up to the increasing complexity of the available datasets making use of their whole information extent (spectral, spatial and temporal) but the comparability of different SIF retrievals and consistency across scales is still limited. In this work we present the new retrieval method WAFER (WAvelet decomposition FluorEscence Retrieval) based on wavelet decompositions of the measured spectra of reflected radiance as well as a reference radiance not containing fluorescence. By comparing absolute absorption line depths by means of the corresponding wavelet coefficients, a relative reflectance is retrieved independently of the fluorescence, i.e. without introducing a coupling between reflectance and fluorescence. The fluorescence can then be derived as the remaining offset. This method can be applied to arbitrary chosen wavelength windows in the whole spectral range, such that all the spectral data available is exploited, including the separation into several frequency (i.e. width of absorption lines) levels and without the need of extensive training datasets. At the same time, the assumptions about the reflectance shape are minimal and no spectral shape assumptions are imposed on the fluorescence, which not only avoids biases arising from wrong or differing fluorescence models across different spatial scales and retrieval methods but also allows for the exploration of this spectral shape for different measurement setups. WAFER is tested on a synthetic dataset as well as several diurnal datasets acquired with a field spectrometer (FloX) over an agricultural site. We compare the WAFER method to two established retrieval methods, namely the improved Fraunhofer line discrimination (iFLD) method and spectral fitting method (SFM) and find a good agreement with the added possibility of exploring the true spectral shape of the offset signal and free choice of the retrieval window. On our synthetic dataset, WAFER seems to outperform the SFM and works best in a spectral window only containing solar Fraunhofer lines where we achieve a relative retrieval error of 10% on average. Applied to the real dataset, the method returns reasonable diurnal cycles for SIF and can, due to the decoupling of reflectance and fluorescence retrieval, reveal interesting trends at times when vegetation canopies may experience a midday depression that remain largely unobserved with current methods.
Why it matches plant phenotyping methods植物の光合成状態を示すSIFをスペクトル測定から抽出する新手法を開発し、合成・実測データおよび既存手法との比較で検証しているため、植物フェノタイピング手法が中心である。
abstractIn this work we present the new retrieval method WAFER (WAvelet decomposition FluorEscence Retrieval) based on wavelet decompositions of the measured spectra of reflected radiance as well as a reference radiance not containing fluorescence.
Infection by the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf) threatens the production of olives (Olea europaea L.) and almonds (Prunus dulcis Mill.) worldwide. Producing symptoms that resemble water stress or nutrient deficiency, infection by these vascular pathogens restricts water and nutrient flow through the xylem. Hyperspectral, narrow-band multispectral, and thermal imagery acquired at a high spatial resolution can detect disease symptoms, even before they are visible, potentially allowing growers to distinguish infected plants from those affected by confounding environmental stresses. Nevertheless, operational detection of vascular disease using high-resolution commercial satellite multispectral images remains to be evaluated. Here, we assessed the capacity of high-resolution Worldview-2 and -3 multispectral imagery to detect Xf and Vd infections in olive and almond orchards in Spain, Italy, and Australia between 2011 and 2021. We compared the accuracy of detecting both pathogens using the satellite imagery with results obtained using aerial high-resolution hyperspectral and thermal imaging, with model-inverted plant traits, solar-induced chlorophyll fluorescence (SIF), and thermal data as a reference. Our results using spectral plant traits to examine disease progression at all stages showed that traits and their importance varied as a function of disease severity. Worldview-2 and -3 detected the disease incidence with overall accuracies ranging from 0.63 to 0.83 and kappa coefficients (κ) ranging from 0.29 to 0.68. Nevertheless, detecting the early stages of disease with multispectral satellite data yielded poorer results, with κ values of 0.22–0.45, compared with κ values of 0.3–0.69 obtained from hyperspectral data. Typical multispectral bandsets available from satellite sensors cannot measure important plant traits such as the blue index NPQI, xanthophyll proxy PRIn, SIF, and anthocyanin levels, thus explaining the poorer results obtained from multispectral satellite data for the early detection of vascular diseases. Adding a thermal-based crop water stress indicator to the satellite data improved the overall accuracies by 10–15% and increased κ by >0.2 units. This work shows that commercial multispectral high-spatial resolution imagery can be used to detect intermediate and advanced Xf and Vd infection, but that the early detection of disease symptoms requires hyperspectral and thermal data.
Why it matches plant phenotyping methods高解像度の衛星・航空ハイパースペクトル・熱画像を用いて、植物病害の症状、病勢、植物形質を検出・評価し、センサー間の精度比較も行うことが中心であるため、植物フェノタイピング手法研究に該当する。
abstractHere, we assessed the capacity of high-resolution Worldview-2 and -3 multispectral imagery to detect Xf and Vd infections in olive and almond orchards
Infection by the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf) threatens the production of olives (Olea europaea L.) and almonds (Prunus dulcis Mill.) worldwide. Producing symptoms that resemble water stress or nutrient deficiency, infection by these vascular pathogens restricts water and nutrient flow through the xylem. Hyperspectral, narrow-band multispectral, and thermal imagery acquired at a high spatial resolution can detect disease symptoms, even before they are visible, potentially allowing growers to distinguish infected plants from those affected by confounding environmental stresses. Nevertheless, operational detection of vascular disease using high-resolution commercial satellite multispectral images remains to be evaluated. Here, we assessed the capacity of high-resolution Worldview-2 and -3 multispectral imagery to detect Xf and Vd infections in olive and almond orchards in Spain, Italy, and Australia between 2011 and 2021. We compared the accuracy of detecting both pathogens using the satellite imagery with results obtained using aerial high-resolution hyperspectral and thermal imaging, with model-inverted plant traits, solar-induced chlorophyll fluorescence (SIF), and thermal data as a reference. Our results using spectral plant traits to examine disease progression at all stages showed that traits and their importance varied as a function of disease severity. Worldview-2 and -3 detected the disease incidence with overall accuracies ranging from 0.63 to 0.83 and kappa coefficients (κ) ranging from 0.29 to 0.68. Nevertheless, detecting the early stages of disease with multispectral satellite data yielded poorer results, with κ values of 0.22–0.45, compared with κ values of 0.3–0.69 obtained from hyperspectral data. Typical multispectral bandsets available from satellite sensors cannot measure important plant traits such as the blue index NPQI, xanthophyll proxy PRIₙ, SIF, and anthocyanin levels, thus explaining the poorer results obtained from multispectral satellite data for the early detection of vascular diseases. Adding a thermal-based crop water stress indicator to the satellite data improved the overall accuracies by 10–15% and increased κ by >0.2 units. This work shows that commercial multispectral high-spatial resolution imagery can be used to detect intermediate and advanced Xf and Vd infection, but that the early detection of disease symptoms requires hyperspectral and thermal data.
Why it matches plant phenotyping methods高解像度衛星・航空ハイパースペクトル・熱画像を用いて植物病害症状を検出し、精度比較・評価しており、植物の病害状態を推定するセンシング手法が研究の中心です。
abstractHere, we assessed the capacity of high-resolution Worldview-2 and -3 multispectral imagery to detect Xf and Vd infections in olive and almond orchards
Oil palm is the most efficient oil-producing crop but its extension leads to increased deforestation in Southeast Asia. Oil palm height enables the quantitative estimation for carbon stock or palm oil yield. Nevertheless, there are still no accurate characterization of oil palm height providing information for the tradeoff between forest damage and carbon stock of oil palm in Southeast Asia. The new generation of spaceborne LiDAR provides large-extent canopy height samples, offering an opportunity for mapping the oil palm height at the regional scale yet with challenge to extrapolate the footprint heights to spatially coherent maps. Here, we proposed a new method by combining Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) footprint data with stand age that is closely related to tree height. We first developed a semi-automatic filtering algorithm to filter the low-quality ICESat-2 data, and used a change detection algorithm to optimize the planting year map of oil palm. Then, an empirical age-height model was derived by linking ICESat-2 footprint canopy height with oil palm age that was used to estimate spatially and temporally oil palm height for the whole Peninsular Malaysia. A validation with independent ICESat-2 footprint data suggests a high agreement for the height estimates in 2020 from the age-height model (R² = 0.63; RMSE = 1.64 m) with a bias within ±3 m for >90% of the height estimates. Using the age-height model and planting year map of oil palm, we produced the first comprehensive wall-to-wall maps of long-term yearly oil palm height at a spatial resolution of 30 m in Peninsular Malaysia during 2001 through 2020. Our results suggest that the mean height of oil palm in all and regionally-disturbed areas have increased by 10.82 m and 9.29 m respectively during the last two decades in Peninsular Malaysia. Our results indicate that combining stand age and ICESat-2 footprint data has great potential in spatially-explicit mapping regional oil palm height that contributes to a better quantification for regional plantation carbon stock.
Why it matches plant phenotyping methodsICESat-2と樹齢データを用いて油ヤシ樹高を推定・地図化する手法を開発し、独立データで検証しており、植物形質の取得方法が中心である。
abstractHere, we proposed a new method by combining Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) footprint data with stand age that is closely related to tree height.
Solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in modeling global terrestrial gross primary production (GPP). Canopy total SIF emissions (SIFₜₒₜₐₗ) are mechanistically linked to the plant photosynthesis, and can be estimated from satellite observed SIF (SIFₒbₛ) through radiative transfer modeling. However, the current satellite SIFₒbₛ and thus SIFₜₒₜₐₗ are available only at coarse spatial resolutions from several kilometers to tens of kilometers, inhibiting the application at fine spatial scales. Here, we proposed an algorithm to generate both global high-resolution SIFₜₒₜₐₗ (HSIFₜₒₜₐₗ) and high-resolution SIFₒbₛ (HSIFₒbₛ) at 1 km from low-resolution SIFₒbₛ (LSIFₒbₛ) from the TROPOspheric Monitoring Instrument (TROPOMI), which has a spatial resolution at nadir of 3.5 km by 5.6–7 km. Our statistical method is based on the law of energy conservation and uses satellite derived fraction of absorbed photosynthetically active radiation, fluorescence efficiency, and the escape probability of fluorescence. We evaluated the accuracy of our HSIFₜₒₜₐₗ using the Orbiting Carbon Observatory-2 SIF (R² = 0.78). We found that the spatial resolution had clear effects on the relationship between HSIFₜₒₜₐₗ and GPP. We also compared HSIFₜₒₜₐₗ to 8-day averaged tower GPP from 135 flux sites and found that they were better correlated when HSIFₜₒₜₐₗ was averaged over a 1-km radius around the tower than when averaged over a larger radius. Our study provided a unique high-resolution HSIFₜₒₜₐₗ product, which will advance the estimation of GPP by extrapolating site-level relationships to the global scale.
Why it matches plant phenotyping methods衛星SIFから植物キャノピーの蛍光・光合成関連状態を高解像度で推定するアルゴリズムを開発し、独立データで精度検証しており、植物フェノタイピング手法が中心である。
abstractHere, we proposed an algorithm to generate both global high-resolution SIFₜₒₜₐₗ (HSIFₜₒₜₐₗ) and high-resolution SIFₒbₛ (HSIFₒbₛ) at 1 km from low-resolution SIFₒbₛ (LSIFₒbₛ) from the TROPOspheric Monitoring Instrument (TROPOMI)
Soil moisture (SM) and vegetation optical depth (VOD) are essential variables in the terrestrial ecosystem. The multi-frequency radiometers AMSR-E and AMSR2 provide >20 years of data records, enabling the development of long-term SM and VOD products. Most of the current retrieval algorithms either only focus on SM or VOD, and generally ignore the polarization or simplify the frequency dependence of vegetation effects for reducing the unknowns and facilitating the retrieval process, limiting the synergic applicability of VOD and SM products in the soil-plant-atmosphere continuum. In this study, a new global SM and frequency- and polarization-dependent VOD product from 2002 to 2021 was developed using the multi-channel collaborative algorithm (MCCA), based on the inter-calibrated AMSR-E/2 multi-frequency passive microwave measurements. The MCCA algorithm comprehensively considers the physical relationship between multiple microwave channels and could retrieve frequency- and polarization-dependent VOD while considering the accuracy of the SM retrievals. In the overall comparison with other SM products (AMSR-ANN, CCI-passive v07.1, LPRM-C/X, JAXA) over 25 dense SM networks, MCCA achieved the best scores in terms of root mean square error (RMSE = 0.074 m³/m³), unbiased root mean square error (ubRMSE = 0.073 m³/m³) and bias (0.007 m³/m³), and presented slightly lower value of Pearson's correlation coefficient (R = 0.709) than LPRM-X (R = 0.735). For the indirect evaluation of VOD with aboveground biomass (AGB) and MODIS NDVI, the MCCA product showed the performance comparable to other products (LPRM-C/X, VODCA-C/X/Ku). MCCA-derived VODs, especially for the H-polarized VODs, exhibited smooth non-linear density distribution with AGB and high temporal correlations with MODIS NDVI over most regions of the globe. In particular, MCCA-derived VODs can physically present reasonable variations across the microwave spectrum (values of VOD increase with microwave frequency), which is superior to the LPRM and VODCA products. It is expected that the MCCA algorithm can be extended to the observations of the ongoing AMSR2 or other similar satellite missions with multi-frequency capability, such as FY-3B/C/D/F/G or the upcoming AMSR3 and CMIR missions.
Why it matches plant phenotyping methodsAMSR-E/2の多周波マイクロ波観測から植生光学的厚さ（VOD）を推定する新しい取得・推定アルゴリズムと20年全球データセットを開発し、AGBやNDVI等で評価しているため、植生状態の測定法が中心である。
abstracta new global SM and frequency- and polarization-dependent VOD product from 2002 to 2021 was developed using the multi-channel collaborative algorithm (MCCA)
Clumping describes the spatial distribution of foliage elements (leaves or needles) within a vegetation canopy. Clumping information is important for determining the radiation transfer through canopies, photosynthesis, and hydrological processes. Clumping of needles in shoots in conifer stands has posed a challenge because optical instruments have generally been incapable of measuring gaps between needles within a shoot. Previous methods for estimating the needle-to-shoot-area ratio have had in common destructive and/or highly labor-intensive aspects. We introduce blue light 3D photogrammetry scanning as a highly efficient technique for estimating shoot-level clumping, which significantly reduces the labor intensity aspect of the previous approaches. We validate the approach by comparing it to the combined photographic/volume displacement method – an established methodology for quantifying shoot-level clumping. We used shoots of two species - Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies L. Karst.) - collected from trees in the Järvselja RAdiation transfer Model Intercomparison (RAMI) pine stand in Estonia. The needle-to-shoot area ratio values were similar to those measured using the traditional combined photographic/volume displacement method. The demonstrated effectiveness and performance of the blue light 3D photogrammetry scanning method shall lead to more frequent actual measurements of 3D shoot structures. Growth in knowledge about this most elementary yet often overlooked level of foliage clumping in canopies shall improve coniferous forest 3D radiative transfer modeling.
Why it matches plant phenotyping methods青色光3Dフォトグラメトリで針葉シュート構造・針葉面積比を推定する手法を開発し、既存法と比較検証しており、植物形質取得が中心である。
abstractWe introduce blue light 3D photogrammetry scanning as a highly efficient technique for estimating shoot-level clumping
The obtention of wall-to-wall fire severity estimates through reliable remote sensing-based techniques that align with management needs is a critical factor in post-fire decision-making processes. In this paper, we novelty proposed a multi-date change detection framework based on the variation in fractional vegetation cover (FCOVER), with enough ecological sense and physical basis to be generalizable across different plant communities and burned landscapes with varying environmental conditions. This framework meets the definition of fire severity operationally used in the field as a biophysical indicator when fire effects on the understory and overstory layers are linked. The FCOVER was retrieved from Sentinel-2 surface reflectance scenes by inverting PROSAIL-D radiative transfer model (RTM) simulations using the random forest regression algorithm. FCOVER retrievals were validated in the field using burned and unburned control plots. We computed the FCOVERr metric as the ratio of post-fire to pre-fire FCOVER. We tested the relationship of the FCOVERr and the most common bi-temporal spectral indices in the literature, i.e. the differenced Normalized Burn Ratio (dNBR), the Relative dNBR (RdNBR) and the Relativized Burn Ratio (RBR), with the Composite Burn Index (CBI) measured in field plots for validation purposes in two case-study wildfires in the western Mediterranean Basin. We also calculated the transferability of FCOVERr and the spectral indices between different plant communities within each site, as well as between sites. The predictive errors of pre and post-fire FCOVER retrievals were found to be low (RMSE ≈ 10%) for the two study sites. Overall, the FCOVERr metric provided more accurate CBI estimations (R² = 0.87 ± 0.04) than spectral indices (R² = 0.71 ± 0.13). The CBI was linearly related with the FCOVERr metric for both sites, whereas the type of relationship with spectral indices was not consistent, which translated into better transferability performance of the FCOVERr metric (nRMSE = 14.27% ± 3.75%) than that of the spectral indices (nRMSE = 21.97% ± 8.09%), not only between different Mediterranean plant communities within sites, but also between the two sites. Spectral indices underestimated moderate to high fire severity to a greater extent than FCOVERr in the CBI field plots, and misclassified fire severity in several areas with patchiness fire effects identified in the field. The FCOVERr product proposed in this study may be a sound choice for the operational identification of priority areas for post-fire management.
Why it matches plant phenotyping methodsSentinel-2と放射伝達モデル・機械学習により植物群落の分数植生被覆を推定し、現地プロットで検証して火災影響・植生状態を評価する手法が研究の中心である。
abstractIn this paper, we novelty proposed a multi-date change detection framework based on the variation in fractional vegetation cover (FCOVER)
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods高精度の青色光3Dフォトグラメトリースキャンにより、針葉樹のシュート構造という植物形態形質を推定する手法が中心である。
titleEstimation of coniferous shoot structure by high precision blue light 3D photogrammetry scanning
Leaf chlorophyll, as a key factor for carbon circulation in the ecosystem, is significant for the photosynthetic productivity estimation and crop growth monitoring in agricultural management. Hyperspectral remote sensing (RS) provides feasible solutions for obtaining crop leaf chlorophyll content (LCC) by the advantages of its repeated and high throughput observations. However, the data redundancy and the poor robustness of the inversion models are still major obstacles that prevent the widespread application of hyperspectral RS for crop LCC evaluation. For winter wheat LCC inversion from hyperspectral observations, this study described a novel hybrid method, which is based on the combination of amplitude- and shape- enhanced 2D correlation spectrum (2DCOS) and transfer learning. The innovative feature selection method, amplitude- and shape- enhanced 2DCOS, which originated from 2DCOS, additionally considered the relationships between external perturbations and hyperspectral amplitude and shape characteristics to enhance the dynamic spectrum response. To extract the representative LCC featured wavelengths, the amplitude- and shape- enhanced 2DCOS was conducted on the leaf optical PROperties SPECTra (PROSPECT) + Scattering from Arbitrarily Inclined Leaves (SAIL) (PROSAIL) simulated dataset, which covered most possible winter wheat canopy spectra. Nine wavelengths (i.e., 455, 545, 571, 615, 641, 662, 706, 728, and 756 nm) were then extracted as the sensitive wavelengths of LCC with the amplitude- and shape- enhanced 2DCOS. These wavelengths had specificity to LCC and showed good correlation with LCC from the aspect of photosynthesis mechanism, molecular structure, and optical properties. The transfer learning techniques based on the deep neural network was then introduced to transfer the knowledge learned from the PROSAIL simulated dataset to the inversion tasks of field measured LCC. Parts of the labeled samples in field observations were used to finetune the model pre-trained by the simulated dataset to improve the inversion accuracy of the winter wheat LCC in different field scenes, aiming to reduce the need for the field measured and labeled sample size. To further ascertain the universality, transferability and predictive ability of the proposed hybrid method, field samples collected from different locations at different phenological phases, including the jointing and heading stages in 2013, 2014, and 2018, were utilized as target tasks to validate the proposed hybrid method. Moreover, the LCC of winter wheat estimated with the proposed method was evaluated with the ground-based platform and the UAV-based platform to verify the model versatility for different monitoring platforms. Various validations demonstrated that the hybrid inversion method combining the amplitude- and shape- enhanced 2DCOS and the fine-tuned transfer learning model could effectively estimate winter wheat LCC with good accuracy and robustness, and can be extended to the detection and inversion of other key variables of crops.
Why it matches plant phenotyping methods冬小麦葉 chlorophyll 含量という植物生理形質を、ハイパースペクトルデータと特徴波長選択・転移学習で推定する手法を開発し、複数圃場・時期・測定プラットフォームで検証しており、表現型取得法が中心である。
abstractFor winter wheat LCC inversion from hyperspectral observations, this study described a novel hybrid method, which is based on the combination of amplitude- and shape- enhanced 2D correlation spectrum (2DCOS) and transfer learning.
Disentangling the individual contributions from vegetation and soil in measured canopy reflectance is a grand challenge to the remote sensing and ecophysiology communities. Since Solar Induced chlorophyll Fluorescence (SIF) is uniquely emitted from vegetation, it can be used to evaluate how well reflectance-based vegetation indices (VIs) can separate the vegetation and soil components. Due to the residual soil background contributions, Near-infrared (NIR) reflectance of vegetation (NIRv) and Difference Vegetation index (DVI) present offsets when compared to SIF (i.e., the value of NIRv or DVI is non-zero when SIF is zero). In this study, we proposed a simple framework for estimating the true NIR reflectance of vegetation from Hyperspectral measurements (NIRvH) with minimal soil impacts. NIRvH takes advantage of the spectral shape variations in the red-edge region to minimize the soil effects. We evaluated the capability of NIRvH, NIRv and DVI in isolating the true NIR reflectance of vegetation using the data from both the model-based simulations and Hyperspectral Plant imaging spectrometer (HyPlant) measurements. Benchmarked by simultaneously measured SIF, NIRvH has the smallest offset (0–0.037), as compared to an intermediate offset of 0.047–0.062 from NIRv, and the largest offset of 0.089–0.112 from DVI. The magnitude of the offset can vary with different soil reflectance spectra across spatio-temporal scales, which may lead to bias in the downstream NIRv-based photosynthesis estimates. NIRvH and SIF measurements from the same sensor platform avoided complications due to different geometry, footprint and time of observation across sensors when studying the radiative transfer of reflected photons and SIF. In addition, NIRvH was primarily determined by canopy structure rather than chlorophyll content and soil brightness. Our work showcases that NIRvH is promising for retrieving canopy structure parameters such as leaf area index and leaf inclination angle, and for estimating fluorescence yield with current and forthcoming hyperspectral satellite measurements.
Why it matches plant phenotyping methodsハイパースペクトル測定から土壌影響を抑えて植生の真のNIR反射を推定する枠組みを開発し、シミュレーションとHyPlant測定で既存指標と比較検証している。葉面積指数や葉傾斜角などのキャノピー形質推定に結び付くため、方法が中心的である。
abstractIn this study, we proposed a simple framework for estimating the true NIR reflectance of vegetation from Hyperspectral measurements (NIRvH) with minimal soil impacts.
Water fulfils key roles in maintaining a plant's biological activity. Water shortage induces stomatal closure, causing a reduction in photosynthesis and transpiration rates. Sun-induced chlorophyll fluorescence (SIF) emission is sensitive to subtle, stress-induced variations in non-photochemical quenching and in photosynthetic electron transport, caused by e.g., a fluctuation in the water availability. Based on this sensitivity, a framework for calibrating a water stress function in a crop growth model using ground-based SIF observations is proposed. SIF time series are simulated by coupling the AgroC crop growth model to the Soil Canopy Observations Photosynthesis Energy (SCOPE) model. This allowed parametrizing the water stress function in the AgroC crop growth model, resulting in improved estimates of actual evapotranspiration and net ecosystem exchange over a sugar beet stand during stressed periods. The improvement in the estimation of the water and carbon fluxes by AgroC during the summer months highlights the ability of canopyscale SIF observations to serve as a remote sensing metric to indicate the intensity of a stress condition. We argue that our framework, linking SIF emission to stress functions, can be used to extract information concerning drought stress from the Fluorescence Explorer (FLEX) satellite, scheduled for launch in 2024.
Why it matches plant phenotyping methods植物キャノピーの乾燥ストレス状態をSIF観測から推定し、作物モデルのストレス関数を較正する方法論が中心であり、単なる生理測定ではない。
abstracta framework for calibrating a water stress function in a crop growth model using ground-based SIF observations is proposed.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsリモートセンシングと深層学習により、植物バイオマスという明示的な形質を推定する方法が題名の中心であるため、植物フェノタイピング手法として含めます。
titlePredicting plant biomass and species richness in temperate grasslands across regions, time, and land management with remote sensing and deep learning
Solar-induced chlorophyll fluorescence (SIF) has been widely used to track vegetation photosynthesis at different scales ranging from in-situ measurements to satellite products. Airborne platforms sample SIF data at a spatial scale intermediate between in-situ and satellite, matching that of ground measurement (e.g. flux tower footprints and other field sampling), enabling us to explore causes of SIF variation and validate satellite-based SIF products. However, harmonizing SIF across sensors and platforms (correcting for systematic errors to yield a consistent, comparable SIF product) is challenging because SIF can be retrieved in different absorption windows, with different instruments and methods complicating the comparison between different observational levels (i.e., ground, airborne, satellites) and between sites equipped with different instruments with varying optical properties (spectral resolution and sampling intervals, spatial resolution). Additionally, the spatial and temporal variability of atmospheric properties can influence the retrieval of the weak SIF signal. Because of these complications, direct comparisons of airborne and ground SIF across scales are rarely attempted. In this study, we combined airborne SIF data with simultaneous ‘ground truth’ data collected by stationary and mobile platforms in a soybean field in Nebraska, USA. In this effort, we tested several SIF extraction methods, including Fraunhofer Line Discrimination (FLD), improved Fraunhofer Line Discrimination (iFLD), Spectral Fitting Method (SFM), SpecFit, and a Singular Vector Decomposition (SVD) method. The SpecFit method was sensitive to the 715–740 nm water bands and removing the water bands in the fitting process yielded better agreement between the airborne and ground SIF spectra. Accurate estimation of the ground level downwelling irradiance obtained by ground measurements over a calibration target improved agreement between airborne and ground SIF retrievals at the O₂A band, and allowed us to derive a SIF dataset with improved agreement across platforms and sampling scales. This experimental approach provided a method for generating comparable SIF signals across instruments, methods and platforms, which is critical to understanding the SIF-GPP relationship at different scales and to cross-validate the diversity of platforms used for satellite products calibration and validation.
Why it matches plant phenotyping methods植物の光合成状態を示すSIFについて、複数の抽出法・センサー・空間スケールを比較し、校正と検証を通じて互換性のある測定データを生成する方法が中心である。
abstractIn this study, we combined airborne SIF data with simultaneous ‘ground truth’ data collected by stationary and mobile platforms in a soybean field in Nebraska, USA.
In recent years, various techniques have been developed to generate crop-type maps based on remote sensing data. Wheat and barley are two major cereal crops cultivated as the first and fourth largest grain crops across the globe. The variations in spectral temporal profile of both crops are generally insignificant at small scales and therefore the two crops are phenologically fairly clearly separated; however, at large scale areas the variance of phenological parameters increases for both crops due to the effects of various climatic and orographic factors which adversely influences discrimination of wheat and barley. Additionally, wheat and barley are usually cultivated as both spring and winter or early and late season crops in some areas, making it more difficult to distinguish them. Therefore, developing a new method based on remote sensing data for effective discrimination of wheat and barley is an important necessity in the field of precision agriculture. To this end, this research presents a new phenology-based method to discriminate barley from wheat. In this study, Sentinel-2 (S2) time-series data of a study site in Iran (Markazi) and two sites in the USA (Idaho and North California), are employed. Spectral reflectance values of wheat and barley are examined during the growing season and a new spectral-temporal feature is successfully developed for automatic identification of the barley heading date. The Relief-f algorithm is then employed to select appropriate spectral features of S2 to distinguish wheat from barley at the heading date. Finally, generated spectral features at the heading date are used as input to Support Vector Machine (SVM) and Random Forest (RF) to produce barley and wheat maps. The Kappa coefficient and overall accuracy (OA) obtained for the three study sites are more than 0.67 and 76%, respectively. The findings of this study demonstrate the potential of remote sensing data to identify the phenological growth stages of barley and distinguish it successfully from wheat.
Why it matches plant phenotyping methodsSentinel-2時系列から作物の生育段階（barley heading date）を自動抽出する新しいスペクトル・時間特徴量を開発しており、単なる作物分類ではなく植物フェノタイプ取得手法が中心である。
abstracta new spectral-temporal feature is successfully developed for automatic identification of the barley heading date
The objective of this study is to evaluate the performances of a semi-empirical approach based on the Bayesian theory to retrieve Green Area Index (GAI) from multiple decametric satellites. It is designed to overcome some limitations in existing Radiative Transfer Model (RTM) inversion methods, including the high dimensionality of the inverse problem, the convergence problem due to possible equifinality, and the dependence of some RTM variables on the crop-specific architecture. The PROSAIL model is first inverted in a calibration step using the Hamiltonian Monte Carlo (HMC) algorithm over a global dataset of ground GAI measurements (for maize, wheat, and rice) and the corresponding reflectance observations from Landsat-8, Sentinel-2, and Quickbird to derive crop-specific distributions of PROSAIL input variables. These distributions were then used as prior information to predict GAI over an independent set of reflectance observations. Results show that the full Bayesian approach provides close estimates of GAI to ground truth, with respective Root Mean Square Error (RMSE) of 1.01, 1.33, and 0.97 for maize, wheat, and rice (R²=0.67, 0.76 and 0.63, respectively). The performances are better than those approaches generally reported using radiative transfer models that are non-crop-specific, like the SNAP algorithm for Sentinel-2, but are slightly behind the purely empirical models based on machine learning. However, the proposed approach provides an explicit insight of the joint distribution of PROSAIL variables that are valid for any satellite platform. This constitutes a major advantage against purely empirical models, as it enables to fully exploit large observational datasets from multiple sensors and generalize to other platforms.
Why it matches plant phenotyping methods衛星反射データから作物別GAIを推定するベイズ逆解析手法を開発・評価しており、植物形態形質の取得法が研究の中心である。
abstractThe objective of this study is to evaluate the performances of a semi-empirical approach based on the Bayesian theory to retrieve Green Area Index (GAI) from multiple decametric satellites.
Plant area density (PAD in m²·m⁻³) defines the total one-sided total plant surface area within a given volume. It is a key variable in characterizing exchange processes between the atmosphere and land surface. Terrestrial laser scanning (TLS) provides unprecedented detail of the 3D structure of forest canopies. Yet, signal occlusion and uneven sampling density of the TLS point clouds limit our capacity to characterize the 3D distribution of canopy components. Recent studies have made use of statistical estimators of PAD that are applied to TLS point clouds subdivided into three-dimensional (3D) cubes, or voxels. Computation of such metrics under actual field conditions with point clouds containing several millions of returns is challenging. Moreover, rigorous assessment of the estimated PAD and effects of occlusions in forests remain unclear due to laborious, time-consuming, and inaccurate field measurements. In the present study, we present L-Vox, a software that computes PAD per voxel for TLS scans acquired in forest environments, which is based upon recent development of unbiased estimators derived from maximum likelihood. Two applications are presented. First, the software is evaluated for virtual forest plots, which are detailed 3D models of individual trees with corresponding simulated TLS scans, for which reference data are known. Second, L-Vox is applied to actual scans that were acquired in hardwood and coniferous plots in New Brunswick and Newfoundland, Canada. Both test cases were used to investigate the effects of occlusion and the uneven sampling in estimating PAD. The test cases were also used to assess the influence of voxel size and the number of scans per plot on PAD estimates. Our results showed strong correlations between the estimated PAD profile from L-Vox and simulated PAD for virtual forest plots, with a mean R² = 0.98 and a mean coefficient of variation (CV) = 15.6%. We demonstrated that comparing multi-scan to single scan TLS acquisitions in real forest plots substantially reduced signal occlusion, resulting in an increase up to 50% in PAD values. Effects of voxel size on PAD estimates greatly depended upon the relative size of foliar and woody elements, with an optimal size around 10 cm in coniferous plots. L-Vox proved to be an efficient and accurate tool for computing 3D distributions of PAD from TLS measurements in natural forest environments.
Why it matches plant phenotyping methods森林キャノピーの植物面積密度という明示的な植物形質を、TLS点群から推定するソフトウェアを開発・検証しており、取得・抽出手法が研究の中心である。
abstractwe present L-Vox, a software that computes PAD per voxel for TLS scans acquired in forest environments
Solar-induced chlorophyll fluorescence (SIF) has been shown to be a novel proxy for terrestrial gross primary production (GPP). A growing number of ground-based automatic SIF observation systems equipped with hemispherical-conical and bi-hemispherical observation configurations have been developed in synergy with EC flux measurements across different ecosystems. However, the difference in the canopy SIF observed by these two types of configurations has not been well studied, which poses challenges in evaluating their performance in tracking GPP. In this study, we investigated SIF from both hemispherical-conical and bi-hemispherical observation configurations for their ability to track GPP in a maize field during the 2020 growth season. We found that bi-hemispherical SIF observations (SIFHₑₘᵢₛ) showed higher correlations with GPP at both diurnal and seasonal scales, and the superiority of SIFHₑₘᵢₛ for GPP estimation was also supported by Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model simulations. In addition, we found that the SIFHₑₘᵢₛ-GPP model established at a satellite overpass time (e.g., 09:30) outperformed the corresponding SIFNₐdᵢᵣ-GPP model in estimating both the half-hourly and daily GPP. The underlying mechanism for the advantage of this SIFHₑₘᵢₛ-GPP relationship was elucidated by a simplified geometrical optical model, which showed that the diurnal patterns of the observed sunlit and shaded leaves for the SIFHₑₘᵢₛ were consistent with those of the canopy GPP. Our study recommends a bi-hemispherical configuration setup for its superiority in monitoring GPP dynamics.
Why it matches plant phenotyping methodsキャノピー蛍光観測の2種類のセンサー構成を比較し、GPP推定性能を検証することが中心であり、植物生理状態の測定法として実質的な方法検証に該当する。
abstractwe investigated SIF from both hemispherical-conical and bi-hemispherical observation configurations for their ability to track GPP in a maize field
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods航空機ハイパースペクトル画像とSIFを用いてアーモンド葉の窒素含量を評価する、植物形質推定手法が題名上の中心であるため。
titleEvaluating the role of solar-induced fluorescence (SIF) and plant physiological traits for leaf nitrogen assessment in almond using airborne hyperspectral imagery
Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under shortterm, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.
Why it matches plant phenotyping methods作物ストレスに関する植物形質のリモートセンシング技術と検索・推定手法を体系的にレビューしており、植物フェノタイピング手法が中心である。
abstractthis study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture
Water plays a crucial role in maintaining plant functionality and drives many ecophysiological processes. The distribution of water resources is in a continuous change due to global warming affecting the productivity of ecosystems around the globe, but there is a lack of non-destructive methods capable of continuous monitoring of plant and leaf water content that would help us in understanding the consequences of the redistribution of water. We studied the utilization of novel small hyperspectral sensors in the 1350–1650 nm and 2000–2450 nm spectral ranges in non-destructive estimation of leaf water content in laboratory and field conditions. We found that the sensors captured up to 96% of the variation in equivalent water thickness (EWT, g/m²) and up to 90% of the variation in relative water content (RWC). Further tests were done with an indoor plant (Dracaena marginate Lem.) by continuously measuring leaf spectra while drought conditions developed, which revealed detailed diurnal dynamics of leaf water content. The laboratory findings were supported by field measurements, where repeated leaf spectra measurements were in fair agreement (R² = 0.70) with RWC and showed similar diurnal dynamics. The estimation of leaf mass per area (LMA) using leaf spectra was investigated as a pathway to improved RWC estimation, but no significant improvement was found. We conclude that close-range hyperspectral spectroscopy can provide a novel tool for continuous measurement of leaf water content at the single leaf level and help us to better understand plant responses to varying environmental conditions.
Why it matches plant phenotyping methods近接ハイパースペクトルセンサーにより葉の含水量を非破壊・連続推定する手法の開発と検証が中心であり、植物の生理形質を直接測定している。
abstractWe studied the utilization of novel small hyperspectral sensors in the 1350–1650 nm and 2000–2450 nm spectral ranges in non-destructive estimation of leaf water content in laboratory and field conditions.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsSARと光学時系列を深層統合して作物の生育フェノロジーを追跡する手法が題名上の中心であり、植物状態の推定に該当する。
titleSpatial-aware SAR-optical time-series deep integration for crop phenology tracking
Leaf area index (LAI) is a terrestrial essential climate variable that is required in a variety of ecosystem and climate models. The Global LAnd Surface Satellite (GLASS) LAI product has been widely used, but its current version (V5) from Moderate Resolution Imaging Spectroradiometer (MODIS) data has several limitations, such as frequent temporal fluctuation, large data gaps, high dependence on the quality of surface reflectance, and low computational efficiency. To address these issues, this paper presents a deep learning model to generate a new version of the LAI product (V6) at 250-m resolution from MODIS data from 2000 onward. Unlike most existing algorithms that estimate one LAI value at one time for each pixel, this model estimates LAI for 2 years simultaneously. Three widely used LAI products (MODIS C6, GLASS V5, and PROBA-V V1) are used to generate global representative time-series LAI training samples using K-means clustering analysis and least difference criteria. We explore four machine learning models, the general regression neural network (GRNN), long short-term memory (LSTM), gated recurrent unit (GRU), and Bidirectional LSTM (Bi-LSTM), and identify Bi-LSTM as the best model for product generation. This new product is directly validated using 79 high-resolution LAI reference maps from three in situ observation networks. The results show that GLASS V6 LAI achieves higher accuracy, with a root mean square (RMSE) of 0.92 at 250 m and 0.86 at 500 m, while the RMSE is 0.98 for PROBA-V at 300 m, 1.08 for GLASS V5, and 0.95 for MODIS C6 both at 500 m. Spatial and temporal consistency analyses also demonstrate that the GLASS V6 LAI product is more spatiotemporally continuous and has higher quality in terms of presenting more realistic temporal LAI dynamics when the surface reflectance is absent for a long period owing to persistent cloud/aerosol contaminations. The results indicate that the new Bi-LSTM deep learning model runs significantly faster than the GLASS V5 algorithm, avoids the reconstruction of surface reflectance data, and is resistant to the noises (cloud and snow contamination) or missing values contained in surface reflectance than other methods, as the Bi-LSTM can effectively extract information across the entire time series of surface reflectance rather than a single time point. To our knowledge, this is the first global time-series LAI product at the 250-m spatial resolution that is freely available to the public (www.geodata.cn and www.glass.umd.edu).
Why it matches plant phenotyping methodsMODIS時系列から植物キャノピー形質であるLAIを推定するBi-LSTM手法と250 mプロダクトを開発し、現地参照マップで直接検証しているため、フェノタイピング手法が中心である。
abstractthis paper presents a deep learning model to generate a new version of the LAI product (V6) at 250-m resolution from MODIS data from 2000 onward.
Timely monitoring of above-ground biomass (AGB) is essential for indicating the crop growth status and predicting grain yield and carbon dynamics. Non-destructive remote sensing techniques with a large spatial coverage have become a promising method for crop biomass monitoring. However, most existing crop biomass models have only been tested at a single growth stage or only at a small number of growth stages at a single location. This has limited the ability of these AGB models to be transferred spatially, to other fields or regions, to predict AGB at any growth stage during the season, or to be potentially used with data from other sensing systems. Here, a new crop biomass algorithm (CBA-Wheat) was developed to estimate AGB over the entire growing season. It uses information on the crop growth stage, based on phenological scale observations (Zadoks scale or ZS), the day of the year or thermal indices (growing degree days), to correct AGB estimations from remotely sensed vegetation indices. The model transferability was evaluated across multiple regional test sites and different data sources (UAV and hand-held spectroscopic data). Results showed that the coefficient values [slope (k) and intercept (b)] of ordinary least squares regression (OLSR) of AGB with vegetation indices had a strong relationship with ZS. These k and b relationships were used to correct the OLSR model parameters based on the observed phenological stage (ZS value). The two-band enhanced vegetation index (EVI2) was the best vegetation index for predicting AGB with the new CBA-WheatZS model, with R² and RMSE values of 0.83 and 2.07 t/ha for an experimental trial site, 0.78 and 2.05 t/ha for multiple independent regional test sites, and 0.69 and 1.87 t/ha when transferred to EVI2 derived from UAV. Model performance was lower with the day of the year and thermal index corrections; however, the use of relative growing degree-days (RGS; CBA-WheatRGS), instead of ZS information, to adjust the model parameters showed a high consistency with the CBA-WheatZS model, and a good potential for estimation of AGB at regional scales without the need for local phenological observations. The CBA-WheatRGS had validated R² and RMSE values of 0.82 and 2.01 t/ha for the experimental trial site, 0.76 and 2.39 t/ha for multiple independent regional test sites, and 0.66 and 2.14 t/ha for UAV hyperspectral imagery. These results demonstrated a good potential to estimate biomass from remotely sensed imagery at varying spatio-temporal scales in winter wheat.
Why it matches plant phenotyping methods小麦の地上部バイオマスをリモートセンシングから推定する新規アルゴリズムを開発し、複数地域・データソースで転移性を検証しており、植物形質取得法が研究の中心である。
abstractHere, a new crop biomass algorithm (CBA-Wheat) was developed to estimate AGB over the entire growing season.
In-season prediction of crop yield is a topic of research studied by several scientists using different methods. Seasonal forecasts provide critical insights to different stakeholders who use the information for strategic and tactical decisions. In this study, we propose a novel scalable method to forecast in season subfield crop yield through a machine learning model based on remotely sensed imagery and data from a process-based crop model on a cumulative crop drought index (CDI) designed to capture the impact of in-season crop water deficit on crops. To evaluate the performance of our proposed model, we used 352 growers' fields of different sizes across the states of Michigan, Indiana, Iowa, and Illinois, with 2520 respective yield maps generated by combine harvesters equipped with precise high-resolution yield monitor sensor, over multiple years (from 2006 up to 2019). We obtained high resolution digital elevation model, climate, and soil data to execute the SALUS model, a process-based crop model, to calculate the CDI for each field used in the study. We used Landsat Analysis Ready Dataset (ARD) products generated by USGS as image source to calculate the green chlorophyll vegetation index (GCVI). We found that the inclusion of the CDI in remote sensing-based random forest models substantially improved in-season subfield corn yield prediction. The addition of the CDI in the yield prediction model showed that the greatest improvements in predictions were observed in the driest year (2012) in our case study. The proposed approach also showed that the subfield spatial variations of corn yield are better captured with the inclusion of CDI for most fields. The earliest prediction in the growing season with GCVI and CDI together outperformed the latest prediction with GCVI alone, highlighting the potential of CDI for predicting spatial variability of maize yield around grain filling period, which is on average close to two months before typical crop harvest in the US Midwest.
Why it matches plant phenotyping methodsリモートセンシング画像と機械学習を用いて、圃場内のトウモロコシ収量という植物形質を予測する手法を提案・評価しており、形質推定法が研究の中心である。
abstractwe propose a novel scalable method to forecast in season subfield crop yield through a machine learning model based on remotely sensed imagery and data from a process-based crop model
Solar-induced chlorophyll fluorescence (SIF) can be used as an indicator of crop photosynthetic activity and a proxy for vegetation stress in plant phenotyping and precision agriculture applications. SIF quantification is sensitive to the spectral resolution (SR), and its accurate retrieval requires sensors with sub-nanometer resolutions. However, for accurate SIF quantification from imaging sensors onboard airborne platforms, sub-nanometer imagers are costly and more difficult to operate than the commonly available narrow-band imagers (i.e., 4- to 6-nm bandwidths), which can also be installed on drones and lightweight aircraft. Although a few theoretical and experimental studies have evaluated narrow-band spectra for SIF quantification, there is a lack of research focused on comparing the effects of the SR on SIF from airborne hyperspectral imagers in practical applications. This study investigates the effects of SR and sensor altitude on SIF accuracy, comparing SIF quantified at the 760-nm O₂-A band (SIF₇₆₀) from two hyperspectral imagers with different spectral configurations (full width at half-maximum resolutions of 0.1–0.2 nm and 5.8 nm) flown in tandem on board an aircraft. SIF₇₆₀ retrievals were compared from two different wheat and maize phenotyping trials grown under different nitrogen fertilizer application rates over the 2019–2021 growing seasons. SIF₇₆₀ from the two sensors were correlated (R² = 0.77–0.9, p < 0.01), with the narrow-band imager producing larger SIF₇₆₀ estimates than the sub-nanometer imager (root mean square error (RMSE) 3.28–4.69 mW/m²/nm/sr). Ground-level SIF₇₆₀ showed strong relationships with both sub-nanometer (R² = 0.90, p < 0.001, RMSE = 0.07 mW/m²/nm/sr) and narrow-band (R² = 0.88, p < 0.001, RMSE = 3.26 mW/m²/nm/sr) airborne retrievals. Simulation-based assessments of SIF₇₆₀ for SRs ranging from 1 to 5.8 nm using the SCOPE model were consistent with experimental results showing significant relationships among SIF₇₆₀ quantified at different SRs. Predictive algorithms of leaf nitrogen concentration using SIF₇₆₀ from either the narrow-band or sub-nanometer sensor yielded similar performance, supporting the use of narrow-band resolution imagery for assessing the spatial variability of SIF in plant phenotyping, vegetation stress detection and precision agriculture contexts.
Why it matches plant phenotyping methods航空ハイパースペクトル画像によるSIF定量法について、異なる分光分解能と高度の影響を比較・検証しており、植物フェノタイピング用のセンサー手法が中心である。
abstractThis study investigates the effects of SR and sensor altitude on SIF accuracy, comparing SIF quantified at the 760-nm O₂-A band (SIF₇₆₀) from two hyperspectral imagers with different spectral configurations
The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically- based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C) top-of-atmosphere (TOA) reflectance data into crop traits. To achieve this, a training dataset was generated using the leaf-canopy RTM PROSAIL in combination with the atmospheric model 6SV. Gaussian process regression (GPR) retrieval models were then established for eight essential crop traits namely leaf chlorophyll content, leaf water content, leaf dry matter content, fractional vegetation cover, leaf area index (LAI), and upscaled leaf variables (i.e., canopy chlorophyll content, canopy water content and canopy dry matter content). An important pre-requisite for implementation into GEE is that the models are sufficiently light in order to facilitate efficient and fast processing. Successful reduction of the training dataset by 78% was achieved using the active learning technique Euclidean distance-based diversity (EBD). With the EBD-GPR models, highly accurate validation results of LAI and upscaled leaf variables were obtained against in situ field data from the validation study site Munich-North-Isar (MNI), with normalized root mean square errors (NRMSE) from 6% to 13%. Using an independent validation dataset of similar crop types (Italian Grosseto test site), the retrieval models showed moderate to good performances for canopy-level variables, with NRMSE ranging from 14% to 50%, but failed for the leaf-level estimates. Obtained maps over the MNI site were further compared against Sentinel-2 Level 2 Prototype Processor (SL2P) vegetation estimates generated from the ESA Sentinels' Application Platform (SNAP) Biophysical Processor, proving high consistency of both retrievals ( R 2 from 0.80 to 0.94). Finally, thanks to the seamless GEE processing capability, the TOA-based mapping was applied over the entirety of Germany at 20 m spatial resolution including information about prediction uncertainty. The obtained maps provided confidence of the developed EBD-GPR retrieval models for integration in the GEE framework and national scale mapping from S2-L1C imagery. In summary, the proposed retrieval workflow demonstrates the possibility of routine processing of S2 TOA data into crop traits maps at any place on Earth as required for operational agricultural applications.
Why it matches plant phenotyping methods衛星データから作物形質を推定するGPR retrievalモデルと、GEE上での実装・検証ワークフローが研究の中心であり、植物形質フェノタイピング手法に該当する。
abstractwe implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C) top-of-atmosphere (TOA) reflectance data into crop traits.
NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
Why it matches plant phenotyping methodsGEDI波形ライダーから森林の地上部バイオマス密度という植物・林冠形質を推定するモデルを開発し、モデル選択と性能評価を行った研究であり、形質取得手法が中心的です。
abstractThis paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection.
Lespedeza cuneata (sericea lespedeza; hereafter “sericea”) is an invasive species brought to the U.S. from East Asia in the 1890s to be used as forage. However, it has now become a growing ecological and economic threat in grasslands of several states in the U.S. southern Great Plains including Oklahoma, Kansas, Missouri, and Nebraska. Here, we demonstrate the capability of airborne imaging spectroscopy to map sericea in a large natural grassland within the Tallgrass Prairie Preserve, the largest protected tallgrass prairie in the world, located in northeastern Oklahoma. Through this research, we investigated which remotely observable vegetation functional traits (referring to biochemical, physiological, and structural traits) contribute to distinguishing sericea from co-occurring native species and whether we can detect sericea remotely through quantifying these functional traits using imaging spectroscopic data (also known as hyperspectral data). To achieve these objectives, full-range airborne hyperspectral data with spatial resolution of 1 m were collected from the study area in August 2020. In addition, a total of 12 vegetation functional traits were measured through field sampling for model development. We first identified functional traits that contributed to separating sericea from other species, and then used them in a classification model to detect sericea in our study site. We found total carotenoids (sum of neoxanthin, violaxanthin, antheraxanthin, zeaxanthin, and lutein), chlorophyll a + b (sum of chlorophyll a and chlorophyll b), total nitrogen, canopy height, potassium, and magnesium as the main functional traits contributing to the detection of sericea; an overall classification accuracy of approximately 94% was reported. However, the proposed approach overestimated sericea cover in species-rich plant communities. Overall, our findings demonstrated an essential role for airborne remote sensing in 1) direct mapping of invasive plants and 2) quantifying functional traits associated with success strategies of invasive species. Eventually, experiments like ours can aid in developing large-scale and science-driven management practices to both identify the current extent, and to control the spread of invasive species in grasslands and similar short-stature environments. This will not only improve management practices but will have major societal and economic benefits.
Why it matches plant phenotyping methods航空ハイパースペクトル画像から植物の機能形質（色素、窒素、草冠高など）を定量し、分類モデルで侵入植物を検出する手法が研究の中心であるため。
abstractwhether we can detect sericea remotely through quantifying these functional traits using imaging spectroscopic data
Accurate estimation of leaf nitrogen concentration (LNC) is critical to characterize ecosystem and plant physiological processes for example in carbon fixation. Remote sensing can capture LNC, while interrelated traits and spectral diversity across plant species prevent development of transferable LNC assessment models based on leaf reflectance. Here, we developed a new transfer learning method by coupling transfer component analysis with the support vector regression, namely TCA-SVR, to transfer LNC assessment models across different plant species. We benchmarked the performance of TCA-SVR against a well-established partial least squares regression (PLSR) model with five remote sensing datasets on 60 plant species measured from three spectroradiometers with varied spectral resolutions and illumination and viewing angles. The result showed that leaf reflectance presented the high spectral diversity in different spectral regions, plant species, and growth stages. The combination of visible (VIS), near infrared (NIR), and shortwave infrared (SWIR) reflectance (e.g. 550–2300 nm) achieved the optimal LNC assessment across all datasets. Results on the testing datasets showed that the transferability of the PLSR models highly depended on the LNC distribution and spectral features, which were associated with the differences in plant species, spectral measurements, and growth conditions between datasets. These differences led to the large variations in LNC and leaf reflectance, which thus produced the overestimations and underestimations of LNC. Compared to the PLSR model, TCA-SVR greatly improved the transferability of the LNC assessment model by reducing the average root mean square error by 36.76%. Further, the implementation of modeling updating can help TCA-SVR learn the features related to the difference in plant species and LNC ranges by transferring samples from the target dataset to the source dataset. Our model updating approach improved the performance of TCA-SVR and only needed 5% of the off-site samples to supplement the source dataset to achieve an effective assessment of LNC. Refining the proposed method with new remote sensing datasets will aid rapid monitoring of plant nitrogen status and may improve carbon‑nitrogen interactions in existing ecosystem models.
Why it matches plant phenotyping methods葉の窒素濃度という植物形質を対象に、ハイパースペクトル計測と転移学習モデルを開発し、複数データセットで比較・検証しており、フェノタイピング手法が中心である。
abstractHere, we developed a new transfer learning method by coupling transfer component analysis with the support vector regression, namely TCA-SVR, to transfer LNC assessment models across different plant species.
Field spectroscopy is a powerful tool for monitoring leaf functional traits in situ, but it remains unclear whether universal statistical models can be developed to predict traits from spectral information, or whether re-calibration is necessary as conditions vary. In particular, multiple leaf traits vary simultaneously across growing seasons, and it is an open question whether these temporal changes can be predicted successfully from hyperspectral data. To explore this question, monthly changes in 21 physiochemical leaf traits and plant spectra were measured for eight deciduous tree species from the UK. Partial least-squares regression (PLSR) was used to evaluate whether each trait could be predicted from a single PLSR model from reflectance spectra, or whether species- and month-level models were needed. Physiochemical traits and spectra varied greatly over the growing season, although there was less variation among mature leaves harvested between June and September. Importantly, leaf spectroscopy was able to predict seasonal variations of most leaf traits accurately, with accuracies of prediction generally higher for mature leaves. However, for several traits, the PLSR estimation models varied among species, and a single PLSR model could not be used to make accurate species-level predictions. Our findings demonstrate that leaf spectra can successfully predict multiple functional foliar traits through the growing season, establishing one of the fundamentals for monitoring and mapping plant functional diversity in temperate forests from air- and spaceborne imaging spectroscopy.
Why it matches plant phenotyping methods葉の機能形質をハイパースペクトル反射から推定するPLSRモデルを、種・季節間で評価しており、形質取得手法の検証が研究の中心である。
abstractPartial least-squares regression (PLSR) was used to evaluate whether each trait could be predicted from a single PLSR model from reflectance spectra, or whether species- and month-level models were needed.
Soybean is among the most important crops for food and feed production worldwide. Sustainable and local production in regions with marginal climates requires cold-adapted varieties that create high yield and protein content in a short vegetation period. Drone-based high-throughput field phenotyping methods allow monitoring the success and the developmental speed of genotypes in such target environments. This study exemplifies that such frequent and precise analyses of remotely sensed canopy growth traits can be used to derive the optimal genotype, a so-called ideotype, for a given mega-environment. For the case example of Switzerland, a country with a temperate oceanic climate, the results indicate that image-derived traits allow predicting yield and protein content from the dynamics of vegetative growth. Genotypes with early canopy cover produce high yield, whereas genotypes that show a prolonged duration until they have reached their final maximum of leaf area index are characterized by a high protein content. Analyses of early performance trial stage material indicate that there are genotypes that combine both features of growth dynamics. Whether these genotypes are then indeed successful in breeding programs remains to be investigated, since this also depends on disease resistance and other traits of those genotypes. Yet, overall, this study provides strong indications of the high value of high-throughput field phenotyping in the context of physiological and breeding-related analyses of crops.
Why it matches plant phenotyping methodsドローン画像による高スループット圃場フェノタイピングと、キャノピー成長形質の抽出・収量およびタンパク質含量の予測が研究の中心であるため。
abstractDrone-based high-throughput field phenotyping methods allow monitoring the success and the developmental speed of genotypes in such target environments.
Despite its critical importance to carbon storage modeling, forest vertical structure remains poorly characterized over large areas. Canopy height estimates from current satellite missions such as ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) offer promise to close this knowledge gap, but their validation is critically important to inform their measurement uncertainties and scientific utility. Using existing airborne laser scanning (ALS) data, the agreement of a variety of terrain and aboveground canopy height metrics including summary height statistics and percentiles, from ICESat-2’ Land, Water and Vegetation Elevation product (ATL08) product was assessed in 12 sites across six major biomes in the United States. The agreement between ATL08 and ALS heights was assessed using the mean bias (Bias, ATL08 – ALS), the mean absolute error (MAE) and their percent equivalents, percent bias (pBias) and percent MAE (pMAE), respectively. In general, the agreement between ATL08 and ALS terrain heights was high (Bias 0.18 m, pBias 0.1%) while canopy heights showed lower agreement (Bias −1.71 m, pBias −15.9%). Analyses by biome, time of acquisition and beam strength of the ICESat-2 photon data also showed generally higher agreement for ATL08 terrain than canopy heights. Analyses also showed the performance of ATL08 heights varied with canopy cover with ATL08 terrain heights showing the best agreement when canopy cover was between 40 and 70% while the best performance for ATL08 canopy heights was observed when canopy cover was greater than 80%. This observation, coupled with analyses by biome, indicate that ATL08 canopy heights are more suitable in relatively dense canopy environments such as conifer and broadleaf forests than relatively sparse environments such a temperate grassland and Savannas. Higher level canopy height percentiles (95th and 98th) showed higher agreement (mean Bias −12.5%) with ALS heights than lower percentiles (minimum, 25th, mean pBias ~39.2%). These findings indicate that ATL08 canopy heights show more promise for routine canopy height characterization using the 95th and 98% percentiles but is limited in characterizing intermediate vertical structure. The observed performance differences between ATL08 terrain and canopy heights are attributed to differences in photon sampling rates over terrain and canopy surfaces which, compounded with background noise in ICESat-2 photon data, led to different effectiveness for ATL08 processing routines in filtering terrain and off-terrain points. This assessment of the impact of a variety of factors provides the vegetation community with an understanding of the capabilities and limitations of height estimates from the ICESat-2 ATL08 product.
Why it matches plant phenotyping methodsICESat-2による森林キャノピー高測定を航空レーザー測量と比較・検証し、バイアスや誤差、条件別性能を評価しているため、植物形態形質の測定法の技術的検証が中心です。
abstracttheir validation is critically important to inform their measurement uncertainties and scientific utility
Soybean is among the most important crops for food and feed production worldwide. Sustainable and local production in regions with marginal climates requires cold-adapted varieties that create high yield and protein content in a short vegetation period. Drone-based high-throughput field phenotyping methods allow monitoring the success and the developmental speed of genotypes in such target environments. This study exemplifies that such frequent and precise analyses of remotely sensed canopy growth traits can be used to derive the optimal genotype, a so-called ideotype, for a given mega-environment. For the case example of Switzerland, a country with a temperate oceanic climate, the results indicate that image-derived traits allow predicting yield and protein content from the dynamics of vegetative growth. Genotypes with early canopy cover produce high yield, whereas genotypes that show a prolonged duration until they have reached their final maximum of leaf area index are characterized by a high protein content. Analyses of early performance trial stage material indicate that there are genotypes that combine both features of growth dynamics. Whether these genotypes are then indeed successful in breeding programs remains to be investigated, since this also depends on disease resistance and other traits of those genotypes. Yet, overall, this study provides strong indications of the high value of high-throughput field phenotyping in the context of physiological and breeding-related analyses of crops.
Why it matches plant phenotyping methodsドローンによる高スループット圃場フェノタイピングと画像由来キャノピー形質の反復・精密推定が研究の中心であり、遺伝子型評価や収量・タンパク質予測に応用している。
abstractDrone-based high-throughput field phenotyping methods allow monitoring the success and the developmental speed of genotypes in such target environments.
Hyperspectral airborne imagery can provide rich information on plant physiological and structural properties at a scale intermediate to that of proximal and satellite remote sensing and has broad applications in assessing ecosystem function and biodiversity. A key processing step of airborne hyperspectral data is the atmospheric correction that compensates for path radiance, aerosol effects and gas absorption to derive an accurate surface reflectance that can be compared across time and space. In practice, routine correction procedures are often customized for various platforms without fully reporting or checking the errors systematically in the atmospheric correction. Such errors can have significant effects on downstream analyses such as vegetation indices or trait retrievals, and not all subsequent analyses are equally affected by the accuracy of reflectance retrievals. In this study, we examined the errors in three types of atmospheric correction methods including a radiative transfer model (RTM), empirical line correction (ELC) and a hybrid method that combines elements of the two via Bayesian inference. Our results revealed that the individual correction methods had different effects on the reflectance retrievals that impacted downstream measurements. Including spectral measurements from ground vegetation targets in addition to painted calibration targets improved the performance of the ELC method. The hybrid method yielded reflectance spectra that most closely matched the spectra of the ground validation data. The errors in vegetation indices differed with the methods, and certain indices (such as PRI) were more affected than indices that rely on stable, broader spectral features (e.g., NDVI). Plant pigment retrievals via partial least squares regression were less sensitive to errors in atmospheric correction. These findings demonstrate that obtaining high-quality, field spectral measurements over well-characterized calibration targets and representative land cover types within the scene is critical for accurate surface reflectance and subsequent downstream products, such as vegetation indices or plant traits.
Why it matches plant phenotyping methods航空ハイパースペクトル画像の大気補正手法を比較・検証し、反射率、植生指数、色素推定などの植物形質への影響を評価しており、植物形質取得の技術的性能が中心である。
abstractIn this study, we examined the errors in three types of atmospheric correction methods including a radiative transfer model (RTM), empirical line correction (ELC) and a hybrid method that combines elements of the two via Bayesian inference.
Research has demonstrated the utility of digital aerial photogrammetry (DAP) for area-based predictions of forest inventory attributes. To date, studies have used DAP data acquired with a range of spatial resolutions and image overlaps. The systematic benchmarking of DAP acquisition parameters remains an outstanding research and operational gap for forest applications. While the impact of along-track overlap on point cloud metrics and area-based attribute estimates can be readily simulated, the impact of image resolution or across-track overlap requires purpose-acquired data. Moreover, although increases in along-track overlap are enabled by digital camera systems, costs for increasing across-track overlap can be substantial and may negate the cost-effectiveness of DAP for forest inventory. Hence, determining the impacts of varying acquisition parameters is of practical value for inventory programs. Researchers and practitioners have often assumed that more overlap will result in better DAP data, and that minimal overlaps often associated with historic airborne image campaigns are inadequate to support DAP processing. In our study, we found no marked difference among 15 and 20 cm spatial resolutions and overlap scenarios unless across-track overlap was reduced to 40%. Mean differences between DAP metrics and the ALS reference generally increased with decreasing overlap, and mean differences were larger for lower height percentiles (p10). Estimates of canopy height using the p90 metric varied by a root mean squared difference (RMSD) of approximately 5% between 15 cm and 20 cm datasets when along-track overlap was greater than 40%. Lower height percentiles were more strongly impacted by overlaps and resolution. Cover metrics varied by 2% RMSD across all overlap scenarios and resolutions. Comparisons between forest types (conifer, deciduous, mixed), terrain slope and aspect, and ALS-derived canopy cover were conducted to determine whether significant mean differences existed between DAP and the ALS reference. Although some significant differences were found by forest type and terrain variables, significant differences were most commonly associated with canopy cover. Based on the results reported herein, along and across-track overlaps ≥ 60% result in DAP metrics that were more similar to ALS. Increasing across-track overlap from 60% to 80% did not consistently improve the level of agreement between DAP metrics and ALS reference metrics. Conversely, DAP metrics generated using across-track overlaps <60% resulted in metrics with greater differences from the ALS reference, and a greater range of variability in DAP metric values. Image acquisitions for forest inventory must consider a broad range of factors and herein we have quantified that increasing along- or across-track overlap beyond 60% does not improve agreement with ALS area-based point cloud metrics commonly used to model forest inventory attributes. Likewise, overlap that is <60% does result in greater differences with ALS reference. Other applications beyond forest inventory may have different overlap requirements.
Why it matches plant phenotyping methods森林キャノピーの高さ・被覆など植物群落形質を対象に、航空写真測量の取得条件を体系的にベンチマークし、ALS基準との一致度を検証しているため、測定法の技術評価が中心である。
abstractThe systematic benchmarking of DAP acquisition parameters remains an outstanding research and operational gap for forest applications.
Indonesia recently implemented a novel, technology-driven approach for conducting agricultural production surveys, which involves monthly observations at many thousands of strategic locations and automated data logging via a cellular phone application. Data from these comprehensive field surveys offer immense value for advancing remote sensing technology to map crop production across Indonesia, particularly through the development of machine learning approaches to relate survey data with satellite imagery. The objective of this study was to compare different machine learning scenarios for classifying and mapping the temporal progression of paddy rice production stages across West Java, Indonesia using synthetic aperture radar (SAR) and optical remote sensing data from Sentinel-1 and Sentinel-2 satellites. Monthly paddy rice survey data at 21,696 locations across West Java from November 2018 through April 2019 were used for model training and testing. Five classes related to rice production stage or other field conditions were defined, including rice at tillering, heading, and harvest stages, rice fields with little to no vegetation present, and non-rice areas. A recurrent neural network (RNN) with long short term memory (LSTM) nodes provided optimal performance with classification accuracies of 79.6% and 75.9% for model training and testing, respectively, and reduced computational effort. Other approaches that incorporated a convolutional neural network (CNN) either reduced classification accuracy or increased computational effort. Deep machine learning methods (RNN and CNN) generally outperformed other non-deep classifiers, which achieved up to 63.3% accuracy for model testing. Classification accuracies were optimized by inputting two Sentinel-1 channels (VH and VV polarizations) and ten Sentinel-2 channels. Temporal patterns of paddy rice production stages were consistent between the monthly ground-based agricultural survey data and 10-m, satellite-based rice classification maps obtained by applying the LSTM-based RNN across West Java. The results demonstrated the value of combining modern agricultural survey data, satellite remote sensing, and a recurrent neural network to develop multitemporal maps of paddy rice production stages.
Why it matches plant phenotyping methods衛星センサーと深層学習を組み合わせ、イネの生育段階を広域で分類・推定する手法の比較と検証が研究の中心であり、植物の状態を直接推定している。
abstractThe objective of this study was to compare different machine learning scenarios for classifying and mapping the temporal progression of paddy rice production stages across West Java, Indonesia using synthetic aperture radar (SAR) and optical remote sensing data from Sentinel-1 and Sentinel-2 satellites.
The measurement of leaf optical properties (LOP) using reflectance and scattering properties of light allows a continuous, time-resolved, and rapid characterization of many species traits including water status, chemical composition, and leaf structure. Variation in trait values expressed by individuals result from a combination of biological and environmental variations. Such species trait variations are increasingly recognized as drivers and responses of biodiversity and ecosystem properties. However, little has been done to comprehensively characterize or monitor such variation using leaf reflectance, where emphasis is more often on species average values. Furthermore, although a variety of platforms and protocols exist for the estimation of leaf reflectance, there is neither a standard method, nor a best practise of treating measurement uncertainty which has yet been collectively adopted. In this study, we investigate what level of uncertainty can be accepted when measuring leaf reflectance while ensuring the detection of species trait variation at several levels: within individuals, over time, between individuals, and between populations. As a study species, we use an economically and ecologically important dominant European tree species, namely Fagus sylvatica. We first use fabrics as standard material to quantify measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation. We then quantify spectrally resolved variation in reflectance from F. sylvatica leaves. We show that the measurement uncertainty associated with leaf reflectance, estimated using a field spectroradiometer with attached leaf clip, represents on average a small portion of the spectral variation within a single individual sampled over one growing season (2.7 ± 1.7%), or between individuals sampled over one week (1.5 ± 1.3% or 3.4 ± 1.7%, respectively) in a set of monitored F. sylvatica trees located in Swiss and French forests. In all forests, the spectral variation between individuals exceeded the spectral variation of a single individual at the time of the measurement. However, measurements of variation within individuals at different canopy positions over time indicate that sampling design (e.g., standardized sampling, and sample size) strongly impacts our ability to measure between-individual variation. We suggest best practice approaches toward a standardized protocol to allow for rigorous quantification of species trait variation using leaf reflectance.
Why it matches plant phenotyping methods葉反射計測の不確実性を定量化し、測定誤差と個体間・個体内の形質変異を比較するとともに、標準化プロトコルのベストプラクティスを提案しており、植物表現型取得法が中心である。
abstractWe first use fabrics as standard material to quantify measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation.
With the advent of the next generation of space-based laser altimeters, ICESat-2 and GEDI, we are entering an exciting era of active remote sensing of forests that offers unprecedented opportunities for the observation of forest structure. Consistent comparisons of the accuracy of terrain and canopy height retrievals for these two missions are essential for continued improvement and further application. Because the time interval between the spaceborne products and validation data may introduce additional errors, we validate the newly released GEDI L2A product (version 2) and the ICESat-2 ATL08 product (version 4) using high-resolution, locally calibrated airborne lidar products acquired in the same year (2019) as the reference datasets. In addition, our study area contains 40 sites located in the U.S. mainland, Alaska, and Hawaii that encompass a variety of eco-climatic zones and vegetation cover types; thus, it avoids the uncertainties associated with small sample sizes and restricted spatial coverage. The results show that ICESat-2 and GEDI yield reasonable estimates of terrain height, with root mean squared errors (RMSEs) of 2.24 and 4.03 m for mid and low latitudes, respectively, and 0.98 m for high latitudes (ICESat-2 only). ICESat-2 outperforms GEDI across the board for terrain height retrieval, although they both have better accuracy than existing SRTM and GMTED DEM products. Analyses of the error factors suggest that steep slopes (>30°) present the greatest challenge for both GEDI and ICESat-2; in addition, tall (>20 m) and dense canopies (>90%) forest ecosystems also reduce the accuracy of the terrain height estimates. When ICESat-2 and GEDI data are used for canopy height retrieval, the use of only strong/power beam data acquired at night is recommended, as the overall RMSEs decrease from 7.21 and 5.02 m to 3.93 and 3.56 m, respectively, compared to using all data regardless of daytime and beam strength. GEDI outperforms ICESat-2 across the board for canopy height retrieval, as ICESat-2 has a larger potential bias for almost all forest types and cover conditions. ICESat-2 tends to overestimate the canopy height of dwarf shrublands and underestimate the canopy height of forest, and there is a gradual downward shift in the distribution of residuals with increasing canopy height. Overall, ICESat-2 with photon counting technology and GEDI with full waveform technology each represent the state of the art in spaceborne laser altimeters for terrain and canopy height retrieval. Combined, these two missions can take advantage of the unique strengths of each instrument.
Why it matches plant phenotyping methodsGEDIとICESat-2による森林キャノピー高の推定精度を航空LiDARで検証・比較しており、植物構造形質の取得手法の技術評価が研究の中心である。
abstractConsistent comparisons of the accuracy of terrain and canopy height retrievals for these two missions are essential for continued improvement and further application.
Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction (fesc) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine fesc of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy (SIF760canopy) to leaf level (SIF760leaf). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine fesc once a day to reliably scale SIF₇₆₀ from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating fesc for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF₇₆₀ to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency (εSIF) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency.
Why it matches plant phenotyping methods航空機イメージング分光計による作物のSIFを葉レベルへダウンスケールし、光合成効率に関連する生理形質を推定する手法を技術的に解析・適用しており、フェノタイピング手法が中心である。
abstractThis unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy (SIF760canopy) to leaf level (SIF760leaf).
Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction ( f esc ) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine f esc of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy ( SIF 760 canopy ) to leaf level ( SIF 760 leaf ). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine f esc once a day to reliably scale SIF 760 from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating f esc for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF 760 to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency ( ε SIF ) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency.
Why it matches plant phenotyping methods航空イメージング分光計と補正手法を用いて、作物キャノピーのSIFを葉レベルへダウンスケーリングし、光合成効率を推定する方法が研究の中心である。
abstractThis unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy ( SIF 760 canopy ) to leaf level ( SIF 760 leaf ).
Research has demonstrated the utility of digital aerial photogrammetry (DAP) for area-based predictions of forest inventory attributes. To date, studies have used DAP data acquired with a range of spatial resolutions and image overlaps. The systematic benchmarking of DAP acquisition parameters remains an outstanding research and operational gap for forest applications. While the impact of along-track overlap on point cloud metrics and area-based attribute estimates can be readily simulated, the impact of image resolution or across-track overlap requires purpose-acquired data. Moreover, although increases in along-track overlap are enabled by digital camera systems, costs for increasing across-track overlap can be substantial and may negate the cost-effectiveness of DAP for forest inventory. Hence, determining the impacts of varying acquisition parameters is of practical value for inventory programs. Researchers and practitioners have often assumed that more overlap will result in better DAP data, and that minimal overlaps often associated with historic airborne image campaigns are inadequate to support DAP processing. In our study, we found no marked difference among 15 and 20 cm spatial resolutions and overlap scenarios unless across-track overlap was reduced to 40%. Mean differences between DAP metrics and the ALS reference generally increased with decreasing overlap, and mean differences were larger for lower height percentiles (p10). Estimates of canopy height using the p90 metric varied by a root mean squared difference (RMSD) of approximately 5% between 15 cm and 20 cm datasets when along-track overlap was greater than 40%. Lower height percentiles were more strongly impacted by overlaps and resolution. Cover metrics varied by 2% RMSD across all overlap scenarios and resolutions. Comparisons between forest types (conifer, deciduous, mixed), terrain slope and aspect, and ALS-derived canopy cover were conducted to determine whether significant mean differences existed between DAP and the ALS reference. Although some significant differences were found by forest type and terrain variables, significant differences were most commonly associated with canopy cover. Based on the results reported herein, along and across-track overlaps ≥ 60% result in DAP metrics that were more similar to ALS. Increasing across-track overlap from 60% to 80% did not consistently improve the level of agreement between DAP metrics and ALS reference metrics. Conversely, DAP metrics generated using across-track overlaps <60% resulted in metrics with greater differences from the ALS reference, and a greater range of variability in DAP metric values. Image acquisitions for forest inventory must consider a broad range of factors and herein we have quantified that increasing along- or across-track overlap beyond 60% does not improve agreement with ALS area-based point cloud metrics commonly used to model forest inventory attributes. Likewise, overlap that is <60% does result in greater differences with ALS reference. Other applications beyond forest inventory may have different overlap requirements.
Why it matches plant phenotyping methods森林キャノピー高さ・被覆などの植物状態を推定する航空写真測量について、画像解像度とオーバーラップ条件を体系的にベンチマークし、ALS基準と比較しているため、測定法の技術評価が中心である。
abstractThe systematic benchmarking of DAP acquisition parameters remains an outstanding research and operational gap for forest applications.
Holm oak decline is a complex phenomenon mainly influenced by the presence of Phytophthora cinnamomi and water stress. Plant functional traits (PTs) are altered during the decline process — initially affecting the physiological condition of the plants with non-visual symptoms and subsequently the leaf pigment content and canopy structure — being its quantification critical for the development of scalable detection methods for effective management. This study examines the relationship between spectral-based PTs and oak decline incidence and severity. We evaluate the use of high-resolution hyperspectral and thermal imagery (< 1 m) together with a 3-D radiative transfer model (RTM) to assess a supervised classification model of holm oak decline. Field surveys comprising more than 1100 trees with varying disease incidence and severity were used to train and validate the model and predictions. Declining trees showed decreases of model-based PTs such as water, chlorophyll, carotenoid, and anthocyanin contents, as well as fluorescence and leaf area index, and increases in crown temperature and dry matter content, compared to healthy trees. Our classification model built using different PT indicators showed up to 82% accuracy for decline detection and successfully identified 34% of declining trees that were not detected by visual inspection and confirmed in a re-evaluation 2 years later. Among all variables analysed, canopy temperature was identified as the most important variable in the model, followed by chlorophyll fluorescence. This methodological approach identified spectral plant traits suitable for the detection of pre-symptomatic trees and mapping of oak forest disease outbreaks up to 2 years in advance of identification via field surveys. Early detection can guide management activities such as tree culling and clearance to prevent the spread of dieback processes. Our study demonstrates the utility of 3-D RTM models to untangle the PT alterations produced by oak decline due to its heterogeneity. In particular, we show the combined use of RTM and machine learning classifiers to be an effective method for early detection of oak decline potentially applicable to many other forest diseases worldwide.
Why it matches plant phenotyping methodsハイパースペクトル・熱画像、3-D放射伝達モデル、機械学習を用いて、樹木の生理・構造形質と病害状態を推定し、技術的に検証した研究であり、植物フェノタイピング手法が中心である。
abstractWe evaluate the use of high-resolution hyperspectral and thermal imagery (< 1 m) together with a 3-D radiative transfer model (RTM) to assess a supervised classification model of holm oak decline.
Accurate estimation of aerial net primary production (ANPP) using remotely acquired data is one of the main challenges in both environmental monitoring and precision agriculture. Reflectance-based techniques have been widely used for decades, but detection of fluorescence emission by chlorophyll has emerged as a promising alternative in recent years. Although passive sun-induced fluorescence (SIF) monitoring has shown interesting results, the information it provides is limited to few wavelengths (Fraunhofer and telluric lines). On the other hand, active measurements of steady-state fluorescence and its spectral distribution cover the full-emission spectrum but have not been fully explored due to obvious experimental limitations. In this work we develop a novel active fluorescence measurement procedure, based on lamps and sensors mounted on a field tractor. This technique allowed the detection of the full spectrum of fluorescence emission of a plant crop for the first time in the literature.The main objective of this work was to analyze how the information based on reflectance and fluorescence, recorded by the new proposed methodology, tracks the differences caused by different irrigation treatments in the ANPP of three soybean varieties. We observed that reflectance-based vegetation indices showed limited sensitivity to these cumulative differences, as only EVI2, NDWI and SRWI were able to distinguish between rainfed and irrigation treatments in some few cases. Passive, irradiance-normalised SIF showed this same trend, but active fluorescence peak ratio (FRₑd/FFₐᵣ₋ᵣₑd) revealed statistically significant differences for the three cultivars studied. In addition, the latter showed a significant correlation with ANPP for two soybean varieties after correction for light re-absorption and scattering (p 0.5), which was observed for only EVI and foliar water status VIs among passive indicators. Active fluorescence measurements at leaf level by PAM fluorometry did not show differences between treatments in the upper part of the canopy but revealed a biomass-dependent decrease in PSII yield along the vertical axis. Our study demonstrated that fluorescence emission spectrum holds highly valuable information that might allow monitoring ANPP changes upon irrigation from remote sensing applications, and therefore should be carefully studied. Lastly, it highlights the potential of SIF retrieval at both O₂-A and O₂-B lines.
Why it matches plant phenotyping methodsトラクター搭載のランプ・センサーによる新規アクティブ蛍光測定手法を開発し、蛍光スペクトルから作物ANPPや灌漑処理差を推定しており、フェノタイピング手法が研究の中心である。
abstractIn this work we develop a novel active fluorescence measurement procedure, based on lamps and sensors mounted on a field tractor.
Increased focus on restoring forest structural variation and spatial pattern in dry conifer forests has led to greater emphasis on forest monitoring strategies that can be summarized across scales. To inform restoration objectives with data sources that can characterize individual trees, groups of trees, and the entire stand, different remote sensing strategies such as aerial and terrestrial light detection and ranging (LiDAR) have been explored. Unfortunately, high equipment and operational costs of aerial systems, along with limited spatial extent of terrestrial scanners, have restricted widespread adoption of these technologies for repeated forest monitoring. This study investigates applications of unmanned aerial system (UAS) imagery for Structure from Motion derived modeling of individual tree and stand-level metrics. Specifically, we evaluate how flight parameters impact UAS extracted height and imputed DBH accuracies against field stem-mapped values. In total, 30 UAS image datasets collected from combinations of three altitudes, two flight patterns, and five camera orientations were assessed. Tree heights were extracted using a variable window function that searched UAS-derived canopy height models, while DBH was sampled from point cloud slices at 1.32–1.42 m using a least squares circle fitting algorithm. The sample trees were then filtered against National Forest Inventory data from the study region to ensure reasonable matching of extracted heights and diameters. The matched values were used to create a height to diameter relationship for predicting missing DBH values. Extracted and imputed tree values were compared against stem-mapped values to determine tree commission and omission rates, the accuracy and precision of extracted tree height, DBH, as well as overstory and understory stand density. Finding that, 1) tree extraction accuracy and correctness was maximized (F-score = 0.77) for nadir crosshatch UAS flight designs; 2) extracted tree height R² with stem-mapped values was high (R² ≥ 0.98) for all UAS flight parameters, but the quality (mean error = 0.79 cm) and quantity (~10% of all trees) of extracted DBH values was maximized for lower altitude, nadir crosshatch acquisitions; 3) the distribution of predicted DBH values most closely matched field observed values for off-nadir crosshatch flight designs; 4) using either off-nadir or crosshatch flight designs at lower altitudes maximized correlation (r > 0.70) and accuracy (basal area within 2 m² ha⁻¹) of stand density estimates. This study demonstrates a novel UAS-based inventory strategy for estimating individual tree structural attributes (i.e., location, height, and DBH) in dry conifer forests, without the need for in situ field observations.
Why it matches plant phenotyping methodsUAS画像とSfMを用いて樹高・DBH・立木密度を抽出し、飛行条件別の精度を検証することが研究の中心であるため、植物形質計測手法として含める。
abstractThis study investigates applications of unmanned aerial system (UAS) imagery for Structure from Motion derived modeling of individual tree and stand-level metrics.
Large-scale high-resolution satellite observations of plant functional diversity patterns will greatly benefit our ability to study ecosystem functioning. Here, we demonstrate a potentially scalable approach that uses aggregate plant traits estimated from radiative transfer model (RTM) inversion of Sentinel-2 satellite images to calculate community patterns of plant functional diversity. Trait retrieval relied on simulations and Look-up Tables (LUTs) generated by a RTM rather than heavily depending on a priori field data and data-driven statistical learning. This independence from in-situ training data benefits its scalability as relevant field data remains scarce and difficult to acquire. We ran a total of three different inversion algorithms that are representative of commonly applied approaches and we used two different metrics to calculate functional diversity.In tandem with Sentinel-2 image-based estimation of plant traits, we measured Leaf Area Index (LAI), leaf Chlorophyll content (CAB), and Leaf Mass per Area (LMA) in-situ in a (semi-)natural heterogeneous landscape (Montesinho region) located in northern Portugal. Sampling plots were scaled and georeferenced to match the satellite observed pixels and thereby allowed for a direct one-to-one posterior ground truth validation of individual traits and functional diversity.Across approaches, we observe a reasonable correspondence between the satellite-based retrievals and the in-situ observations in terms of the relative distribution of individual trait means and plant functional diversity across locations despite the heterogeneity of the landscape and canopies. The functional diversity estimates, based on a combination of canopy and leaf traits, were robust against estimation biases in trait means. Particularly, the convex hull volume estimate of functional diversity showed strong concordance with in-situ observations across all three inversion methods (Spearman's ρ: 0.67–0.80). The remotely sensed estimates of functional diversity also related to in-situ taxonomic diversity (Spearman's ρ: 0.55–0.63).Our work highlights the potential and challenges of RTM-based functional diversity metrics to study spatial community-level ecological patterns using currently operational and publicly available Sentinel-2 imagery. While further validation and assessment across different ecosystems and larger datasets are needed, the study contributes towards a further maturation of scalable, spatially, and temporally explicit methods for functional diversity assessments from space.
Why it matches plant phenotyping methodsSentinel-2画像のRTM逆解析により植物形質と機能多様性を推定し、現地測定で検証する手法が研究の中心であるため、植物フェノタイピング手法として採用する。
abstractTrait retrieval relied on simulations and Look-up Tables (LUTs) generated by a RTM
Estimation of Green Area Index (GAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) from decametric satellites was investigated in this study using a large database of ground measurements over croplands. It covers six main crop types including rice, corn, wheat and barley, sunflower, soybean and other types of crops. Ground measurements were completed using either digital hemispherical cameras, LAI-2000 or AccuPAR devices over sites representative of a decametric pixel. Sites were spread over the globe and the data collected at several growth stages concurrently to the acquisition of Landsat-8 images. Several machine learning techniques were investigated to retrieve GAI and fAPAR from the Landsat-8 top of canopy reflectance values, either using empirical or simulated calibration databases and generic or crop-type specific algorithms.Results show that using the six Landsat-8 bands together provided the best estimates of GAI and fAPAR. Machine learning techniques trained over a dataset simulated by the PROSAIL model provided less accurate estimates of GAI and fAPAR as compared to machine learning techniques trained over the ground data collected and applied over a similar dataset (best-case scenario). All machine learning techniques performed similarly when calibrated on PROSAIL simulations. The Gaussian process regression (GPR) was the best performing machine learning technique as compared to artificial neural networks (ANN), support vector machine regression (SVM) and as compared to NDVI simple model when calibrated over the ground dataset in the best-case scenario. However, the performance of the GPR trained over ground data is notably degraded when applied to crop types excluded from the calibration (worst-case scenario), and models based on simulations performs similarly of even better. Furthermore, training the GPR over specific crop types was performing slightly better than training over all the crop types together if at least 100 well distributed data points were available in the training dataset. Similar conclusions were obtained for the other machine learning techniques, with crop specific empirical models providing slightly better performances with a few exceptions. Finally, GAI was estimated by GPR with a RMSE varying between 0.45 and 1.19 and fAPAR with RMSE varying between 0.07 and 0.15 depending on the crop type.
Why it matches plant phenotyping methodsLandsat-8画像と機械学習を用いて作物のGAIおよびfAPARという植物形質を推定し、アルゴリズム、学習データ、作物別モデルの性能を比較・検証しているため、フェノタイピング手法が中心です。
abstractEstimation of Green Area Index (GAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) from decametric satellites was investigated in this study using a large database of ground measurements over croplands.
Vegetation phenology obtained from time series of remote sensing data is relevant for a range of ecological applications. The freely available Sentinel-2 imagery at a 10 m spatial resolution with a ~ 5-day repeat cycle provides an opportunity to map vegetation phenology at an unprecedented fine spatial scale. To facilitate the production of a Europe-wide Copernicus Land Monitoring Sentinel-2 based phenology dataset, we design and evaluate a framework based on a comprehensive set of ground observations, including eddy covariance gross primary production (GPP), PhenoCam green chromatic coordinate (GCC), and phenology phases from the Pan-European Phenological database (PEP725). We test three vegetation indices (VI) — the normalized difference vegetation index (NDVI), the two-band enhanced vegetation index (EVI2), and the plant phenology index (PPI) — regarding their capability to track the seasonal trajectories of GPP and GCC and their performance in reflecting spatial variabilities of the corresponding GPP and GCC phenometrics, i.e., start of season (SOS) and end of season (EOS). We find that for GPP phenology, PPI performs the best, in particular for evergreen coniferous forest areas where the seasonal variations in leaf area are small and snow is prevalent during wintertime. Results are inconclusive for GCC phenology, for which no index is consistently better than the others. When comparing to PEP725 phenology phases, PPI and EVI2 perform better than NDVI regarding the spatial correlation and consistency (i.e., lower standard deviation). We also link VI phenometrics at various amplitude thresholds to the PEP725 phenophases and find that PPI SOS at 25% and PPI EOS at 15% provide the best matches with the ground-observed phenological stages. Finally, we demonstrate that applying bidirectional reflectance distribution function correction to Sentinel-2 reflectance is a step that can be excluded for phenology mapping in Europe.
Why it matches plant phenotyping methodsSentinel-2による植物季節性（SOS・EOS）推定 framework を設計・評価し、複数の植生指数を地上観測と比較検証しており、植物表現型の取得・抽出法が中心である。
abstractwe design and evaluate a framework based on a comprehensive set of ground observations
The early detection of Xylella fastidiosa ( Xf ) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf -infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf- infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf ( n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400-850 nm) and short-wave infrared regions (SWIR, 950-1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (T c ), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDF a ), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR ( n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64-65% and kappa = 0.26-31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution.
Why it matches plant phenotyping methodsリモートセンシングで植物形質を取得し、Xylella感染状態を推定するRS-SVMと疫学モデルを開発・比較検証しており、表現型取得・抽出手法が研究の中心です。
abstractWe coupled a spatial spread model with the probability of Xf- infection predicted by a RS-driven support vector machine (RS-SVM) model.
Light detection and ranging (lidar) data acquired from airborne or spaceborne platforms have revolutionized measurement and mapping of forest attributes. Airborne data are often either acquired using multiple overlapped flight lines to provide complete coverage of an area of interest, or using transects to sample a given population. Spaceborne lidar datasets are unique to each sensor and are sample- or profile-based with characteristics driven by acquisition mode and orbital parameters. To leverage the wealth of accurate vegetation structural data from these lidar systems, a number of approaches have been developed to extend these observations over broader areas, from local landscapes to the globe. In this review we examine studies that have utilised modelling approaches to extend air- or space-based lidar data with the aim of communicating methods, outcomes, and accuracies, and offering guidance on linking lidar metrics and lidar-derived forest attributes with broad-area predictors. Modelling approaches are developed for a variety of applications. In some cases, generation of spatially-exhaustive layers may be useful for forest management purposes, driving management and inventory decisions over smaller focus areas or regions. In other cases, outputs are designed for monitoring at regional or global scales, and may be – due to the spatial grain of the structural estimates – insufficiently accurate or reliable for management. From the reviewed studies, we found height, aboveground biomass and volume, derived from either upper proportions of a large-footprint full-waveform lidar profiles, or statistically modelled from discrete return small-footprint lidar point clouds, to be the most commonly extended forest attributes, followed by canopy cover, basal area and stand complexity. Assessment of the accuracy and bias of the extrapolated forest attributes varied with both independent and model-derived estimates. The coefficient of determination (R²) was the most often reported, followed by absolute and relative (i.e., as a proportion of the mean) root mean square error (RMSE and RMSE% respectively). Compilation of the stated accuracies suggested that the variance explained in predictions of forest height ranged from R² = 0.38 to 0.90 (mean = 0.64), RMSE from 2 to 6m and RMSE% from 12 to 34%. For volume, R² ranged from 0.25 to 0.72 (mean = 0.53) and RMSE from 60 to 87 m³/ha and for aboveground biomass (AGB) R² ranged from 0.35 to 0.78 (mean = 0.55) and RMSE from 28 to 44 Mg/ha. There was no consensus on the level of accuracy required to support successful extension over larger areas. Ultimately, the review suggests that the information need motivating the spatial extension over larger areas drives the choice of the type of lidar data, spatial datasets and related grain. We conclude by discussing future directions and the outlook for new approaches including new lidar-derived response variables, advances in modelling approaches, and assessment of change.
Why it matches plant phenotyping methods森林の高さ、バイオマス、体積、樹冠被覆などの植物構造属性を、航空・宇宙 lidar とモデリングで推定・空間展開する手法をレビューしており、植物形質の取得・推定方法が中心である。
abstractIn this review we examine studies that have utilised modelling approaches to extend air- or space-based lidar data with the aim of communicating methods, outcomes, and accuracies, and offering guidance on linking lidar metrics and lidar-derived forest attributes with broad-area predictors.
Predicting leaf traits using models based on spectroscopic data can provide essential information to advance ecological research and future Earth system models. Most current models are based on Partial Least Squares Regression (PLSR) algorithms that attempt to predict a set of leaf traits of several plant groups using leaf spectra. However, PLSR models tend to be inconsistent in describing the importance of absorption features when used to predict leaf traits. Likewise, the effect of contrasting absorption features of different plant groups on the prediction and evaluation of PLSR models is not well understood. Hence, this study focuses on using wavelet spectra to overcome current PLSR's limitation and improve leaf trait predictions. Specifically, we explored the use of visible–near-infrared (0.45–1.0 μm) and mid- long-wave infrared spectra (2.55–11 μm) to predict three-leaf traits of lianas and trees: Leaf Mass Area (LMA), Water Content (WC), and Equivalent Water Thickness (EWT). We also compare the effect of life forms on the prediction of traits by using sun leaves collected from 14 liana species and 21 tree species (n = 700) from a Neotropical Dry Forest. On each leaf, reflectance measurements were performed for both selected spectral regions; then, leaf traits were estimated from a leaf segment. Leaf reflectance was first resampled and then processed using continuous wavelet transformation (CWT) to derive the wavelet spectra. PLSR models linking the leaf traits and the reflectance or wavelet spectra were compared. Our results reveal that PLSR models based on wavelet spectra require fewer components to predict traits (13–16) than those based on reflectance (25–29). In addition, PLSR models' performance (e.g., R²) of testing datasets tend to be higher for models based on wavelet spectra (LMA = 0.83; WC = 0.77; EWT = 0.68) than reflectance (LMA = 0.78; WC = 0.76; EWT = 0.49). Wavelet spectra models also seem to better characterize absorption features that drive the variability of leaf traits than models based on reflectance. However, life forms play an essential role in model performance, where the prediction of lianas' traits presenting lower R² (R² = 0.61 ± 0.25) than trees' traits (R² = 0.69 ± 0.15) regardless of the type of spectra or leaf trait. Our findings highlight the use of wavelet spectra to overcome limitations of the PLSR models for predicting leaf traits and the need to explore potential bias associated with plant groups on the model evaluations.
Why it matches plant phenotyping methods分光データとウェーブレット変換を用いて葉形質を予測する手法を開発・比較・評価しており、表現型取得と計算推定が研究の中心である。
abstractthis study focuses on using wavelet spectra to overcome current PLSR's limitation and improve leaf trait predictions.
Absorption of radiation in the photosynthetically active radiation (PAR) region is significantly influenced by plant biochemistry, structural properties, and photosynthetic pathway. To understand and quantify the effects of these traits on absorbed PAR it is necessary to develop practical and reliable tools that are sensitive to these traits. Using a semi-analytical modeling framework for deriving the absorption coefficient of plant canopies from reflectance spectra, we quantify the effects of functional, structural and biochemical traits of vegetation on the relationship between the absorption coefficient in the PAR region (αₚₐᵣ) with canopy characteristics such as the fraction of PAR absorbed by photosynthetically active vegetation (fAPARgᵣₑₑₙ) and chlorophyll (Chl) content. The reflectance dataset used in the study included simulated data obtained from a canopy reflectance model (PROSAIL) and empirical data on three diverse crop species with different leaf structures, canopy architectures and photosynthetic pathways (rice, maize and soybean) acquired at proximal (i.e., using field spectroradiometers) and remote (i.e., Landsat TM and ETM+) distances. Results show the usefulness of αₚₐᵣ derived from reflectance data for assessing not only the photosynthetic status of vegetation, but also the effects of different functional, structural and biochemical traits on plant performance. Furthermore, these assessments can be made using data acquired by satellite sensor systems such as the Landsat series, which are available since the 1980s, thus facilitating the analysis of the photosynthetic status of terrestrial ecosystems throughout the world with a high temporal depth.
Why it matches plant phenotyping methods反射スペクトルからPAR吸収係数を導出し、植物キャノピーの光合成状態や機能・構造・生化学的形質を評価する手法をモデルデータと複数作物の実測データで検証しており、表現型取得・評価法が中心である。
abstractUsing a semi-analytical modeling framework for deriving the absorption coefficient of plant canopies from reflectance spectra, we quantify the effects of functional, structural and biochemical traits of vegetation
Rice blast is considered as the most destructive disease that threatens global rice production and causes severe economic losses worldwide. A detection of rice blast infection in an early manner is vital to limit its expansion and proliferation. However, little research has been devoted to spectral detection of rice leaf blast (RLB) infection, especially at the asymptomatic or early stages. To fill the gap, this study aimed to examine the feasibility of detecting RLB infection from leaf reflectance spectra at asymptomatic, early and mild stages of disease development. Greenhouse experiments were conducted over two consecutive years to collect hyperspectral data (350–2500 nm) on various days after inoculation (DAIs) for the three infection stages. These hyperspectral data were processed to select disease specific spectral features (DSSFs). Such DSSFs were then used to feed the machine learning based sequential floating forward selection (ML-SFFS) methodology for determining the optimal feature combination (OFC) and overall accuracy (OA) in the detection of RLB at various infection stages.The results demonstrated that the rice plants displayed considerable biochemical and spectral variations and this pattern of variations existed consistently during plant-pathogen interactions. A multivariate pool of DSSFs comprising two reflectance bands, fourteen SIs, and five continuous wavelet coefficients, were determined for revealing the dynamic response of RLB infection across two years. The combination of 2 to 4 spectral features selected by the ML-SFFS algorithm was sufficient to identify infected leaves with classification accuracies over 65% and 80% for the asymptomatic and early infection stages, respectively. The OA could rise up to 95% for the mild stage. Compared to the use of all DSSFs with a support vector machine (SVM) classifier, the SVM-based SFFS (SVM-SFFS) algorithm prevailed in the classification accuracy up to 10% over the sampling period. Our results demonstrated the feasibility of accurate classification of RLB infected samples by ML-SFFS. This study suggests that reflectance spectroscopy has great potential in the pre-visual detection of RLB infection and airborne or spaceborne imaging spectroscopy is promising for the mapping of early occurrence and severity levels of RLB infection at large scales.
Why it matches plant phenotyping methodsイネ葉の病害状態を反射スペクトルで直接推定し、特徴選択と機械学習による検出性能を評価しており、病害フェノタイピング手法が研究の中心である。
abstractSuch DSSFs were then used to feed the machine learning based sequential floating forward selection (ML-SFFS) methodology for determining the optimal feature combination (OFC) and overall accuracy (OA) in the detection of RLB at various infection stages.
As an essential feature of plant autotrophy, Nitrogen (N) is the major nutrient affecting plant growth in terrestrial ecosystems, thus is of not only fundamental scientific interest, but also a crucial factor in crop productivity. Timely non-destructive monitoring of canopy nitrogen concentration (N%) demands fast and highly accurate estimation, which is often quantified using spectroscopic analyses in the 400—2500 nm spectral region. However, extracting a set of useful spectral absorption features from canopy spectra to determine N% remains challenging due to confounding canopy architecture. Deep Learning as a statistical learning technique is useful to extract biochemical information from canopy spectra. We evaluated the performance of a one-dimensional convolutional neural network (1D-CNN) and compared it with two state-of-the-art methods: partial least squares regression (PLSR) and gaussian process regression (GPR). We utilized a large and diverse in-field multi-season (autumn, winter, spring and summer) spectral database (n = 7014) over 8 years (2009–2016) of dairy and hill country farms across New Zealand to develop season specific and spectral-region specific (VNIR and/or SWIR) 1D-CNN models. Results on the independent validation dataset (not used to train the model) showed that the 1D-CNN model provided higher accuracy (R² = 0.72; nRMSE% = 14) than PLSR (R² = 0.54; nRMSE% = 19) and GPR (with R² = 0.62; nRMSE% = 16). Season specific models based on 1D-CNN indicated apparent differences (14 ≤ nRMSE ≤19 for the test dataset), while the performance of all seasons combined model was remained higher for the test dataset (nRMSE% = 14). The full spectral range model showed higher accuracy than the spectral region-specific models (VNIR and SWIR alone) (15.8 ≤ nRMSE ≤18.5). Additionally, predictions derived using 1D-CNN were more precise (less uncertain) with <0.12 mean standard deviation (uncertainty intervals) than PLSR (0.31) and GPR (0.16). This study demonstrated the potential of 1D-CNN as an alternative to conventional techniques to determine the N% from canopy hyperspectral spectra.
Why it matches plant phenotyping methodsキャノピー分光から植物の窒素濃度を推定するCNN手法を開発・比較検証しており、形質取得とモデル性能評価が研究の中心である。
abstractTimely non-destructive monitoring of canopy nitrogen concentration (N%) demands fast and highly accurate estimation
Due to the success of the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, new satellite missions are on the horizon. The current and future missions can benefit from investigations that seek to improve retrieval algorithms that quantitatively map global soil moisture and vegetation optical depth (tau) from Earth's microwave emissions. In this study, we explore multi-angular and multi-frequency approaches for the retrieval of soil moisture and vegetation tau, considering the payload configurations of current and future satellite missions (such as the Copernicus Imaging Microwave Radiometer, the Water Cycle Observation Mission, and the Terrestrial Water Resources Satellite) using a new set of ground observations. Two ground-based microwave radiometry datasets collected in Inner Mongolia during the Soil Moisture Experiment in the Luan River from July to August 2017 (cropland) and August to September 2018 (grassland) are used for this study. The corn field, which covers an entire growth period, indicated that the degree of information increases linearly as the number of channels (in terms of the incidence angle and frequency) increases, and that the multi-frequency observations contain slightly more independent information than do the multi-angular observations under the same number of channels. The polarization difference in brightness temperature is sensitive to both soil moisture and vegetation water content, especially at L-band due to its penetrating ability. Soil moisture explains most of the variance in frequency differences of brightness temperature at adjacent frequencies (L- & C-bands, C- & X-bands), while the variance in incidence-angle differences of brightness temperature is mostly associated with the vegetation water content. A multi-channel collaborative algorithm (MCCA) is developed based on the two-component version of the omega-tau model, which utilizes information from collaborative channels expressed as an analytical form of brightness temperature at the core channel to rule out the parameters to be retrieved. Results of soil moisture retrieval show that the multi-angular approach used by the MCCA generally has a better performance, unbiased root mean square difference (ubRMSD) varying from 0.028 cm³/cm³ to 0.037 cm³/cm³, than the multi-frequency approach (ubRMSD from 0.028 cm³/cm³ to 0.089 cm³/cm³) for the corn field. This is attributed to the dependence of vegetation tau on the frequency being more significant than that on the incidence angle. Except for in the C- & X-band combination, the multi-frequency approach used by the MCCA performs better (ubRMSD from 0.018 cm³/cm³ to 0.023 cm³/cm³) than the multi-angular version (ubRMSD from 0.026 cm³/cm³ to 0.034 cm³/cm³) for the grass field due to reduced vegetation effects for this type of cover. It is affirmed that increasing the number of observation channels could make the soil moisture retrieval more robust, but might also limit the retrieval performance, as the probability that the model estimations will not match the observations is increased. This study provides new insights into the design of potential satellite missions to improve soil moisture retrieval. A satellite with simultaneous multi-angular and multi-frequency observation capabilities is highly recommended.
Why it matches plant phenotyping methods作物・草地の植生光学的厚さや植生水分状態を多チャネルマイクロ波観測から推定するアルゴリズムを開発・評価しており、植物キャノピー状態の取得法が中心である。
abstractA multi-channel collaborative algorithm (MCCA) is developed based on the two-component version of the omega-tau model
Recent developments in remote sensing are enabling automatic, high resolution, and non-destructive survey of agriculture fields, providing the key basis for advancing plant breeding. Among the used remote sensing modalities, LiDAR has attracted wide attention for its ability to directly provide accurate 3D information. Despite the increasing utilization of LiDAR technology in phenotyping, there is still a lack of effective quality control strategies, in particular, quality control of LiDAR data collected on a multi-temporal basis. This study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields. Features extracted from the fields – terrain patches and row/alley locations – are utilized for evaluating the vertical and planimetric relative accuracy of the point clouds. Row/alley locations in the field are automatically identified from the point clouds based on the assumption that higher point density and/or higher elevation correspond to plant locations. The performance of the proposed quality control strategies is evaluated using multi-temporal datasets collected in agricultural fields of different sizes, orientation, crops, and growth stages. The result shows that the net vertical and planimetric discrepancies between multi-temporal point clouds are ±3 cm and ±8 cm, respectively. While the former reflects the actual accuracy of the point clouds, the latter is a combined effect of the LiDAR point cloud accuracy, rasterization artifacts, crop type, growth pattern, and wind condition during data acquisition. In terms of row and alley detection, the result shows that the proposed strategy achieves high performance and can deal with different planting orientation, crop types, growth stages, canopy cover, and planting density. In conclusion, this study presents a quality control framework for multi-temporal LiDAR data. Moreover, the row and alley detection leads to automated extraction of plots, and hence facilitates the use of remotely sensed data for automated phenotyping.
Why it matches plant phenotyping methods農業圃場のマルチテンポラルUAV LiDARについて、品質管理、作物列・区画抽出、作物表現型解析への利用を中心に技術を開発・評価しているため、植物フェノタイピング手法として含める。
abstractThis study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields.
In forest landscapes affected by fire, the estimation of fractional vegetation cover (FVC) from remote sensing data using radiative transfer models (RTMs) enables to evaluate the ecological impact of such disturbance across plant communities at different spatio-temporal scales. Even though, when landscapes are highly heterogeneous, the fine-scale ground spatial variation might not be properly captured if FVC products are provided at moderate or coarse spatial scales, as typical of most of operational Earth observing satellite missions. The objective of this study was to evaluate the potential of a RTM inversion approach for estimating FVC from satellite reflectance data at high spatial resolution as compared to the standard use of coarser imagery. The study was conducted both at landscape and plant community levels within the perimeter of a megafire that occurred in western Mediterranean Basin. We developed a hybrid retrieval scheme based on PROSAIL-D RTM simulations to create a training dataset of top-of-canopy spectral reflectance and the corresponding FVC for the dominant plant communities. The machine learning algorithm Gaussian Processes Regression (GPR) was learned on the training dataset to model the relationship between canopy reflectance and FVC. The GPR model was then applied to retrieve FVC from WorldView-3 (spatial resolution of 2 m) and Sentinel-2 (spatial resolution of 20 m) surface reflectance bands. A set of 75 plots of 2x2m and 45 plots of 20x20m was distributed under a stratified schema across the focal plant communities within the fire perimeter to validate FVC satellite derived retrieval. At landscape scale, the accuracy of the FVC retrieval was substantially higher from WorldView-3 (R² = 0.83; RMSE = 7.92%) than from Sentinel-2 (R² = 0.73; RMSE = 11.89%). At community level, FVC retrieval was more accurate for oak forests than for heathlands and broomlands. The retrieval from WorldView-3 minimized the over- and underestimation effects at low and high field sampled vegetation cover, respectively. These findings emphasize the effectiveness of high spatial resolution satellite reflectance data to capture FVC ground spatial variability in heterogeneous burned areas using a hybrid RTM retrieval method.
Why it matches plant phenotyping methods衛星反射データと放射伝達モデル・機械学習を組み合わせ、植物群落の植生被覆率（FVC）を推定する手法を開発し、異なる空間解像度で検証しているため、植物表現型取得が中心である。
abstractWe developed a hybrid retrieval scheme based on PROSAIL-D RTM simulations to create a training dataset of top-of-canopy spectral reflectance and the corresponding FVC for the dominant plant communities.
Imaging spectroscopy provides the opportunity to monitor nutrient status of vegetation. In crops, prior studies have generally been limited in scope, either to a small wavelength range (e.g., 400–1300 nm), a small number of crop cultivars, a single growth stage or single growing season. Methods that are not time- or site-specific are needed to use imaging spectroscopy for routine monitoring of crop status. Using data from four cultivars of potatoes (Solanum tuberosum L.), three growth stages and two growing seasons, we demonstrate the capacity of full-range (400–2350 nm) imaging spectroscopy to quantify nutrient status (petiole nitrate, whole leaf and vine total nitrogen) and predict tuber yield in potatoes across cultivars, growth stages and growing seasons. We specifically tested the capabilities of: (1) ordinary least-squares regression (OLSR) using traditional hyperspectral vegetation indices (VIs); (2) partial least-squares regression (PLSR) using full spectrum (400–2350 nm), VNIR- (visible-to-near infrared: 400–1300 nm) or SWIR-only (shortwave infrared: 1400–2350 nm) wavelengths; (3) predictive models developed for one potato type or planting season on withheld data from a different type or season. Our results show that OLSR models produced poor predictions with data from all dates pooled together (validation R² < 0.01). Single-date OLSR models performed better (R² = 0.20–0.60, relative RMSE = 15–30%). PLSR models performed well and were comparable using different spectral regions (full-spectrum, VNIR-only and SWIR-only), with validation R² = 0.68–0.82 and RRMSE = 12–25%. Testing across potato types, models produced reliable predictions (R² = 0.45–0.75, RRMSE = 13–30%), but with some bias. Cross-season models had validation R² = 0.46–0.75 and RRMSE = 17–100%, with a more significant bias than the cross-potato type models. To achieve models that are generalizable and robust, we recommend: (1) obtaining ground measurements that capture the full range of plant growth conditions and developmental stages, and (2) ensuring that image processing approaches minimize spectral discrepancies among dates.
Why it matches plant phenotyping methodsハイパースペクトル画像と回帰モデルにより、ジャガイモの栄養状態と収量を定量推定し、品種・生育段階・季節をまたいで検証しているため、植物表現型取得・推定手法が中心である。
abstractwe demonstrate the capacity of full-range (400–2350 nm) imaging spectroscopy to quantify nutrient status (petiole nitrate, whole leaf and vine total nitrogen) and predict tuber yield in potatoes across cultivars, growth stages and growing seasons.
Assessing changes in forest structure over time is crucial for monitoring forest resources, supporting sustainable forest management practices, and providing key insights into changes in the terrestrial carbon cycle. Recent research interest and rapid growth of unmanned aerial vehicle (UAV)-based digital aerial photogrammetry (DAP) technology principally due to its low cost and timeliness, is providing high-spatial resolution data for enhancing forest inventories and forest dynamics monitoring. The increasing prevalence of these UAV acquired datasets in forestry promotes a need for better understanding of how DAP-based point clouds change over time, and how these changes may relate to changes in forest structure. In this study, we utilized bi-temporal DAP data to investigate changes in forest structure over a 3-year period in subtropical planted forest stands throughout Dongtai Yellow Sea National Forest Park, Jiangsu Province, China. To do so, we evaluated both direct (i.e., structural parameter changes estimated using the differences between bi-temporal DAP metrics) and indirect (i.e., structural parameters were modelled for each date, and their changes predicted as their differences) methods to estimate the changes in forest structure. In addition, once models were developed, changes in Lorey's mean height and volume were extrapolated across the entire study site and examined related to the forest type and age. Our assessments of the different approaches showed that the direct approach (R² = 0.54–0.78) resulted in improved accuracy compared to the indirect method (R² = 0.51–0.73). The distributional metrics, namely, height percentiles (e.g., H₇₅ and H₉₅) and canopy return density (e.g., D₇ and D₅), and the Weibull-fitted metrics (e.g., α) were found to be sensitive to changes in structural parameters, whereas canopy volume-related metrics had relatively low predictive capabilities. Overall, the predicted changes in Lorey's mean height and volume were mapped over the entire study area, and indicated that Lorey's mean height increased mostly in middle-aged and young forest stands. Over-mature stands showed the lowest height increment. This study proved the capability of using bi-temporal point clouds from UAV-based DAP for enhancing forest inventories and promoting sustainable forest management.
Why it matches plant phenotyping methodsUAVデジタル航空写真測量点群を用いて森林構造（平均樹高・体積）の変化推定手法を比較・検証しており、植物形質の取得方法が中心的です。
abstractIn this study, we utilized bi-temporal DAP data to investigate changes in forest structure over a 3-year period
During spruce budworm (SBW; Choristoneura fumiferana (Clem.)) outbreaks, defoliation causes severe tree damage and mortality to spruce-balsam fir (Picea spp.-Abies balsamea (L.) Mill.) forests. Annual defoliation thematic maps provide vital information for forest protection planning against outbreaks, but such data are challenging to accurately collect over large areas during a short (2–3 week) period when damaged foliage peaks. Current operational methods for estimating annual defoliation are aerial surveys, through visual observations by professionals from small aircraft, defining broad defoliation classes which are subjective and spatially can be inaccurate. Remote sensing studies estimating annual defoliation are limited due to lack of ground verification data and difficulty of capturing spectral changes specific to the short detection period. Only one study has used satellite hyperspectral imagery to detect annual SBW defoliation, classifying just two classes of defoliation and using inaccurate aerial survey results for training and validation. In this paper, we introduce a new method to estimate annual SBW defoliation in three classes (light ≤ 30%, moderate 30–70%, and severe ≥ 70%), using change detection of vegetation indices (VIs) derived from satellite (EO-1 Hyperion) hyperspectral imagery acquired before and after annual defoliation. Random Forest and Support Vector Machine classifiers were used to classify defoliation, and intensive field measurements of branch-level defoliation per plot were used for accuracy assessment. Field plots, with spatially correlated defoliation within stands, were used to derive an additional 624 plots to obtain sufficient and independent datasets for training and validating the detection results. The top four VIs for detecting annual defoliation, based on Random Forest Total Variable Importance Score and Spearman's rank correlations, were Plant Senescence Reflectance Index, Normalized Difference Water Index, Moisture Stress Index, and Enhanced Vegetation Index. The spectral regions of VIs used to classify defoliation included visible, near-infrared, red-edge, and shortwave infrared. Accuracies of the light, moderate, and severe defoliation classes using our method ranged from 55–85%, 40–76%, and 40–80%, respectively, which were significantly higher than the 32% accuracy of aerial survey defoliation data for the plots.
Why it matches plant phenotyping methods衛星ハイパースペクトル画像と植生指数の変化検出により、樹木の食害・落葉状態を定量分類する手法を開発し、現地測定で精度検証しているため、植物状態の取得手法が中心である。
abstractwe introduce a new method to estimate annual SBW defoliation in three classes
Plant leaf water content plays a key role in several biogeochemical processes, such as photosynthesis, evapotranspiration, and net primary production. Yet, the accurate estimation of leaf water content using multi-angular reflectance measurements across different plant species is still challenging. This study aims to propose a generic spectral index for accurately estimating equivalent water thickness (EWT) when multi-angular spectral reflection is considered. The index was selected to have the format of a difference ratio using three reflectance factors. The reflectance factor at 410 nm was used to reduce the specular reflection from the leaf surface in the 400–2500 nm range, and the ratio of the wavelengths in the 1268–1285 nm range (at non-water absorption wavelengths) to the wavelengths in the 1339–1346 nm range (at water absorption wavelengths) strengthened the relationship with EWT for all of the sampled plant species. The modified difference ratio (MDR) index, (R₁₂₇₁-R₄₁₀)/(R₁₃₄₂-R₄₁₀), was linearly proportional to EWT, with R² > 0.90, when the leaves reflection data were collected from various viewing angles. However, the relationships between some existing indices (simple difference, simple ratio, normalized difference, double difference index and difference ratio indices) and EWT at the leaf level were weak and unstable for all of the 18 plant species (including 14 broadleaf, 3 shrub, and 1 liana species) at different angles under laboratory and field conditions. Moreover, validation results from six independent datasets (n = 1800) and one modeled dataset (n = 2375) further confirmed that the algorithm derived from the proposed index (based on multi-angular reflectance factors of leaves) was not only effective for EWT estimation across a diverse set of plant species with widely variable leaf structure and water content, but also insensitive to different measurement conditions (leaf clip, integrating spheres or multi-angle measurements). The algorithm developed from this new index is generic, does not require reparameterization for each species, and can be accurately used for nondestructive EWT estimations using a simple handheld laboratory or field instrument, and thus, is convenient for agricultural and ecological studies.
Why it matches plant phenotyping methods多角度反射スペクトルから葉の含水量（EWT）を推定する新規スペクトル指数を開発し、複数データセットで検証しており、植物表現型取得法が研究の中心である。
abstractThis study aims to propose a generic spectral index for accurately estimating equivalent water thickness (EWT) when multi-angular spectral reflection is considered.
Multi-sensor remote sensing data fusion technologies have been developed and widely applied in recent years, providing a feasible and economical solution to increase the availability of high spatial and temporal resolution data. These methods, however, have been challenging to apply in highly heterogeneous areas, especially in complex agricultural landscapes where there are rapid changes at small scales, while features at larger scales change more slowly. In this study, we developed a novel method to reconstruct daily 30 m Normalized Difference Vegetation Index (NDVI) using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat and Landsat-like platforms, and the Cropland Data Layer (CDL). This method utilizes a crop reference curve (CRC) approach, in which a set of NDVI time series are extracted from pure MODIS pixels (250 m resolution) identified using the CDL, and then used to fit Landsat-like observations (30 m). The CRC based method was applied over a complex agricultural landscape in the Choptank River watershed on the Eastern Shore of Maryland. Landsat data from 2013 and 2014 and Harmonized Landsat and Sentinel-2 (HLS) data from 2018 were used to reconstruct 30 m daily NDVI maps for major crop types. Results show that the relative error (RE) in reconstructed NDVI is around 6–8% during periods of rapid crop growth, and 3–5% during peak periods when growth is slow. The accuracy of the CRC method outperforms a standard image pair-based data fusion algorithm (Spatial and Temporal Adaptive Reflectance Fusion Model; STARFM), which yields RE of 4–9% in slow-growth periods and 10–16% in fast-growth periods when clear Landsat images are scarce. The CRC method was also compared with time-series data fusion methods, including a harmonic fitting model and the SaTellite dAta IntegRation (STAIR) model. The results show that CRC gives similar results when the Landsat-like image availability is high (around 27 images per year), but outperforms other methods when availability is limited (less than 15 images per year). The reconstructed NDVI time series for corn, soybean, winter wheat/soybean and forest at 30-m resolution show clear phenological patterns at the sub-field scale. The resulting 30-m NDVI timeseries data provide useful information for mapping crop phenology and monitoring crop condition in complex agricultural landscapes, especially for complex double-cropping areas. However, the input requirement of an accurate 30-m crop classification map constrains its application to areas and periods where classifications are available.
Why it matches plant phenotyping methods作物のNDVI時系列を30 m解像度で再構成する手法を開発し、複数のデータ融合手法と精度比較している。NDVIおよびフェノロジー・作物状態という植物状態の推定が中心である。
abstractIn this study, we developed a novel method to reconstruct daily 30 m Normalized Difference Vegetation Index (NDVI) using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat and Landsat-like platforms, and the Cropland Data Layer (CDL).
In forest landscapes affected by fire, the estimation of fractional vegetation cover (FVC) from remote sensing data using radiative transfer models (RTMs) enables to evaluate the ecological impact of such disturbance across plant communities at different spatio-temporal scales. Even though, when landscapes are highly heterogeneous, the fine-scale ground spatial variation might not be properly captured if FVC products are provided at moderate or coarse spatial scales, as typical of most of operational Earth observing satellite missions. The objective of this study was to evaluate the potential of a RTM inversion approach for estimating FVC from satellite reflectance data at high spatial resolution as compared to the standard use of coarser imagery. The study was conducted both at landscape and plant community levels within the perimeter of a megafire that occurred in western Mediterranean Basin. We developed a hybrid retrieval scheme based on PROSAIL-D RTM simulations to create a training dataset of top-of-canopy spectral reflectance and the corresponding FVC for the dominant plant communities. The machine learning algorithm Gaussian Processes Regression (GPR) was learned on the training dataset to model the relationship between canopy reflectance and FVC. The GPR model was then applied to retrieve FVC from WorldView-3 (spatial resolution of 2 m) and Sentinel-2 (spatial resolution of 20 m) surface reflectance bands. A set of 75 plots of 2x2m and 45 plots of 20x20m was distributed under a stratified schema across the focal plant communities within the fire perimeter to validate FVC satellite derived retrieval. At landscape scale, the accuracy of the FVC retrieval was substantially higher from WorldView-3 (R 2 = 0.83; RMSE = 7.92%) than from Sentinel-2 (R 2 = 0.73; RMSE = 11.89%). At community level, FVC retrieval was more accurate for oak forests than for heathlands and broomlands. The retrieval from WorldView-3 minimized the over- and under-estimation effects at low and high field sampled vegetation cover, respectively. These findings emphasize the effectiveness of high spatial resolution satellite reflectance data to capture FVC ground spatial variability in heterogeneous burned areas using a hybrid RTM retrieval method.
Why it matches plant phenotyping methods衛星反射データと放射伝達モデル・機械学習を組み合わせ、植物群落のFVCという明示的な植物状態を推定・検証する検索手法が研究の中心であるため。
abstractThe objective of this study was to evaluate the potential of a RTM inversion approach for estimating FVC from satellite reflectance data at high spatial resolution as compared to the standard use of coarser imagery.
Functional diversity (FD) provides a link between biodiversity and ecosystem functioning, summarizing inter- and intra-specific variation of functional traits. However, quantifying plant traits and FD consistently and cost-effectively across large and heterogeneous forest areas is challenging with traditional field sampling. Airborne light detection and ranging (LiDAR) and imaging spectroscopy provide spatially explicit data, which allow mapping of selected forest traits and FD at different spatial scales. We develop an individual tree-based method to measure forest FD from tree neighborhoods to whole forests, and demonstrate the approach by mapping functional traits of over one million trees in a subtropical forest in China. We retrieved canopy morphological traits (95th quantile height, leaf area index and foliage height diversity) and physiological traits (proxies of nitrogen, carotenoids and specific leaf area) for each individual canopy tree crown from LiDAR and imaging spectroscopy data, respectively. Based on the multivariate trait space spanned by the six trait axes and filled by measured tree individuals, we mapped forest FD as richness, divergence and evenness, and explored spatial patterns of FD as well as FD–area and FD–tree number relationships. The results show that LiDAR-derived morphological traits and spectral indices of physiological traits are consistent with field measurements and show weak correlations between each other at individual tree level. Morphological functional richness follows a hump-shaped pattern along the elevational gradient of 984–1805 m, with maximum values at elevations around 1450 m, while high physiological functional richness occurs at medium and high elevations. At an ecosystem scale of 30 × 30 m, morphological richness increases continuously with tree density, but physiological richness decreases again at very high densities. Moreover, functional richness shows a logarithmic relationship with increasing area or number of individual trees, and local trait convergence is predominant in our study area. We demonstrate the ability to quantify FD using morphological and physiological traits by remote sensing, which provides a pathway to conduct individual-level trait-based ecology with wall-to-wall data.
Why it matches plant phenotyping methodsLiDARとイメージング分光から個体樹冠の形態・生理形質を推定し、森林機能多様性を測定する手法を開発・実証しており、表現型取得が研究の中心です。
abstractWe develop an individual tree-based method to measure forest FD from tree neighborhoods to whole forests
Models of radiative transfer (RT) are important tools for remote sensing of vegetation, allowing for forward simulations of remotely sensed data as well as inverse estimation of biophysical and biochemical traits from vegetation optical properties. Estimation of foliar protein content is the key to monitor the nitrogen cycle in terrestrial ecosystems, in particular to assess the photosynthetic capacity of plants and to improve nitrogen management in agriculture. However, until now physically based leaf RT models have not allowed for proper spectral decomposition and estimation of leaf dry matter as nitrogen-based proteins and other carbon-based constituents (CBC) from optical properties of fresh and dry foliage. Such an achievement is the key for subsequent upscaling to canopy level and for development of new Earth observation applications.Therefore, we developed a new version of the PROSPECT model, named PROSPECT-PRO, which separates the nitrogen-based constituents (proteins) from CBC (including cellulose, lignin, hemicellulose, starch and sugars). PROSPECT-PRO was calibrated and validated on subsets of the LOPEX dataset, accounting for both fresh and dry broadleaf and grass samples. We applied an iterative model inversion optimization algorithm and identified the optimal spectral ranges for retrieval of proteins and CBC. When combining leaf reflectance and transmittance within the selected optimal spectral domains, PROSPECT-PRO inversions revealed similarly accurate CBC estimates of fresh and dry leaf samples (respective validation R² = 0.96 and 0.95, NRMSE = 9.6% and 13.4%), whereas a better performance was obtained for fresh than for dry leaves when estimating proteins (respective validation R² = 0.79 and 0.57, NRMSE = 15.1% and 26.1%). The accurate estimation of leaf constituents for fresh samples is attributed to the optimal spectral feature selection procedure.We further tested the ability of PROSPECT-PRO to estimate leaf mass per area (LMA) as the sum of proteins and CBC using independent datasets acquired for numerous plant species. Results showed that both PROSPECT-PRO and PROSPECT-D inversions were able to produce comparable LMA estimates across an independent dataset gathering 1685 leaf samples (validation R² = 0.90 and NRMSE = 16.5% for PROSPECT-PRO, and R² = 0.90 and NRMSE = 18.3% for PROSPECT-D). Findings also revealed that PROSPECT-PRO is capable of assessing the carbon-to‑nitrogen ratio based on the retrieved CBC-to-proteins ratio (R² = 0.87 and NRMSE = 15.7% for fresh leaves, and R² = 0.65 and NRMSE = 28.1% for dry leaves). The performance assessment of newly designed PROSPECT-PRO demonstrates a promising potential for its involvement in precision agriculture and ecological applications aiming at estimation of leaf carbon and nitrogen contents from observations of current and forthcoming airborne and satellite imaging spectroscopy sensors.
Why it matches plant phenotyping methodsPROSPECT-PROという放射伝達モデルを開発・較正・検証し、葉のタンパク質、炭素系成分、LMA、C/N比を反射・透過スペクトルから推定する手法が中心であるため、植物フェノタイピング手法研究に該当する。
abstractTherefore, we developed a new version of the PROSPECT model, named PROSPECT-PRO, which separates the nitrogen-based constituents (proteins) from CBC
Significant advances toward the remote sensing of photosynthetic activity have been achieved in the last decades, including sensor design and radiative transfer model (RTM) development. Nevertheless, finding methods to accurately quantify carbon assimilation across species and spatial scales remains a challenge. Most methods are either empirical and not transferable across scales or can only be applied if highly complex input data are available. Under stress, the photosynthetic rate is limited by the maximum carboxylation rate (Vcₘₐₓ), which is determined by the leaf biochemistry and the environmental conditions. Vcₘₐₓ has been connected to plant photoprotective mechanisms, photosynthetic activity and chlorophyll fluorescence emission. Recent RTM developments such as the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model allow the simulation of the sun-induced chlorophyll fluorescence (SIF) and Vcₘₐₓ effects on the canopy spectrum. This development provides an approach to retrieve Vcₘₐₓ through RTM model inversion and track assimilation rate. In this study we explore SIF, narrow-band indices and RTM inversion to track changes in photosynthetic efficiency as a function of vegetation stress. We use hyperspectral imagery acquired over an almond orchard under different management strategies which affected the assimilation rates measured in the field. Vcₘₐₓ used as an indicator of assimilation was retrieved through SCOPE model inversion from pure-tree crown hyperspectral data. The relationships between field-measured assimilation rates and Vcₘₐₓ retrieved from model inversion were higher (r² = 0.7–0.8) than when SIF was used alone (r² = 0.5–0.6) or when traditional vegetation indices were used (r² = 0.3–0.5). The method was proved successful when applied to two independent datasets acquired at two different dates throughout the season, ensuring its robustness and transferability. When applied to both dates simultaneously, the results showed a unique significant trend between the assimilation measured in the field and Vcₘₐₓ derived using SCOPE (r² = 0.56, p < 0.001). This work demonstrates that tracking assimilation in almond trees is feasible using hyperspectral imagery linked to radiative transfer-photosynthesis models.
Why it matches plant phenotyping methods航空ハイパースペクトル画像とSCOPEモデル逆解析により、樹冠のVcmaxおよび光合成同化速度を推定する植物表現型計測法を開発・検証しており、手法が研究の中心である。
abstractVcₘₐₓ used as an indicator of assimilation was retrieved through SCOPE model inversion from pure-tree crown hyperspectral data.
High-throughput mapping of latent heat flux (λET) is critical to efforts to optimize water resources management and to accelerate forest tree breeding for improved drought tolerance. Ideally, investigation of the energy response at the tree level may promote tailored irrigation strategies and, thus, maximize crop biomass productivity. However, data availability is limited and planning experimental campaigns in the field can be highly operationally complex. To this end, a multi-platform multi-sensor observational approach is herein developed to dissect the λET signature of a black poplar (Populus nigra) breeding population (“POP6”) at the canopy level. POP6 comprised more than 4600 trees representing 503 replicated genotypes, whose parents were derived from contrasting environmental conditions. Trees were trialed in two adjacent plots where different irrigation treatments (moderate drought [mDr] and well-watered [WW]) were applied. Data collected from satellite and unmanned aerial vehicles (UAVs) remote sensing as well as from ground-based proximal sensors were integrated at consistent spatial aggregation and combined to compute the surface energy balance of the trees through a modified Priestley-Taylor method. Here, we demonstrated that λET response was significantly different between WW and mDr trees, whereby genotypes in mDr conditions exhibited larger standard deviations. Importantly, genotypes classified as drought tolerant based on the stress susceptibility index (SSI) presented λET values significantly higher than the rest of the population. This study confirmed that water limitation in mDr settings led to reduced soil moisture in the tree root zone and, thus, to lower λET. These results pave the way to breeding poplar and other bioenergy crops with this underexploited trait for higher λET. Most notably, the illustrated work demonstrates a multi-platform multi-sensor data fusion approach to tackle the global challenge of monitoring landscape-scale ecosystem processes at fine resolution.
Why it matches plant phenotyping methods黒ポプラ個体・遺伝型の干ばつ応答を推定するマルチプラットフォーム・マルチセンサー融合手法を開発・適用しており、潜熱フラックスという生理形質の取得が研究の中心である。
abstracta multi-platform multi-sensor observational approach is herein developed to dissect the λET signature of a black poplar (Populus nigra) breeding population (“POP6”) at the canopy level.
Tropical forest ecosystems are undergoing rapid transformation as a result of changing environmental conditions and direct human impacts. However, we cannot adequately understand, monitor or simulate tropical ecosystem responses to environmental changes without capturing the high diversity of plant functional characteristics in the species-rich tropics. Failure to do so can oversimplify our understanding of ecosystems responses to environmental disturbances. Innovative methods and data products are needed to track changes in functional trait composition in tropical forest ecosystems through time and space. This study aimed to track key functional traits by coupling Sentinel-2 derived variables with a unique data set of precisely located in-situ measurements of canopy functional traits collected from 2434 individual trees across the tropics using a standardised methodology. The functional traits and vegetation censuses were collected from 47 field plots in the countries of Australia, Brazil, Peru, Gabon, Ghana, and Malaysia, which span the four tropical continents. The spatial positions of individual trees above 10 cm diameter at breast height (DBH) were mapped and their canopy size and shape recorded. Using geo-located tree canopy size and shape data, community-level trait values were estimated at the same spatial resolution as Sentinel-2 imagery (i.e. 10 m pixels). We then used the Geographic Random Forest (GRF) to model and predict functional traits across our plots. We demonstrate that key plant functional traits can be accurately predicted across the tropicsusing the high spatial and spectral resolution of Sentinel-2 imagery in conjunction with climatic and soil information. Image textural parameters were found to be key components of remote sensing information for predicting functional traits across tropical forests and woody savannas. Leaf thickness (R² = 0.52) obtained the highest prediction accuracy among the morphological and structural traits and leaf carbon content (R² = 0.70) and maximum rates of photosynthesis (R² = 0.67) obtained the highest prediction accuracy for leaf chemistry and photosynthesis related traits, respectively. Overall, the highest prediction accuracy was obtained for leaf chemistry and photosynthetic traits in comparison to morphological and structural traits. Our approach offers new opportunities for mapping, monitoring and understanding biodiversity and ecosystem change in the most species-rich ecosystems on Earth.
Why it matches plant phenotyping methodsSentinel-2画像とGRFを用いて樹冠の機能形質を推定・予測し、予測精度も評価しており、植物形質取得・抽出手法が研究の中心である。
abstractThis study aimed to track key functional traits by coupling Sentinel-2 derived variables with a unique data set of precisely located in-situ measurements of canopy functional traits
The ESA's forthcoming FLuorescence EXplorer (FLEX) mission is dedicated to the global monitoring of the vegetation's chlorophyll fluorescence by means of an imaging spectrometer, FLORIS. In order to properly interpret the fluorescence signal in relation to photosynthetic activity, essential vegetation variables need to be retrieved concomitantly. FLEX will fly in tandem with Sentinel-3 (S3), which conveys the Ocean and Land Colour Instrument (OLCI) that is designed to characterize the atmosphere and the terrestrial vegetation at a spatial resolution of 300 m. In this work we present the retrieval models of four essential biophysical variables: (1) Leaf Area Index (LAI), (2) leaf chlorophyll content (Cab), (3) fraction of absorbed photosynthetically active radiation (fAPAR), and (4) fractional vegetation cover (FCover). These variables can be operationally inferred by hybrid retrieval approaches, which combine the generalization capabilities offered by radiative transfer models (RTMs) with the flexibility and computational efficiency of machine learning methods. The RTM SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) was used to generate a database of reflectance spectra corresponding to a large variety of canopy realizations, which served subsequently as input to train a Gaussian Process Regression (GPR) algorithm for each targeted variable. Three sets of GPR models were developed, based on different spectral band settings: (1) OLCI (21 bands between 400 and 1040 nm), (2) FLORIS (281 bands between 500 and 780 nm), and (3) their synergy. Their respective performances were assessed based on simulated reflectance scenes. Regarding the retrieval of Cab, the OLCI model gave good model performances (R 2 : 0.91; RMSE: 7.6 μg. cm -2 ), yet superior accuracies were achieved as a result of FLORIS' higher spectral resolution (R 2 : 0.96; RMSE: 4.8 μg . cm -2 ). The synergy of both datasets did not further enhance the variable retrieval. Regarding LAI, the improvement of the model performances by using only FLORIS spectra (R 2 : 0.87; RMSE: 1.05 m 2 .m -2 ) rather than only OLCI spectra (R 2 : 0.86; RMSE: 1.12 m 2 .m -2 ) was less evident but merging both data sets was more beneficial (R 2 : 0.88; RMSE: 1.01 m 2 .m -2 ). Finally, the three data sources gave good model performances for the retrieval of fAPAR and Fcover, with the best performing model being the Synergy model (fAPAR: R 2 : 0.99; RMSE: 0.02 and FCover: R 2 : 0.98; RMSE: 0.04). The ability of the models to process real data was subsequently demonstrated by applying the OLCI models to S3 surface reflectance products acquired over Western Europe and Argentina. Obtained maps showed consistent patterns and variable ranges, and comparison against corresponding Sentinel-2 products (coarsened to a 300 m spatial resolution) led to reasonable matches (R 2 : 0.5-0.7). Altogether, given the availability of the multiple data sources, the FLEX tandem mission will foster unique opportunities to quantify essential vegetation properties, and hence facilitate the interpretation of the measured fluorescence levels.
Why it matches plant phenotyping methods衛星センサーと機械学習を組み合わせ、LAI、葉緑素量、fAPAR、植生被覆率という植物形質を推定するモデルを開発・評価しており、植物フェノタイピング手法が研究の中心である。
abstractIn this work we present the retrieval models of four essential biophysical variables: (1) Leaf Area Index (LAI), (2) leaf chlorophyll content (Cab), (3) fraction of absorbed photosynthetically active radiation (fAPAR), and (4) fractional vegetation cover (FCover).
Rangelands cover 70% of the world's land surface, and provide critical ecosystem services of primary production, soil carbon storage, and nutrient cycling. These ecosystem services are governed by very fine-scale spatial patterning of soil carbon, nutrients, and plant species at the centimeter-to-meter scales, a phenomenon known as “islands of fertility”. Such fine-scale dynamics are challenging to detect with most satellite and manned airborne platforms. Remote sensing from unmanned aerial vehicles (UAVs) provides an alternative option for detecting fine-scale soil nutrient and plant species changes in rangelands tn0020 smaller extents. We demonstrate that a model incorporating the fusion of UAV multispectral and structure-from-motion photogrammetry classifies plant functional types and bare soil cover with an overall accuracy of 95% in rangelands degraded by shrub encroachment and disturbed by fire. We further demonstrate that employing UAV hyperspectral and LiDAR fusion greatly improves upon these results by classifying 9 different plant species and soil fertility microsite types (SFMT) with an overall accuracy of 87%. Among them, creosote bush and black grama, the most important native species in the rangeland, have the highest producer's accuracies at 98% and 94%, respectively. The integration of UAV LiDAR-derived plant height differences was critical in these improvements. Finally, we use synthesis of the UAV datasets with ground-based LiDAR surveys and lab characterization of soils to estimate that the burned rangeland potentially lost 1474 kg/ha of C and 113 kg/ha of N owing to soil erosion processes during the first year after a prescribed fire. However, during the second-year post-fire, grass and plant-interspace SFMT functioned as net sinks for sediment and nutrients and gained approximately 175 kg/ha C and 14 kg/ha N, combined. These results provide important site-specific insight that is relevant to the 423 Mha of grasslands and shrublands that are burned globally each year. While fire, and specifically post-fire erosion, can degrade some rangelands, post-fire plant-soil-nutrient dynamics might provide a competitive advantage to grasses in rangelands degraded by shrub encroachment. These novel UAV and ground-based LiDAR remote sensing approaches thus provide important details towards more accurate accounting of the carbon and nutrients in the soil surface of rangelands.
Why it matches plant phenotyping methodsUAVマルチスペクトル・ハイパースペクトル・LiDARと地上LiDARを融合し、植物機能型・種・植物高を空間的に推定する手法が研究の主要な技術的貢献であるため、植物フェノタイピング手法として採用する。
abstractWe demonstrate that a model incorporating the fusion of UAV multispectral and structure-from-motion photogrammetry classifies plant functional types and bare soil cover with an overall accuracy of 95%
Radar data at C-band has shown great potential for the monitoring of soil and canopy hydric conditions of wheat crops. In this study, the C-band Sentinel-1 time series including the backscattering coefficients σ⁰ at VV and VH polarization, the polarization ratio (PR) and the interferometric coherence ρ are first analyzed with the support of experimental data gathered on three plots of irrigated winter wheat located in the Haouz plain in the center of Morocco covering five growing seasons. The results showed that ρ and PR are tightly related to the canopy development. ρ is also sensitive to soil preparation. By contrast, σ⁰ was found to be widely linked to changes in surface soil moisture (SSM) during the first growth stages when Leaf Area Index remains moderate (<1.5 m²/m²). In addition, drastic changes in the crop geometry associated to heading had a strong impact on the C-band σ⁰, in particular for VH polarization. The coupled water cloud and Oh models (WCM) were then calibrated and validated on the study sites. The comparison between the predicted and observed σ⁰ yielded a root mean square error (RMSE) values ranging from 1.50 dB to 2.02 dB for VV and between 1.74 dB to 2.52 dB for VH with significant differences occurring in the second part of the season after heading. Finally, new approaches based on the inversion of the WCM for SSM retrieval over wheat fields were proposed using Sentinel-1 radar data only. To this objective, the dry above-ground biomass (AGB) and the vegetation water content (VWC) were retrieved from the interferometric coherence and the PR. The relationships were then used as the vegetation descriptor in the WCM. The best retrieval results were obtained using the relationship between ρVV and the AGB (R and RMSE of 0.82, 0.05 m³/m³ respectively and no bias). The new retrieval approaches were then applied to a large database covering a rainfed field in Morocco and 18 plots of rainfed and irrigated wheat of the Kairouan plain (Tunisia) and compared to other classical techniques of SSM retrieval including simple linear relationships between SSM and σ⁰. The method based on the WCM and the ρVV-AGB relationships also provided with slightly better results than the others on the validation database (r = 0.75, RMSE = 0.06 m³/m³ and bias = 0.01 m³/m³ over the 18 plots of Tunisia) but the simple linear relationships performed also reasonably well (r = 0.62, RMSE = 0.07, bias = −0.01 in Tunisia for instance). This study opens perspectives for high resolution soil moisture mapping from Sentinel-1 data over south Mediterranean wheat crops and in fine, for irrigation scheduling and retrieval through the assimilation of these new products in an evapotranspiration model.
Why it matches plant phenotyping methodsSentinel-1データから小麦のAGB・VWCなどの植物形質を推定する手法を開発し、WCMを較正・検証しているため、植物形質取得法が中心的です。
abstractThe coupled water cloud and Oh models (WCM) were then calibrated and validated on the study sites.
For agricultural applications, identification of non-photosynthetic above-ground vegetation is of great interest as it contributes to assess harvest practices, detecting crop residues or drought events, as well as to better predict the carbon, water and nutrients uptake. While the mapping of green Leaf Area Index (LAI) is well established, current operational retrieval models are not calibrated for LAI estimation over senescent, brown vegetation. This not only leads to an underestimation of LAI when crops are ripening, but is also a missed monitoring opportunity. The high spatial and temporal resolution of Sentinel-2 (S2) satellites constellation offers the possibility to estimate brown LAI (LAI G ) next to green LAI (LAI G ). By using LAI ground measurements from multiple campaigns associated with airborne or satellite spectra, Gaussian processes regression (GPR) models were developed for both LAI G and LAI B , providing alongside associated uncertainty estimates, which allows to mask out unreliable LAI retrievals with higher uncertainties. A processing chain was implemented to apply both models to S2 images, generating a multiband LAI product at 20 m spatial resolution. The models were adequately validated with in-situ data from various European study sites (LAI G : R 2 = 0.7, RMSE = 0.67 m 2 /m 2 ; LAI B : R 2 = 0.62, RMSE = 0.43 m 2 /m 2 ). Thanks to the S2 bands in the red edge (B5: 705 nm and B6: 740 nm) and in the shortwave infrared (B12: 2190 nm) a distinction between LAI G and LAI B can be achieved. To demonstrate the capability of LAI B to identify when crops start senescing, S2 time series were processed over multiple European study sites and seasonal maps were produced, which show the onset of crop senescence after the green vegetation peak. Particularly, the LAI B product permits the detection of harvest (i.e., sudden drop in LAI B ) and the determination of crop residues (i.e., remaining LAI B ), although a better spectral sampling in the shortwave infrared would have been desirable to disentangle brown LAI from soil variability and its perturbing effects. Finally, a single total LAI product was created by merging LAI G and LAI B estimates, and then compared to the LAI derived from S2 L2B biophysical processor integrated in SNAP. The spatiotemporal analysis results confirmed the improvement of the proposed descriptors with respect to the standard SNAP LAI product accounting only for photosynthetically active green vegetation.
Why it matches plant phenotyping methodsSentinel-2データから緑色・褐色LAIを推定するGPRモデルと処理チェーンを開発し、現地データで検証した植物形質推定手法が中心である。
abstractA processing chain was implemented to apply both models to S2 images, generating a multiband LAI product at 20 m spatial resolution.
The ability to accurately detect and quantify the presence of invasive plants is integral in their management, treatment, and removal. Remotely piloted aircraft systems (RPASs) are becoming an important remote sensing tool for mapping invasive plants. Spotted knapweed (Centaurea maculosa) is highly invasive in North America. This study developed and evaluated a novel method for analysis of multispectral data to map the relative cover of spotted knapweed in a heterogeneous grassland community. The method developed in this work, termed metapixel-based image analysis, segments the image into a grid of metapixels for which grey level co-occurrence matrix (GLCM)-based statistics can be computed as descriptive features. Using RPAS-acquired multispectral imagery and plant species inventories performed on 1m² quadrats, a random forest classifier was trained to predict the qualitative degree of spotted knapweed ground cover within each metapixel. The best mean cross-validation score achieved was 71.3% when describing relative ground cover of spotted knapweed, with an accuracy of 66.0% when applied to an independent validation dataset. Analysis of the performance of metapixel-based image analysis on this study site suggests that feature optimization, including feature subset selection, and the use of GLCM-based texture features is of critical importance for achieving an accurate classification.
Why it matches plant phenotyping methodsRPASマルチスペクトル画像から植物種の相対被覆度を推定する画像解析法を開発し、独立データで検証しており、植物状態の取得方法が中心的です。
abstractThis study developed and evaluated a novel method for analysis of multispectral data to map the relative cover of spotted knapweed in a heterogeneous grassland community.
Fire severity assessment is crucial for predicting ecosystem response and prioritizing post-fire forest management strategies. Although a variety of remote sensing approaches have been developed, more research is still needed to improve the accuracy and effectiveness of fire severity mapping. This study proposes a unitemporal simulation approach based on the generation of synthetic spectral databases from linear spectral mixing. To fully exploit the potential of these training databases, the Random Forest (RF) machine learning algorithm was applied to build a classifier and regression model. The predictive models parameterized with the synthetic datasets were applied in a case study, the Sierra de Luna wildfire in Spain. Single date Landsat-8 and Sentinel-2A imagery of the immediate post-fire environment were used to develop the validation spectral datasets and a Pléiades orthoimage, providing the ground truth data. The four defined severity categories – unburned (UB), partial canopy unburned (PCU), canopy scorched (CS), and canopy consumed (CC) – demonstrated high accuracy in the bootstrapped (about 95%) and real validation sets (about 90%), with a slightly better performance observed when the Sentinel-2A dataset was used. Abundance of four ground covers (green vegetation, non-photosynthetic vegetation, soil, and ash) was also quantified with moderate (~45% for NPV) or high accuracy (higher than 75% for the remaining covers). No specific pattern in the comparison of sensors was observed. Variable importance analysis highlighted the complementary behavior of the spectral bands, although the contrast between the near and shortwave infrared regions stood out above the rest. Comparison of procedures reinforced the usefulness of the approach, as RF image-derived models and the multiple endmember spectral unmixing technique (MESMA) showed lower accuracy. The capabilities for detailed mapping are reflected in the development of different types of cartography (classification maps and fraction cover maps). The approach holds great potential for fire severity assessment, and future research needs to extend the predictive modeling to other burned areas – also in different ecosystems – and analyze its competence and the possible adaptations needed.
Why it matches plant phenotyping methods森林植生の焼損状態・被覆量という植物群落の状態を、合成スペクトルデータ、Random Forest、衛星画像で推定する手法を開発・検証しており、植物状態の取得・抽出が中心的です。
abstractThis study proposes a unitemporal simulation approach based on the generation of synthetic spectral databases from linear spectral mixing.
The phenological dynamics of crops reflect the response and feedback of agricultural systems to climate and environmental constraints, and have significant controls on carbon and nutrient cycling across the globe. Remote monitoring of crop phenological dynamics in a consistent and systematic manner is vitally crucial for optimizing the farm management activities and evaluating the agricultural resilience to extreme weather conditions and future climate change. Yet our ability to retrieve crop growing stages with satellite time series is limited. The remotely sensed phenological transition dates may not be characteristic of crop physiological growing stages. The objective of this study is to develop a remote sensing phenological monitoring framework that can reconcile satellite-based phenological measures with ground-based crop growing observations, with corn and soybean in Illinois as a case study. The framework comprises three key components: time series phenological pre-processing, time series phenological modeling, and time series phenological characterization. As an exploratory prototype, the framework retrieved a total of 56 phenological transition dates that were subsequently evaluated with the district-level ground phenological observations. The results indicated that the devised framework can adequately retrieve a wide range of physiological growing stages for corn and soybean in Illinois, with R square greater than 0.6 and RMSE less than 1 week for most stages. The devised framework largely extends the limited satellite phenological measures to a range of phenological transition dates that are characteristic of essential crop growing stages. It paves the way for formulating standard crop phenological monitoring protocols via remote sensing. The wealth of retrieved phenological characteristics open up unique opportunities to enhance our understanding of the complex mechanisms underlying the crop growth in response to varying environmental stresses, and to make more adaptive farm management strategies towards sustained agricultural development.
Why it matches plant phenotyping methods衛星時系列からトウモロコシ・ダイズの生理的生育段階を抽出するリモートセンシング枠組みを開発し、地上観測で評価しており、植物状態の取得手法が中心である。
abstractThe objective of this study is to develop a remote sensing phenological monitoring framework that can reconcile satellite-based phenological measures with ground-based crop growing observations
The prediction of carbon uptake by forests across fertility gradients requires accurate characterisation of how biochemical limitations to photosynthesis respond to variation in key elements such as nitrogen (N) and phosphorus (P). Over the last decade, proxies for chlorophyll and photosynthetic activity have been extracted from hyperspectral imagery and used to predict important photosynthetic variables such as the maximal rate of carboxylation (Vcₘₐₓ) and electron transport (Jₘₐₓ). However, little research has investigated the generality of these relationships within the nitrogen (N) and phosphorus (P) limiting phases, which are characterised by mass based foliage ratios of N:P ≤ 10 for N limitations and N:P > 10 for P limitations.Using measurements obtained from one year old Pinus radiata D. Don grown under a factorial range of N and P treatments this research examined relationships between photosynthetic capacity (Vcₘₐₓ, Jₘₐₓ) and measured N, P and chlorophyll (Chlₐ₊b). Using functional traits quantified from hyperspectral imagery we then examined the strength and generality of relationships between photosynthetic variables and Photochemical Reflectance Index (PRI), Sun-Induced Chlorophyll Fluorescence (SIF) and chlorophyll a + b derived by radiative transfer model inversion.There were significant (P < .001) and strong relationships between photosynthetic variables and both N (R² = 0.82 for Vcₘₐₓ; R² = 0.87 for Jₘₐₓ) and Chlₐ₊b (R² = 0.85 for Vcₘₐₓ; R² = 0.86 for Jₘₐₓ) within the N limiting phase that were weak (R² < 0.02) and insignificant within the P limiting phase. Similarly, there were significant (P < .05) positive relationships between P and photosynthetic variables (R² = 0.50 for Vcₘₐₓ; R² = 0.58 for Jₘₐₓ) within the P limiting phase that were insignificant and weak (R² < 0.33) within the N limiting phase.Predictions of photosynthetic variables using Chlₐ₊b estimated by model inversion were significant (P < .001), positive and strong (R² = 0.64 for Vcₘₐₓ; R² = 0.63 for Jₘₐₓ) within the N limiting phase but insignificant and weak (R² < 0.05) within the P limiting phase. In contrast, both SIF and PRI exhibited moderate to strong positive correlations with photosynthetic variables within both the N and P limiting phases. These results suggest that quantified SIF and PRI from hyperspectral images may have greater generality in predicting biochemical limitations to photosynthesis than proxies for N and chlorophyll a + b, particularly under high foliage N content, when P is limiting.
Why it matches plant phenotyping methodsハイパースペクトル画像からPRI、SIF、クロロフィルなどの植物機能形質を定量し、光合成能力を予測・評価することが研究の中心であり、植物フェノタイピング手法の実質的な適用・検証に該当する。
abstractUsing functional traits quantified from hyperspectral imagery we then examined the strength and generality of relationships between photosynthetic variables and Photochemical Reflectance Index (PRI), Sun-Induced Chlorophyll Fluorescence (SIF) and chlorophyll a + b derived by radiative transfer model inversion.
The measurement of chlorophyll fluorescence in remote way represents a tool that is becoming increasingly important in relation to the diagnosis of plant health and carbon budget on the planet. However, the detection of this emission is severely affected by distortions, due to processes of light re-absorption both in the leaf and in the canopy. Even though some advances have been made to correct the signal in the far-red, the whole spectral range needs to be addressed, in order to accurately assess plant physiological state. In 2018, we introduced a model to obtain fluorescence spectra at leaf level, from what was observed at canopy level. In this present work, we publish a revision of that physical model, with a more rigorous and exact mathematical treatment. In addition, multiple scattering between the soil and the canopy, and the fraction of land covered by vegetation have also been taken into consideration. We validate this model upon experimental measures, in three types of crops of agronomic interest (Pea, Rye grass and Maize) with different architecture. Our model accurately predicts both the shape of fluorescence spectra at leaf level from that measured at canopy level and the fluorescence ratio. Furthermore, not only do we eliminate artifacts affecting the spectral shape, but we are also able to calculate the quantum yield of fluorescence corrected for re-absorption, from the experimental quantum yield at canopy level. This represents an advance in the study of these systems because it offers the opportunity to make corrections for both the fluorescence ratio and the intensity of the observed fluorescence.
Why it matches plant phenotyping methodsキャノピー蛍光から葉レベルの蛍光スペクトルと量子収率を推定・補正する物理モデルを改良し、複数作物で実測値により検証しており、植物生理状態の取得手法が研究の中心である。
abstractIn this present work, we publish a revision of that physical model, with a more rigorous and exact mathematical treatment.
Drones offer entirely new prospects for precision agriculture. This study investigates the utilisation of drone remote sensing for managing and monitoring silage grass swards. In northern countries, grass swards are fertilised and harvested three times per season when aiming to maximise the yield. Information about the grass quantity and quality is necessary to optimise these operations. Our objectives were to investigate and develop machine-learning techniques for estimating these parameters using drone photogrammetry and spectral imaging. Trial sites were established in southern Finland for the primary growth and regrowth of grass in the summer of 2017. Remote-sensing datasets were captured four times during the primary growth season and three times during the regrowth period. Reference measurements included fresh and dry biomass and several quality parameters, such as the digestibility of organic matter in dry matter (the D-value), neutral detergent fibre (NDF), indigestible neutral detergent fibre (iNDF), water-soluble carbohydrates (WSC), the nitrogen concentration (Ncont) in dry matter (DM) and nitrogen uptake (NU). Machine-learning estimators based on random forest (RF) and multiple linear regression (MLR) methods were trained using the reference measurements and tested using independent test datasets. The best results for the biomass estimation, nitrogen amount and digestibility were obtained when using hyperspectral and 3D data, followed by the combination of multispectral and 3D data. During the training process, the best normalised root-mean-square errors (RMSE%) were 14.66% for the dry biomass and 12% for fresh biomass; the best RMSE% values for NU, the D-value and NDF were 13.6%, 1.98% and 3% respectively. For the primary growth, the accuracies of all quality parameters were better than 20% with the independent test datasets; for the regrowth, the estimation accuracies of the D-value, iNDF, NDF, Ncont and NU were better than 20%. The results showed that drone remote sensing was an excellent tool for the efficient and accurate management of silage production.
Why it matches plant phenotyping methodsドローン画像分光・写真測量と機械学習により、牧草のバイオマスおよび品質形質を推定する手法を開発・検証しており、植物形質取得が中心である。
abstractOur objectives were to investigate and develop machine-learning techniques for estimating these parameters using drone photogrammetry and spectral imaging.
Terrestrial Laser Scanning (TLS) has been used during the past decade to capture the complexity of 3D forest canopy structures, especially Leaf or Plant Area Density (LAD/PAD). TLS data, i.e. point cloud, can be divided into voxels to estimate the three-dimensional distribution of LAD/PAD. However, the combination effects of vegetation occlusion and shooting pattern of TLS scanners lead to a highly heterogeneous sampling, which limits the reliability of some local estimates, since several voxels are either not explored or poorly-explored by laser beams. In practice, recommendations vary regarding the minimum number of beams crossing voxels or the minimum path lengths required to provide reliable predictions. In addition, assigning a value to non-explored and poorly-explored voxels is still an open question.The present work proposes a new method, called LAD-kriging, to mitigate the impact of non-uniform sampling and to increase the accuracy of LAD estimates in non-explored and poorly-explored voxels. The method takes advantage of i) an unbiased LAD estimator of known variance, which was recently developed; ii) the spatial correlation of the LAD field, which derives from vegetation clumping. LAD-kriging computes kriging weights from mathematical derivations, which takes into account both spatial dependencies in the LAD field and the reliability of the estimate available in each voxel. It was evaluated through numerical experiments, which enabled to validate the algorithm and to evaluate its performance through comparison with true references. An example application to field data shows that such spatial correlations truly exist in the field and that LAD-kriging entails to reduce sampling errors (with respect to full resolution scanning). Although a real validation is impossible due to the lack of precise references at the voxel scale, this example allows to gain confidence for its application to field data.In our realistic numerical experiment, up to 25–30% of voxels can be explored by <100 beams, thereby leading to unreliable estimates of the LAD within these voxels and frequent large local errors. In cases where voxels were not explored at all, simple methods previously reported in earlier studies, such as ignoring occlusion or assigning “mean layer” values to unexplored voxels were inefficient (RMSE = 0.72 m⁻¹ and poor local accuracy). By contrast, LAD-kriging exhibited the smallest errors (RMSE = 0.48 m⁻¹) and was locally more accurate. LAD-Kriging was also efficient for correcting voxels explored with few beams, i.e. unreliable, dropping RMSE from 0.92 to 0.42 m⁻¹ in these volumes. In practice, LAD-Kriging does not require defining a reliability threshold or any arbitrary parameterization, which is convenient for field applications and can be extended to other applications than LAD with TLS, such as UAS/VAS scanning, provided that unbiased estimators of known variance are available.
Why it matches plant phenotyping methodsTLSデータから植物の葉面積密度を推定するLAD-kriging法を開発し、数値実験と圃場データで精度評価しており、植物形態計測手法が研究の中心です。
abstractThe present work proposes a new method, called LAD-kriging, to mitigate the impact of non-uniform sampling and to increase the accuracy of LAD estimates in non-explored and poorly-explored voxels.
Woodland ecosystems, dominant on nearly 4% of all terrestrial land globally, are faced with a variety of threats, including increasingly prolonged and severe droughts, invasive insect outbreaks, and the rapid spread of pathogens. While many remote sensing methods have been developed for the detection and quantification of mortality in forested environments, woodland ecosystems present unique challenges to accurately mapping tree die-off due to relatively lower canopy covers, smaller and irregularly-shaped tree crowns, and greater influence of understory vegetation and soil cover on reflectance. To address these challenges, we developed a multi-sensor, multi-scale approach combining the analytical strengths of centimeter-resolution unmanned aerial system imagery for interpreting individual tree-level mortality, airborne lidar for crown mapping and quantifying percent canopy mortality, and Landsat imagery for upscaling mortality estimates to a regional scale. This approach utilizes a new algorithm for delineating the shapes of small, irregular woodland tree crowns using lidar. To demonstrate the application of this method, we map the extent and severity of a recent tree mortality event in piñon-juniper (PJ) woodlands of southeastern Utah. Our results suggest that 39% of PJ in this region has experienced some level of mortality, with patches exceeding 50% mortality. An analysis of potential mortality drivers revealed that canopy cover, terrain, and recent winter precipitation conditions are most directly linked with mortality, although the explanatory power of the mortality driver model was low. Our approach demonstrates a methodology that could be used for tree mortality mapping and scaling in a variety woodland ecosystems, and can provide a strong basis for further ecophysiological, ecological, and carbon cycle studies involving woodland tree mortality.
Why it matches plant phenotyping methods複数センサーと新規アルゴリズムにより、個体・樹冠レベルの樹木枯死状態とその重症度を抽出・スケール化する方法が研究の中心である。
abstractwe developed a multi-sensor, multi-scale approach combining the analytical strengths of centimeter-resolution unmanned aerial system imagery for interpreting individual tree-level mortality, airborne lidar for crown mapping and quantifying percent canopy mortality, and Landsat imagery for upscaling mortality estimates to a regional scale.
Warming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R² = 0.81, RMSE = 1.09 kg m⁻²) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R² = 0.74, RMSE = 1.26 kg m⁻²), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts. m⁻²) to the G-LiHT SfM point cloud (500–2000 pts. m⁻²) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass.
Why it matches plant phenotyping methodsSfMとLiDARを用いて植生の構造・バイオマスという植物形質を景観規模で推定し、複数センサー、解像度、モデル性能を比較・検証しているため、方法が中心的である。
abstractThe goal of this research was to test methods for landscape-scale mapping of tall shrub biomass
Generalized assessments of the accuracy of spectroscopic estimates of ecologically important leaf traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) are still lacking for most ecosystems, and particularly for non-forested and/or seasonally dry tropical vegetation. Here, we tested the ability of using leaf reflectance spectra to estimate LMA and LDMC and classify plant growth forms within the cerrado and campo rupestre seasonally dry non-forest vegetation types of Southeastern Brazil, filling an existing gap in published assessments of leaf optical properties and plant traits in such environments. We measured leaf reflectance spectra from 1648 individual plants comprising grasses, herbs, shrubs, and trees, developed partial least squares regression (PLSR) models linking LMA and LDMC to leaf spectra (400–2500 nm), and identified the spectral regions with the greatest discriminatory power among growth forms using Bhattacharyya distances. We accurately predicted leaf functional traits and identified different growth forms. LMA was overall more accurately predicted (RMSE = 8.58%) than LDMC (RMSE = 9.75%). Our model including all sampled plants was not biased towards any particular growth form, but growth-form specific models yielded higher accuracies and showed that leaf traits from woody plants can be more accurately estimated than for grasses and forbs, independently of the trait measured. We observed a large range of LMA values (31.80–620.81 g/m²) rarely observed in tropical or temperate forests, and demonstrated that values above 300 g/m² could not be accurately estimated. Our results suggest that spectroscopy may have an intrinsic saturation point, and/or that PLSR, the current approach of choice for estimating traits from plant spectra, is not able to model the entire range of LMA values. This finding has very important implications to our ability to use field, airborne, and orbital spectroscopic methods to derive generalizable functional information. We thus highlight the need for increasing spectroscopic sampling and research efforts in drier non-forested environments, where environmental pressures lead to leaf adaptations and allocation strategies that are very different from forested ecosystems. Our findings also confirm that leaf reflectance spectra can provide important information regarding differences in leaf metabolism, structure, and chemical composition. Such information enabled us to accurately discriminate plant growth forms in these environments regardless of lack of variation in leaf economic traits, encouraging further adoption of remote sensing methods by ecologists and allowing a more comprehensive assessment of plant functional diversity.
Why it matches plant phenotyping methods葉の反射スペクトルからLMA・LDMCを推定する分光フェノタイピング手法を開発・評価し、PLSRの精度、成長形別性能、限界を検証しているため、方法が研究の中心である。
abstractWe measured leaf reflectance spectra from 1648 individual plants comprising grasses, herbs, shrubs, and trees, developed partial least squares regression (PLSR) models linking LMA and LDMC to leaf spectra (400–2500 nm)
Nitrogen (N) is considered as one of the most important plant macronutrients and proper management of N therefore is a pre-requisite for modern agriculture. Continuous satellite-based monitoring of this key plant trait would help to understand individual crop N use efficiency and thus would enable site-specific N management. Since hyperspectral imaging sensors could provide detailed measurements of spectral signatures corresponding to the optical activity of chemical constituents, they have a theoretical advantage over multi-spectral sensing for the detection of crop N. The current study aims to provide a state-of-the-art overview of crop N retrieval methods from hyperspectral data in the agricultural sector and in the context of future satellite imaging spectroscopy missions. Over 400 studies were reviewed for this purpose, identifying those estimating mass-based N (N concentration, N%) and area-based N (N content, N area ) using hyperspectral remote sensing data. Retrieval methods of the 125 studies selected in this review can be grouped into: (1) parametric regression methods, (2) linear nonparametric regression methods or chemometrics, (3) nonlinear nonparametric regression methods or machine learning regression algorithms, (4) physically-based or radiative transfer models (RTM), (5) use of alternative data sources (sun-induced fluorescence, SIF) and (6) hybrid or combined techniques. Whereas in the last decades methods for estimation of N area and N% from hyperspectral data have been mainly based on simple parametric regression algorithms, such as narrowband vegetation indices, there is an increasing trend of using machine learning, RTM and hybrid techniques. Within plants, N is invested in proteins and chlorophylls stored in the leaf cells, with the proteins being the major nitrogen-containing biochemical constituent. However, in most studies, the relationship between N and chlorophyll content was used to estimate crop N, focusing on the visible-near infrared (VNIR) spectral domains, and thus neglecting protein-related N and reallocation of nitrogen to non-photosynthetic compartments. Therefore, we recommend exploiting the estimation of nitrogen via the proxy of proteins using hyperspectral data and in particular the short-wave infrared (SWIR) spectral domain. We further strongly encourage a standardization of nitrogen terminology, distinguishing between N% and N area . Moreover, the exploitation of physically-based approaches is highly recommended combined with machine learning regression algorithms, which represents an interesting perspective for future research in view of new spaceborne imaging spectroscopy sensors.
Why it matches plant phenotyping methods作物の窒素含量・窒素濃度という植物形質を、ハイパースペクトルデータから推定する手法を体系的にレビューしており、フェノタイピング手法が中心です。
abstractThe current study aims to provide a state-of-the-art overview of crop N retrieval methods from hyperspectral data in the agricultural sector and in the context of future satellite imaging spectroscopy missions.
Crop lodging assessment is essential for evaluating yield damage and informing crop management decisions for sustainable agricultural production. While a few studies have demonstrated the potential of optical and SAR data for crop lodging assessment, large-scale crop lodging assessment has been hampered by the unavailability of dense satellite time series data. The unprecedented availability of free Sentinel-1 and Sentinel-2 data may provide a basis for operational detection and monitoring of crop lodging. In this context, this study aims to understand the effect of lodging on backscatter/coherence and spectral reflectance derived from Sentinel-1 and Sentinel-2 data and to detect lodging incidence in wheat using time-series analysis. Crop biophysical parameters were measured in the field for both healthy and lodged plots from March to June 2018 in a study site in Ferrara, Italy, and the corresponding Sentinel images were downloaded and processed. The lodged plots were further categorised into different lodging severity classes (moderate, severe and very severe). Temporal profiles of backscatter, coherence, reflectance and continuum removed spectra were studied for healthy and lodging severity classes throughout the stem elongation to ripening growth stages. The Kruskal Wallis and posthoc Tukey tests were used to test for significant differences between different classes. Our results for Sentinel-2 showed that red edge (740 nm) and NIR (865 nm) bands could best distinguish healthy from lodged wheat (particularly healthy and very severe). For Sentinel-1, the analysis revealed the potential of VH backscatter and the complementarity of VV and VH/VV backscatter in distinguishing a maximum number of classes. Our findings demonstrate the potential of Sentinel data for near real-time detection of the incidence and severity of lodging in wheat. To the best of our knowledge, there is no study that has contributed to this application.
Why it matches plant phenotyping methodsSentinel-1/2の時系列センサーデータからコムギの倒伏発生と重症度を推定する手法が研究の中心であり、圃場測定との対応や重症度クラスの識別を評価している。
abstractto detect lodging incidence in wheat using time-series analysis
Mangrove ecosystems are targeted for many conservation and rehabilitation efforts due to their ability to store large amounts of carbon in their living biomass and soil. Traditional methods to monitor above-ground biomass (AGB) rely on on-ground measurements, which are expensive, labour intensive and cover small spatial scales. Structure from Motion and Multi-View Stereo reconstructions from Unmanned Aerial Vehicles imagery (UAV-SfM) have the potential to increase fieldwork efficiency by providing a greater amount of spatial information in less time. However, there is still a need to assess the ability of UAV-SfM to retrieve structural information of mangrove forests, which could pose challenges in areas of high forest complexity and density.In this study we successfully used UAV-SfM data to estimate height, canopy diameter and AGB of natural and rehabilitated mangrove forests across two regions of the southeastern coast of Australia. We used a variable window filter algorithm to detect trees with an 80% detection rate when considering the top canopy. Individual tree canopy segmentation was performed using a marker-controlled watershed segmentation with two sets of constraining markers: treetops and a minimum height below which a pixel is not considered part of a tree.Direct comparison with on-ground measurements at the regional level showed no significant difference in tree height and AGB medians when only top canopy was considered. Similarly, median canopy diameters were not significantly different in natural areas of both regions, but significant differences were found in rehabilitated areas. UAV-SfM estimates of AGB were on average 15% lower in natural areas and 10% higher in rehabilitated areas when compared to on-ground measurements and followed a strong linear relationship close to the ideal one-to-one relationship.Additionally, we performed a cost-benefit analysis of the two methodologies. UAV-SfM methods can save almost AU$ 50,000 per ha when compared to on-ground measurements and become cost-effective (based on total costs) after just 15 days of surveys. The methods described in this study open the possibility for easily repeatable, low-cost UAV-SfM surveys for local managers by providing a faster, more cost-effective approach for monitoring mangrove forests over larger areas than traditional on-ground surveys while maintaining forest inventory data accuracy in both natural and rehabilitated mangrove forests.
Why it matches plant phenotyping methodsUAV-SfM画像から樹高、樹冠径、地上部バイオマスを推定し、地上測定と直接比較して検証する手法が研究の中心であるため、植物フェノタイピング手法として採用。
abstractStructure from Motion and Multi-View Stereo reconstructions from Unmanned Aerial Vehicles imagery (UAV-SfM) have the potential to increase fieldwork efficiency by providing a greater amount of spatial information in less time.
Measurements of reflected solar radiation by imaging spectrometers can quantify water in different states (solid, liquid, gas) thanks to the discriminative absorption shapes. We developed a retrieval method to quantify the amount of water in each of the three states from spaceborne imaging spectroscopy data, such as those from the German EnMAP mission. The retrieval couples atmospheric radiative transfer simulations from the MODTRAN5 radiative transfer code to a surface reflectance model based on the Beer-Lambert law. The model is inverted on a per-pixel basis using a maximum likelihood estimation formalism. Based on a unique coupling of the canopy reflectance model HySimCaR and the EnMAP end-to-end simulation tool EeteS, we performed a sensitivity analysis by comparing the retrieved values with the simulation input leading to an R² of 0.991 for water vapor and 0.965 for liquid water. Furthermore, we applied the algorithm to airborne AVIRIS-C data to demonstrate the ability to map snow/ice extent as well as to a CHRIS-PROBA dataset for which concurrent field measurements of canopy water content were available. The comparison between the retrievals and the ground measurements showed an overall R² of 0.80 for multiple crop types and a remarkable clustering in the regression analysis indicating a dependency of the retrieved water content from the physical structure of the vegetation. In addition, the algorithm is able to produce smoother and more physically-plausible water vapor maps than the ones from the band ratio approaches used for multispectral data, since biases due to background reflectance are reduced. The demonstrated potential of imaging spectroscopy to provide accurate quantitative measures of water from space will be further exploited using upcoming spaceborne imaging spectroscopy missions like PRISMA or EnMAP.
Why it matches plant phenotyping methods宇宙搭載イメージング分光データから水状態を定量推定する検索手法を開発し、作物のキャノピー水分量を地上測定と検証しており、植物状態の取得手法が中心である。
abstractWe developed a retrieval method to quantify the amount of water in each of the three states from spaceborne imaging spectroscopy data
The quantification of leaf area index (LAI) is essential for modeling the interaction between atmosphere and biosphere. The airborne LiDAR has emerged as an effective tool for mapping plant area index (PAI) in a landscape consisting of both woody and leaf materials. However, the discrimination between woody and leaf materials and the estimation of effective LAI (eLAI) have, to date, rarely been studied at landscape scale. We applied a voxel matching algorithm to estimate eLAI of deciduous forests using simulated and field LiDAR data under leaf-on and leaf-off conditions. We classified LiDAR points as either a leaf or a woody hit on leaf-on LiDAR data by matching the point with leaf-off data. We compared the eLAI result of our voxel matching algorithm against the subtraction method, where the leaf-off effective woody area index (eWAI) is subtracted from the effective leaf-on PAI (ePAI). Our results, which were validated against terrestrial LiDAR derived eLAI, showed that the voxel matching method, with an optimal voxel size of 0.1 m, produced an unbiased estimation of terrestrial LiDAR derived eLAI with an R² of 0.70 and an RMSE of 0.41 (RRMSE: 20.1%). The subtraction method, however, yielded an R² of 0.62 and an RMSE of 1.02 (RRMSE: 50.1%) with a significant underestimation of 0.94. Reassuringly, the same outcome was observed using a simulated dataset. In addition, we evaluated the performance of 96 LiDAR metrics under leaf-on conditions for eLAI prediction using a statistical model. Based on the importance scores derived from the random forest regression, nine of the 96 leaf-on LiDAR metrics were selected. Cross-validation showed that eLAI could be predicted using these metrics under leaf-on conditions with an R² of 0.73 and an RMSE of 0.27 (RRMSE: 17.4%). The voxel matching method yielded a slightly lower accuracy (R²: 0.70, RMSE:0.41, RRMSE: 20.1%) than the statistical model. We, therefore, suggest that the voxel matching method offers a new opportunity for the estimating eLAI and other ecological applications that require the classification between leaf and woody materials using airborne LiDAR data. It potentially allows transferability to different sites and flight campaigns.
Why it matches plant phenotyping methodsLiDARによる葉面積指数の推定手法を開発し、地上LiDARおよびシミュレーションデータで検証しており、植物形質取得法が研究の中心です。
abstractWe applied a voxel matching algorithm to estimate eLAI of deciduous forests using simulated and field LiDAR data under leaf-on and leaf-off conditions.
Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on-board Sentinel-2A and -2B, the remote sensing community now has access to moderate (10–30 m) spatial resolution imagery with repeat periods of ~3 days in the mid-latitudes. At the same time, the large combined data volume from Landsat 8 and Sentinel-2 introduce substantial new challenges for users. Land surface phenology (LSP) algorithms, which estimate the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide an intuitive way to reduce data volumes and redundancy, while also furnishing data sets that are useful for a wide range of applications including monitoring ecosystem response to climate variability and extreme events, ecosystem modelling, crop-type discrimination, and land cover, land use, and land cover change mapping, among others. To support the need for operational LSP data sets, here we describe a continental-scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel-2 (HLS) imagery. The algorithm creates high quality times series of vegetation indices from HLS imagery, which are then used to estimate the timing of vegetation phenophase transitions at 30 m spatial resolution. We present results from assessment efforts evaluating LSP retrievals, and provide examples illustrating the character and quality of information related to land cover and terrestrial ecosystem properties provided by the continental LSP dataset that we have developed. The algorithm is highly successful in ecosystems with strong seasonal variation in leaf area (e.g., deciduous forests). Conversely, results in evergreen systems are less interpretable and conclusive.
Why it matches plant phenotyping methodsLandsat/Sentinel画像から植生のフェノフェーズ遷移時期を推定するアルゴリズムとデータプロダクトを開発し、取得結果を評価しており、植物状態の抽出手法が中心である。
abstracthere we describe a continental-scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel-2 (HLS) imagery.
To better eliminate the adverse effects of the ground surface on winter wheat Leaf area index (LAI) inversions and to further improve the accuracy of regional winter wheat LAI inversion using SAR remote sensing data, considering the morphological characteristics at different wheat growth stages, a winter wheat LAI inversion model coupled with the microwave scattering model (MSM) for winter wheat at different growth stages (MSMDGS) and the canopy scattering simulation model (CSSM) was proposed. In this research, taking Hengshui City of Huanghuaihai Plain of North China as the study region, using RADARSAT-2 data as image sources and based on parameter sensitivity analysis and model calibration, the proposed model was applied and validated. The LAI inversion results of winter wheat showed that the proposed model had good performance in the regional application and that LAI inversion results with high accuracy could be obtained. Among the three key growth stages (jointing stage, booting stage and heading stage) of winter wheat, the R², adjusted R² and RMSE between the LAI inversion value and the ground-measured data were 0.918, 0.917 and 0.675, respectively, which indicated that the winter wheat LAI inversion model coupled with MSMDGS and CSSM had certain feasibility and applicability.
Why it matches plant phenotyping methodsSARリモートセンシングと散乱・キャノピーシミュレーションモデルを結合し、冬小麦LAIという植物形質を地域推定する手法を提案・較正・検証しており、フェノタイピング手法が中心である。
abstracta winter wheat LAI inversion model coupled with the microwave scattering model (MSM) for winter wheat at different growth stages (MSMDGS) and the canopy scattering simulation model (CSSM) was proposed.
Sun-induced chlorophyll fluorescence (SIF) has been used to track vegetation photosynthetic activity for improving estimation of gross primary productivity (GPP) and detecting plant stress. There are both physical and physiological controls of SIF measured at the surface and retrieved from remote sensing including satellite observations. In order to accurately use SIF for monitoring of plant physiology, the effects of physically-based radiation processes related to leaf and canopy structure, notably photosynthetically active radiation (PAR) absorption and SIF scattering and re-absorption, must be characterized. In this study, we investigate both PAR absorption and SIF scattering processes and find that although it is difficult to quantify their effects individually by using just reflectance, the combined effects of the two processes can be well approximated by a reflectance index. This index, referred to as FCVI (Fluorescence Correction Vegetation Index), is defined as the difference between near-infrared (NIR) and broad-band visible (VIS, 400–700 nm) reflectance acquired under identical sun-canopy-observer geometry of the SIF measurements. The development of the index was based on the physical connection between reflectance and far-red SIF, which was revealed by using the spectral invariant theory. The utility of FCVI to correct far-red SIF for PAR absorption and scattering effects, thus improving the link to photosynthesis, was tested with data from: (i) a field experiment for a growing season; and (ii) a numerical experiment which included a number of scenarios generated by a radiative transfer model. For both the observations and simulations, the FCVI provided a promising estimate of the impact of the physically-based radiation processes on far-red SIF of moderately dense canopies (i.e., FCVI ≥ 0.18). Normalizing the TOC far-red SIF by both the incident PAR (iPAR) and the FCVI provided a good estimate of the far-red fluorescence emission efficiency of the canopies examined. This approach enhances our ability to generalize retrievals for vegetation processes as they change through natural growth phases and seasons. Taken together, far-red SIF and FCVI may enable the assessment of the light partitioning of vegetation canopies, an essential step to facilitate the use of far-red SIF for tracking physiological processes.
Why it matches plant phenotyping methods植物キャノピーの生理状態・光合成関連情報を推定する反射率指数FCVIを開発し、野外観測と放射伝達モデルで有用性を検証しており、フェノタイピング手法が中心的である。
abstractThis index, referred to as FCVI (Fluorescence Correction Vegetation Index), is defined as the difference between near-infrared (NIR) and broad-band visible (VIS, 400–700 nm) reflectance acquired under identical sun-canopy-observer geometry of the SIF measurements.
In this century, one of the main objectives of agriculture is sustainability addressed to achieve food security, based on the improvement of use efficiency of farm resources, the increasing of crop yield and quality, under climate change conditions. The optimization of farm resources, as well as the control of soil degradation processes (e.g., soil erosion), can be realized through crop monitoring in the field, aiming to manage the local spatial variability (time and space) with a high resolution. In the case of high profitability crops, as the case of vineyards for high-quality wines, the capability to manage and follow spatial behavior of plants during the season represents an opportunity to improve farmer incomes and preserve the environmental health. However, any field monitoring represents an additional cost for the farmer, which slows down the objective of a diffuse sustainable agriculture.Satellite multispectral images have been widely used for production management in large areas. However, their observation is limited by the pre-defined and fixed scale with relatively coarse spatial resolution, resulting in limitations in their application.In this paper, encouraged by recent achievements in convolutional neural network (CNN), a multiscale full-connected CNN is constructed for the pan-sharpening of Sentinel-2A images by UAV images. The reconstructed data are validated by independent multispectral UAV images and in-situ spectral measurements. The reconstructed Sentinel-2A images provide a temporal evaluation of plant responses using selected vegetation indices. The proposed methodology has been tested on plant measurements taken either in-vivo and through the retrospective reconstruction of the eco-physiological vine behavior, by the evaluation of water conductivity and water use efficiency indexes from anatomical and isotopic traits recorded in vine trunk wood.In this study, the use of such a methodology able to combine the pro and cons of space-borne and UAVs data to evaluate plant responses, with high spatial and temporal resolution, has been applied in a vineyard of southern Italy by analyzing the period from 2015 to 2018. The obtained results have shown a good correspondence between the vegetation indexes obtained from reconstructed Sentinel-2A data and plant hydraulic traits obtained from tree-ring based retrospective reconstruction of vine eco-physiological behavior.
Why it matches plant phenotyping methods衛星画像とUAV画像を統合するCNNパンシャープニング手法を開発・検証し、植生指数およびブドウ樹の生理・水分関連形質を高い時空間解像度で評価しており、植物表現型取得が中心的である。
abstracta multiscale full-connected CNN is constructed for the pan-sharpening of Sentinel-2A images by UAV images
Leaf area index (LAI) is a key variable for characterizing crop growth conditions and estimating crop productivity. Despite continuing efforts to develop LAI estimation algorithms, LAI datasets still need improvement at spatial and temporal resolutions to meet the requirements of agricultural applications. Advancements in data fusion technique and the emergence of new satellite data provide opportunities for LAI data at higher resolutions in both space and time. In this study, we derived new LAI estimations by leveraging novel satellite remote sensing datasets, STAIR fusion (MODIS-Landsat fusion) and Planet Labs' CubeSat data (through a reprocessed pipeline) for a typical agricultural landscape in the U.S. Corn Belt. The STAIR fused data and our reprocessed CubeSat data have both fine spatial resolutions (30 m and 3.125 m, respectively) and high frequencies (daily for both). To reliably estimate LAI from these advanced satellite datasets, we used two methods: inversion of a radiative transfer model (RTM), and empirical relationship with vegetation index (VI) calibrated from field measured LAI. Compared to the ground-truth LAI collected at 36 sites across the study region, reliable approximations were achieved by both LAI estimations based on PROSAIL RTM (STAIR: R² = 0.69 and root mean squared error (RMSE) = 1.12 (m² m⁻²), CubeSat: R² = 0.76 and RMSE = 1.09 (m² m⁻²)), and LAI estimations based on Green Wide Dynamic Range Vegetation Index (GrWDRVI) (STAIR: R² = 0.75, RMSE = 1.10 (m² m⁻²), CubeSat: R² = 0.76, RMSE = 1.08 (m² m⁻²), where validation ground-truth is independent from calibration data). Newly estimated high-resolution LAI data were aggregated at 500 m resolution and compared with MODIS and VIIRS LAI products, revealing substantial uncertainties and biases in these two products. We also demonstrated phenology stage estimation at fine spatial resolutions based on our high-frequency LAI data. The proposed LAI estimation methods at both high spatial resolution and temporal frequency can be applied to the entire U.S. Corn Belt and provide significant advancement to crop monitoring and precision agriculture.
Why it matches plant phenotyping methods衛星データ融合とRTM・植生指数によるLAI推定手法を開発し、独立した地上LAIで検証しているため、植物形質取得が研究の中心である。
abstractIn this study, we derived new LAI estimations by leveraging novel satellite remote sensing datasets, STAIR fusion (MODIS-Landsat fusion) and Planet Labs' CubeSat data
Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness and growing season length) often termed ‘land surface phenology’, as well as more advanced methods that estimate species-specific phenological stages (e.g., silking stage of maize). Common data processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phenology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multi-scale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization.
Why it matches plant phenotyping methods植物のフェノロジー指標を時系列・マルチスペクトル衛星画像から抽出する手法を中心に扱うレビューであり、植物状態の推定方法、処理、誤差、不確実性を体系的に論じている。
abstractThe goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery.
Preharvest crop yield prediction is critical for grain policy making and food security. Early estimation of yield at field or plot scale also contributes to high-throughput plant phenotyping and precision agriculture. New developments in Unmanned Aerial Vehicle (UAV) platforms and sensor technology facilitate cost-effective data collection through simultaneous multi-sensor/multimodal data collection at very high spatial and spectral resolutions. The objective of this study is to evaluate the power of UAV-based multimodal data fusion using RGB, multispectral and thermal sensors to estimate soybean (Glycine max) grain yield within the framework of Deep Neural Network (DNN). RGB, multispectral, and thermal images were collected using a low-cost multi-sensory UAV from a test site in Columbia, Missouri, USA. Multimodal information, such as canopy spectral, structure, thermal and texture features, was extracted and combined to predict crop grain yield using Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Support Vector Regression (SVR), input-level feature fusion based DNN (DNN-F1) and intermediate-level feature fusion based DNN (DNN-F2). The results can be summarized in three messages: (1) multimodal data fusion improves the yield prediction accuracy and is more adaptable to spatial variations; (2) DNN-based models improve yield prediction model accuracy: the highest accuracy was obtained by DNN-F2 with an R2 of 0.720 and a relative root mean square error (RMSE%) of 15.9%; (3) DNN-based models were less prone to saturation effects, and exhibited more adaptive performance in predicting grain yields across the Dwight, Pana and AG3432 soybean genotypes in our study. Furthermore, DNN-based models demonstrated consistent performance over space with less spatial dependency and variations. This study indicates that multimodal data fusion using low-cost UAV within a DNN framework can provide a relatively accurate and robust estimation of crop yield, and deliver valuable insight for high-throughput phenotyping and crop field management with high spatial precision.
Why it matches plant phenotyping methodsUAVによるRGB・マルチスペクトル・熱画像から大豆の収量形質を推定するデータ融合・深層学習ワークフローを評価しており、表現型取得・推定手法が中心である。
abstractThe objective of this study is to evaluate the power of UAV-based multimodal data fusion using RGB, multispectral and thermal sensors to estimate soybean (Glycine max) grain yield within the framework of Deep Neural Network (DNN).
Rice growth monitoring using Synthetic Aperture Radar (SAR) is recognized as a promising approach for tracking the development of this important crop. Accurate spatio-temporal information of rice inventories is required for water resource management, production risk occurrence, and yield forecasting. This research investigates the potential of the proposed Generalized volume scattering model based Radar Vegetation Index (GRVI) for monitoring rice growth at different phenological stages. The GRVI is derived using the concept of a geodesic distance (GD) between Kennaugh matrices projected on a unit sphere. We utilized this concept of GD to quantify a similarity measure between the observed Kennaugh matrix (representation of observed Polarimetric SAR information) and the Kennaugh matrix of a generalized volume scattering model (a realization of scattering media). The similarity measure is then modulated with a factor estimated from the ratio of the minimum to the maximum GD between the observed Kennaugh matrix and the set of elementary targets: trihedral, cylinder, dihedral, and narrow dihedral. In this work, we utilize a time series of C-band quad-pol RADARSAT-2 observations over a semi-arid region in Vijayawada, India. Among the several rice cultivation practices adopted in this region, we analyze the growth stages of direct seeded rice (DSR) and conventional tansplanted rice (TR) with the GRVI and crop biophysical parameters viz., Plant Area Index – PAI. The GRVI is compared for both rice types against the Radar Vegetation Index (RVI) proposed by Kim and van Zyl. A temporal analysis of the GRVI with crop biophysical parameters at different phenological stages confirms its trend with the plant growth stages. Also, the linear regression analysis confirms that the GRVI outperforms RVI with significant correlations with PAI (r ≥ 0.83 for both DSR and TR). In addition, PAI estimations from GRVI show promising retrieval accuracy with Root Mean Square Error (RMSE) <1.05m²m⁻² and Mean Absolute Error (MAE) <0.85m²m⁻².
Why it matches plant phenotyping methodsSAR由来の新しいGRVIを用いて稲の生育段階を推定し、PAIとの相関・誤差で性能検証しており、植物形質取得手法が中心である。
abstractThis research investigates the potential of the proposed Generalized volume scattering model based Radar Vegetation Index (GRVI) for monitoring rice growth at different phenological stages.
A physically based metamodel is proposed to describe the dependency of canopy reflectance on the wavelength, leaf and soil optical properties. The four-stream solution is first applied to describe the interaction between the soil background and the vegetation layers. This leads to the calibration of four terms for a given canopy structure, observation configuration and leaf properties. This number can be reduced to two terms by using a linear approximation which shows a slight degradation when the multiple scattering contribution is significant. The dependency of each of the two or four terms on wavelength and leaf properties is described using the leaf total absorption coefficient. Our approach requires only 12 (linear approximation) to 24 (four-stream solution) simulations of a reference model to describe the full canopy reflectance dependency on wavelength, leaf and soil properties. The approach was evaluated against reference canopy reflectance simulations using the ray tracing LuxCoreRender model. LuxCoreRender was first compared against reference radiative transfer models. The reference dataset corresponds to a range of detailed 3D maize canopies showing variation of leaf and background properties and one heterogeneous scene including vegetation elements of different shapes that is classically used for model inter-comparison exercises under different view and sun directions in a set of wavebands. Results demonstrate that our approach provides an accurate description of the dependency of canopy reflectance on wavelength, leaf and soil properties with RMSE = 0.0017 for the four-stream solution and RMSE = 0.0022 for the linear approximation. The proposed approach appears therefore computationally effective and well suited to generate a large number of canopy reflectance simulations with detailed 3D radiative transfer models that can be used to retrieve vegetation characteristics from remote sensing observations.
Why it matches plant phenotyping methodsキャノピー反射の依存性を効率的に再現する物理ベースのメタモデルを開発し、基準放射伝達モデルとの比較で精度検証している。リモートセンシングによる植生特性推定に再利用可能な計算的フェノタイピング手法であり、方法が中心である。
abstractA physically based metamodel is proposed to describe the dependency of canopy reflectance on the wavelength, leaf and soil optical properties.
There is growing interest in using Digital Aerial Photogrammetry (DAP) for forestry applications. However, the performance of pushbroom DAP relative to frame-based DAP and airborne lidar is not well documented. Interest in DAP stems largely from its low cost relative to lidar. Studies have demonstrated that frame-based DAP generally performs slightly poorer than lidar, but still provides good value due to its reduced cost. In the USA pushbroom imagery can be dramatically less expensive than frame-camera imagery in part because of a nationwide collection program. There is an immediate need then to understand how well pushbroom DAP works as an auxiliary data source in the prediction of key forest attributes including basal area, volume, height, and the number of trees per ha.This study compares point clouds generated from 40 cm pushbroom DAP with point clouds from lidar and 7.5 cm, 15 cm, and 30 cm frame-based DAP. Differences in point clouds from these data sources are readily apparent in visual inspections; e.g. DAP tends to measure canopy gaps poorly, omit individual trees in openings, is typically unable to represent the ground beneath canopy, and is susceptible to commission errors manifested as points above the canopy surface. Frame-based DAP provides greater canopy detail than pushbroom DAP, which becomes more apparent with higher image resolution. Our results indicated that DAP height metrics generally have a strong linear relationship with lidar metrics, with R² values ranging from 83 – 90% for cover, and 47–80% for height quantiles. Similarly, lidar auxiliary variables explain the greatest variation in forest attributes, e.g., volume (84%), followed closely by 30 cm frame-based DAP (81%), with the poorest results from pushbroom DAP (75%). While DAP resolution had a visible effect on canopy definition, it did not appreciably affect point cloud metrics or model performances. Although pushbroom DAP explained the least variation in forest attributes, it still had sufficient explanatory power to provide good value when frame-based DAP and lidar are not available.
Why it matches plant phenotyping methods森林の樹高・林分材積・胸高断面積などの植物・林分形質を対象に、pushbroom DAP、フレームカメラDAP、LiDARの点群と予測性能を比較検証しており、形質取得手法の評価が研究の中心です。
abstractThis study compares point clouds generated from 40 cm pushbroom DAP with point clouds from lidar and 7.5 cm, 15 cm, and 30 cm frame-based DAP.
The prediction of grasslands plant diversity using satellite image time series is considered in this article. Fifteen months of freely available Sentinel optical and radar data were used to predict taxonomic and functional diversity at the pixel scale (10 m × 10 m) over a large geographical extent (40,000 km²). 415 field measurements were collected in 83 grasslands to train and validate several statistical learning methods. The objective was to link the satellite spectro-temporal data to the plant diversity indices. Among the several diversity indices tested, Simpson and Shannon indices were best predicted with a coefficient of determination around 0.4 using a Random Forest predictor and Sentinel-2 data. The use of Sentinel-1 data was not found to improve significantly the prediction accuracy. Using the Random Forest algorithm and the Sentinel-2 time series, the prediction of the Simpson index was performed. The resulting map highlights the intra-parcel variability and demonstrates the capacity of satellite image time series to monitor grasslands plant taxonomic diversity from an ecological viewpoint.
Why it matches plant phenotyping methodsSentinel-1/2時系列と統計学習により、草地の植物多様性指数を画素単位で推定・検証する方法が研究の中心であり、植物群落状態の定量的なフェノタイプ推定に該当する。
abstractFifteen months of freely available Sentinel optical and radar data were used to predict taxonomic and functional diversity at the pixel scale (10 m × 10 m) over a large geographical extent (40,000 km²).
Lodging - the bending of crop stems - reduces the quantity and quality of cereal crop yields. Early quantification of crop lodging is important to prevent further losses and to facilitate harvesting operations. Crop angle of inclination (CAI) is a quantitative measure of the lodging stage and a component of lodging severity/score. CAI is an important structural parameter for lodged crops and very few studies have investigated its estimation using satellite-based remote sensing. In this study, the performance of Sentinel-1 and multi-incidence angle (FQ8-27° and FQ21-41°) RADARSAT-2 data were investigated for estimating CAI. Temporal crop biophysical/structural parameters (CAI and crop height) and meteorological data (rainfall and wind speed) were collected throughout May 1-June 30, 2018 in a very large commercial farm located in Jolanda di Savoia, Ferrara, Italy. Field data were grouped into different crop lodging stages (non-lodged/healthy (H), moderate lodging (ML), severe lodging (SL) and very severe lodging (VSL)) based on CAI. Quantitative relationships were established between field-measured CAI values and the RS-derived metrics for Sentinel-1 and RADARSAT-2 timeseries using support vector regression (SVR) models. The RADARSAT-2 FQ8 model performed most robustly with a R²CV (cross-validated R²) of 0.87 and a RMSECV (cross-validated RMSE) of 8.89° while the performance of the Sentinel-1 and RADARSAT-2 FQ21 models were comparable with an RMSECV of 11.35° and 11.63° respectively. Low incidence angle data were particularly sensitive to high CAI values (VSL) while high incidence angle data were useful for predicting lower CAI (ML and SL). While the RADARSAT-2 FQ-8 model outperformed the other two, the Sentinel-1 model still explained 78% of the CAI variability in the study site, which is important in the context of operational crop lodging stage assessment. This is the first study to demonstrate the utility of SAR remote sensing data for estimating CAI as a measure of the lodging stage and a component of lodging severity.
Why it matches plant phenotyping methodsSARリモートセンシングとSVRにより小麦の倒伏角度（CAI）を推定し、交差検証で性能比較しており、植物形質取得手法が研究の中心である。
abstractthe performance of Sentinel-1 and multi-incidence angle (FQ8-27° and FQ21-41°) RADARSAT-2 data were investigated for estimating CAI
Leaf chlorophyll is central to the exchange of carbon, water and energy between the biosphere and the atmosphere, and to the functioning of terrestrial ecosystems. This paper presents the first spatially-continuous view of terrestrial leaf chlorophyll content (ChlLₑₐf) at the global scale. Weekly maps of ChlLₑₐf were produced from ENVISAT MERIS full resolution (300 m) satellite data using a two-stage physically-based radiative transfer modelling approach. Firstly, leaf-level reflectance was derived from top-of-canopy satellite reflectance observations using 4-Scale and SAIL canopy radiative transfer models for woody and non-woody vegetation, respectively. Secondly, the modelled leaf-level reflectance was input into the PROSPECT leaf-level radiative transfer model to derive ChlLₑₐf. The ChlLₑₐf retrieval algorithm was validated using measured ChlLₑₐf data from 248 sample measurements at 28 field locations, and covering six plant functional types (PFTs). Modelled results show strong relationships with field measurements, particularly for deciduous broadleaf forests (R² = 0.67; RMSE = 9.25 μg cm⁻²; p < 0.001), croplands (R² = 0.41; RMSE = 13.18 μg cm⁻²; p < 0.001) and evergreen needleleaf forests (R² = 0.47; RMSE = 10.63 μg cm⁻²; p < 0.001). When the modelled results from all PFTs were considered together, the overall relationship with measured ChlLₑₐf remained good (R² = 0.47, RMSE = 10.79 μg cm⁻²; p < 0.001). This result is an improvement on the relationship between measured ChlLₑₐf and a commonly used chlorophyll-sensitive spectral vegetation index; the MERIS Terrestrial Chlorophyll Index (MTCI; R² = 0.27, p < 0.001). The global maps show large temporal and spatial variability in ChlLₑₐf, with evergreen broadleaf forests presenting the highest leaf chlorophyll values, with global annual median values of 54.4 μg cm⁻². Distinct seasonal ChlLₑₐf phenologies are also visible, particularly in deciduous plant forms, associated with budburst and crop growth, and leaf senescence. It is anticipated that this global ChlLₑₐf product will make an important step towards the explicit consideration of leaf-level biochemistry in terrestrial water, energy and carbon cycle modelling.
Why it matches plant phenotyping methods衛星リモートセンシングと放射伝達モデルにより葉 chlorophyll 含量を推定する手法を開発・検証し、植物機能タイプ別の葉形質を広域定量化しているため、手法が中心的です。
abstractWeekly maps of ChlLₑₐf were produced from ENVISAT MERIS full resolution (300 m) satellite data using a two-stage physically-based radiative transfer modelling approach.
Outbreaks of Xylella fastidiosa (Xf) in Europe generate considerable economic and environmental damage, and this plant pest continues to spread. Detecting and monitoring the spatio-temporal dynamics of the disease symptoms caused by Xf at a large scale is key to curtailing its expansion and mitigating its impacts. Here, we combined 3-D radiative transfer modelling (3D-RTM), which accounts for the seasonal background variations, with passive optical satellite data to assess the spatio-temporal dynamics of Xf infections in olive orchards. We developed a 3D-RTM approach to predict Xf infection incidence in olive orchards, integrating airborne hyperspectral imagery and freely available Sentinel-2 satellite data with radiative transfer modelling and field observations. Sentinel-2A time series data collected over a two-year period were used to assess the temporal trends in Xf-infected olive orchards in the Apulia region of southern Italy. Hyperspectral images spanning the same two-year period were used for validation, along with field surveys; their high resolution also enabled the extraction of soil spectrum variations required by the 3D-RTM to account for canopy background effect. Temporal changes were validated with more than 3000 trees from 16 orchards covering a range of disease severity (DS) and disease incidence (DI) levels. Among the wide range of structural and physiological vegetation indices evaluated from Sentinel-2 imagery, the temporal variation of the Atmospherically Resistant Vegetation Index (ARVI) and Optimized Soil-Adjusted Vegetation Index (OSAVI) showed superior performance for DS and DI estimation (r²VALUES>0.7, p < 0.001). When seasonal understory changes were accounted for using modelling methods, the error of DI prediction was reduced 3-fold. Thus, we conclude that the retrieval of DI through model inversion and Sentinel-2 imagery can form the basis for operational vegetation damage monitoring worldwide. Our study highlight the value of interpreting temporal variations in model retrievals to detect anomalies in vegetation health.
Why it matches plant phenotyping methodsオリーブ樹のXylella感染症状・感染率を、3D放射伝達モデル、航空ハイパースペクトル画像、Sentinel-2時系列から推定する手法を開発し、3000本超の樹木で検証しており、植物状態の取得・推定が中心である。
abstractWe developed a 3D-RTM approach to predict Xf infection incidence in olive orchards, integrating airborne hyperspectral imagery and freely available Sentinel-2 satellite data with radiative transfer modelling and field observations.
Drone applications are becoming increasingly common in the arena of forest management and forest inventories. In particular, the use of photogrammetrically derived drone-based image point clouds (DIPC) in individual tree detection has become popular. Use of an area-based approach (ABA) in small areas has also been considered. However, in-situ field measurements of sample plots substantially increase the cost of small area forest inventories. Therefore, we examined whether small-scale forest management inventories could be carried out without local field measurements. We used nationwide and regional ABA models for stem volumes fitted with airborne laser scanning (ALS) data to predict stem volumes using corresponding metrics calculated from DIPC data. The stem volumes were predicted at the cell level (15 × 15 m) and aggregated to test plots (30 × 30 m). Height metrics for the dominant tree layer from the DIPC data showed strong correlations with similar metrics computed from the ALS data. The ALS-based models applied with DIPC metrics performed well, especially if the ABA model was fitted in the same geographical area (regional model) and the inventory units were disaggregated to coniferous and deciduous dominated stands using auxiliary information from Multi-source National Forest Inventory data (root mean square error at 30 × 30 m level was 13.1%). The corresponding root mean square error associated with the nationwide ABA model was 20.0% with an overestimation (mean difference 9.6%).
Why it matches plant phenotyping methodsドローン画像由来の点群を用いて森林の個体・区画レベルの幹材積を推定し、ALSモデルとの比較および誤差評価を行う測定手法が研究の中心であるため、植物形質推定の方法適用・検証として含める。
abstractThe stem volumes were predicted at the cell level (15 × 15 m) and aggregated to test plots (30 × 30 m).
A variety of prototypical metrics of ICESat-1 GLAS full-waveform data have been applied to estimate the forest canopy height over a footprint area and presented using the values of maximum canopy height, crown-area-weighted canopy height, or basal-area-weighted canopy height. The full-waveform pattern of a large footprint is changeable due to complicated interaction of canopy elements and terrain relief, consequently a noticeable bias may be introduced to the estimation. This paper begins with the approach of area-based growing stock volume calculation, developing harmonized metrics to integrate versatile signals in a denoised multimodal waveform of ICESat-1 data. These novel metrics were applied to derive a harmonized canopy height model for estimating the canopy height at footprint level and the distribution of canopy height at stand level. The performance of this model was evaluated with respect to the prototypical metrics derived canopy height model based on airborne LiDAR data determined canopy heights of a secondary evergreen mixed forest in a steep rugged terrain in central Taiwan. Footprint-level canopy height was evaluated via accuracy measures using a cross-validation method. Appropriateness of canopy height structure was examined via a post hoc test of the shape similarity for both ICESat-1 data-derived and airborne LiDAR-derived canopy height distribution models at stand level. Results showed that the harmonized canopy height model was able to achieve an accuracy of RMSE = 3.13 ± 0.08 m and RMSE% = 22.40 ± 0.55% as well as a significant reduction of bias. The canopy height distribution of the forest stand derived by harmonized metrics of ICESat-1 full-waveform data is statistically the same as that determined by airborne LiDAR data. Harmonized metrics provide compensatory information of trailing-edge extent and terrain index for describing canopy height. In contrast to the canopy height model using only prototypical waveform metrics, the harmonized metrics based canopy height model can mitigate the potential influence of terrain effects and dense canopy coverage on waveform pattern. Because a priori knowledge of terrain relief data is not required for modeling, the harmonized algorithm is useful for retrieving the trend of canopy height growth over the terrestrial forest ecosystem.
Why it matches plant phenotyping methodsICESat-1全波形データから森林キャノピー高とその分布を推定する新規指標・モデルを開発し、航空LiDARとの交差検証で精度評価しており、植物状態の取得手法が中心である。
abstractdeveloping harmonized metrics to integrate versatile signals in a denoised multimodal waveform of ICESat-1 data
Digital aerial photogrammetry (DAP) has demonstrated utility across a range of forest environments as an alternative data source to airborne laser scanning (ALS) for estimating forest inventory attributes in an area-based approach. In this context, metrics are typically derived from the DAP point cloud in a manner analogous to that of ALS data. However, image matching algorithms also allow for spectral information from the image data to be transferred to the point cloud. Herein, we quantify the contribution of this spectral information to the area-based prediction of five forest inventory attributes: Lorey's mean height, quadratic mean diameter, basal area, gross volume per ha, and stems per ha in a highly productive coastal temperate rainforest on Vancouver Island, British Columbia, Canada. Using ground plots and ALS-derived area-based estimates as reference, we compare plot-level predictions generated using (i) DAP point cloud metrics, (ii) DAP spectral metrics, and (iii) combinations of DAP point cloud and spectral metrics. In addition to prediction accuracy, we assessed variable importance to identify those metrics that were most informative for the developed models. Our results indicated that for models generated using DAP data, prediction accuracy was greatest when the point cloud-based metrics were incorporated. Models that incorporated both point cloud and spectral information were only slightly more accurate than models based on point cloud metrics only. We found that the improvement in accuracy was not observed for all stand attributes. The highest increase in accuracy for models combining point cloud and spectral metrics was observed for quadratic mean diameter, basal area per hectare, and stem volume per hectare, with change in relative root mean square error of −1.3%, −1.75%, and −0.23%, respectively. Models derived with spectral metrics only had the lowest accuracy with R2 values never exceeding 0.25. Analysis of the variable importance indicated that point cloud metrics are markedly more important than spectral metrics. We conclude that the benefit of the additional spectral information in this forest environment is negligible, and the effort to derive the spectral information cannot be justified for operational applications. Our results confirm those of other studies in other environments that have likewise found minimal benefit to the incorporation of DAP spectral information in area-based estimation.
Why it matches plant phenotyping methodsDAP点群・スペクトル情報を用いて森林プロットの樹高、径、基底面積、材積、立木本数を推定し、予測精度と変数重要度を比較検証しており、植物状態の測定手法評価が中心である。
abstractHerein, we quantify the contribution of this spectral information to the area-based prediction of five forest inventory attributes: Lorey's mean height, quadratic mean diameter, basal area, gross volume per ha, and stems per ha
Provisioning services from grassland ecosystems are strongly linked to physical and chemical grassland traits, which are affected by atmospheric CO2 concentrations ([CO2]s). The influences of increased [CO2]s ([eCO2]s) are typically investigated in Free Air Carbon dioxide Enrichment (FACE) studies via destructive sampling methods. This traditional approach is restricted to sampling plots and harvest dates, while hyperspectral approaches provide new opportunities as they are rapid, non-destructive and cost-effective. They further allow a high temporal resolution including spatially explicit information. In this study we investigated the hyperspectral predictability of 14 grassland traits linked to forage quality and quantity within a FACE experiment in central Germany with three plots under ambient atmospheric [CO2]s, and three plots at [eCO2]s (∼20% above ambient [CO2]s). We analysed the suitability of various normalisation and feature selection techniques to link comprehensive laboratory analyses with two years of hyperspectral measurements (spectral range 600–1600 nm). We applied partial least squares regression and found good to excellent predictive performances (0.49 ≤ leave one out cross-validation R2≤ 0.94), which depended on the normalisation method applied to the hyperspectral data prior to model training. Noteworthy, the models' predictive performances were not affected by the different [CO2]s, which was anticipated due to the altered plant physiology under [eCO2]s. Thus, an accurate monitoring of grassland traits under different [CO2]s (present-day versus future, or within a FACE facility) is promising, if appropriate predictors are selected. Moreover, we show how hyperspectral predictions can be used e.g., within a future phenotyping approach, to monitor the grassland on a spatially explicit level and on a higher temporal resolution compared to conventional destructive sampling techniques. Based on the information during the vegetation period we show how hyperspectral monitoring might be used e.g., to adapt harvest practices or gain deeper insights into physiological plant alterations under [eCO2]s.
Why it matches plant phenotyping methodsハイパースペクトル計測と回帰モデルにより14種の草地形質を推定し、正規化・特徴選択の適性と予測性能を検証しており、形質取得手法が研究の中心である。
abstractWe analysed the suitability of various normalisation and feature selection techniques to link comprehensive laboratory analyses with two years of hyperspectral measurements (spectral range 600–1600 nm).
Plant photosynthetic traits may be indicative of stress tolerance and performance in the field, making their accurate assessment critical in phenotyping trials. The maximum rate of carboxylation (Vcmax) is a key parameter for estimating CO2 assimilation (A), as it controls the CO2 fixation rate. This study demonstrates the utility of combining airborne-based solar-induced chlorophyll fluorescence (SIF) and hyperspectral imagery through the inversion of the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) model to estimate Vcmax, using sensor resolutions available in precision agriculture technologies. Vcmax was quantified in three wheat phenotyping experimental fields during the 2015–2018 growing seasons, comprising both rainfed and irrigated conditions. Airborne campaigns were carried out with two hyperspectral sensors, covering the 400–850 nm (20 cm resolution) and 950–1750 nm (70 cm resolution) spectral regions, and with a thermal camera (25 cm resolution) in the 8–14 μm region. Validation between model-estimated and field-measured Vcmax was statistically significant (r2 = 0.77; p-value ≤2.2e−16), and Vcmax was reliably associated with net assimilation both in irrigated and rainfed conditions (r2 = 0.65 and 0.5, respectively). By contrast, simulated chlorophyll content (Cab) and airborne-derived structural and chlorophyll indicators (NDVI and PSSRb) lacked significant correlations with assimilation rate in irrigated plots, while the relationship between assimilation rate and the crop water stress index (CWSI) was not significant in rainfed plots. The superior sensitivity of remotely-sensed Vcmax under irrigated conditions was likely related to its robustness to distortions from high canopy densities observed in other indices. The remote sensing retrieval of Vcmax, and the methodology demonstrated in this study is directly relevant for high-throughput plant phenotyping and for precision agriculture applications.
Why it matches plant phenotyping methods植物の光合成形質Vcmaxを、ハイパースペクトル画像・SIF・放射伝達モデルから推定し、圃場実測値で検証した手法研究であり、フェノタイピング手法が中心です。
abstractThis study demonstrates the utility of combining airborne-based solar-induced chlorophyll fluorescence (SIF) and hyperspectral imagery through the inversion of the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) model to estimate Vcmax
Spectroscopy is becoming an increasingly powerful tool to alleviate the challenges of traditional measurements of key plant traits at the leaf, canopy, and ecosystem scales. Spectroscopic methods often rely on statistical approaches to reduce data redundancy and enhance useful prediction of physiological traits. Given the mechanistic uncertainty of spectroscopic techniques, genetic modification of plant biochemical pathways may affect reflectance spectra causing predictive models to lose power. The objectives of this research were to assess over two separate years, whether a predictive model can represent natural and imposed variation in leaf photosynthetic potential for different crop cultivars and genetically modified plants, to assess the interannual capabilities of a partial least square regression (PLSR) model, and to determine whether leaf N is a dominant driver of photosynthesis in PLSR models. In 2016, a PLSR analysis of reflectance spectra coupled with gas exchange data was used to build predictive models for photosynthetic parameters including maximum carboxylation rate of Rubisco ( V c , max ), maximum electron transport rate ( J max ) and percentage leaf nitrogen ([N]). The model was developed for wild type and genetically modified plants that represent a wide range of photosynthetic capacities. Results show that hyperspectral reflectance accurately predicted V c ,max , J max and [N] for all plants measured in 2016. Applying these PLSR models to plants grown in 2017 resulted in a strong predictive ability relative to gas exchange measurements for V c ,max , but not for J max , and not for genotypes unique to 2017. Building a new model including data collected in 2017 resulted in more robust predictions, with R 2 increases of 17% for V c , max . and 13% J max . Plants generally have a positive correlation between leaf nitrogen and photosynthesis, however, tobacco with reduced Rubisco (SSuD) had significantly higher [N] despite much lower V c ,max . The PLSR model was able to accurately predict both lower V c , max and higher leaf [N] for this genotype suggesting that the spectral based estimates of V c , max and leaf nitrogen [N] are independent. These results suggest that the PLSR model can be applied across years, but only to genotypes used to build the model and that the actual mechanism measured with the PLSR technique is not directly related to leaf [N]. The success of the leaf-scale analysis suggests that similar approaches may be successful at the canopy and ecosystem scales but to use these methods across years and between genotypes at any scale, application of accurately populated physical based models based on radiative transfer principles may be required.
Why it matches plant phenotyping methods植物の光合成生理形質をハイパースペクトル反射とPLSRで推定する手法を開発・検証し、年次・遺伝子型間の予測性能を評価しているため、フェノタイピング手法が中心です。
titleHigh-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR)
The light absorption coefficient of vegetation is related to the content and composition of pigments in the plant canopy. It is a useful metric for understanding the spatial and temporal dynamics of the absorbed solar radiation, photosynthetic capacity, and productivity of vegetation. Still, its estimation in vivo is challenging due to the large variability induced by numerous features: canopy-related factors, including biochemical and structural characteristics, and factors external to the canopy such as soil background, solar irradiation, and sun-target-sensor geometry conditions. Here we revisit a semi-analytical modeling framework for deriving the light absorption coefficient of plant canopies from reflectance data. The proposed approach is based on the partition of the total light absorption coefficient into photosynthetic and non-photosynthetic pigment components in the canopy, and canopy backscattering. The model-derived absorption coefficient of chlorophyll was compared with field matchups of total canopy chlorophyll content in three crops with contrasting leaf structures, canopy architectures and photosynthetic pathways: maize, soybean, and rice. The model allows for the derivation of absorption coefficient spectra across the photosynthetically active radiation and the red edge spectral regions, as well as accurate estimations of canopy chlorophyll content, both of which are necessary for analyzing the physiological and phenological status, and the canopy-level photosynthetic capacity, of plants.
Why it matches plant phenotyping methods植物キャノピーの反射スペクトルから光吸収係数とクロロフィル量を推定する手法を開発し、複数作物で実測値と比較検証しているため、植物表現型取得が中心である。
abstractHere we revisit a semi-analytical modeling framework for deriving the light absorption coefficient of plant canopies from reflectance data.
-Solar-induced chlorophyll a Fluorescence (SIF), which is distributed over a relatively broad (~200 nm) spectral range, is a signal intricately connected to the efficiency of photosynthesis and is now observable from space. Variants of the Fraunhofer Line Depth/Discriminator (FLD) method are used as the basis of retrieval algorithms for estimating SIF from space. Although typically unobserved directly, recent advances in FLD-based algorithms now facilitate the prediction (by model inversion) of the canopy emitted fluorescence spectrum from the discrete-feature FLD retrievals.-Here we present first canopy scale measurements of chlorophyll a fluorescence spectra emitted from Scots pine at two times of year, and also from a lingonberry dominated understory. We used a high power multispectral Light Emitting Diode (LED) array to illuminate the respective canopies at night and measured under standardised conditions using a field spectrometer mounted in the nadir position above the canopy. We refer to the technique, which facilitates the in situ upscaling of a commonly measured leaf scale quantity to the canopy, as nocturnal LED-Induced chlorophyll a Fluorescence (LEDIF).-The shape of the LEDIF spectra was dependant on the colour of the excitation light and also on the dominant species. Because we measured pine at two different times of year we were also able to show an increase in the canopy scale apparent quantum yield of fluorescence which was consistent with leaf-level increase in fluorescence yield recorded with a monitoring PAM fluorometer.-The automation of the LEDIF technique could be used to estimate seasonal changes in canopy fluorescence spectra and yield from fixed or mobile platforms and provide a window into functional traits across species and architectures. LEDIF could also be used to evaluate FLD and inversion-based retrievals of canopy spectra, as well as different irradiance normalisation schemes typically applied to SIF data to account for the dependence of SIF on ambient light conditions.
Why it matches plant phenotyping methods植物キャノピーのクロロフィル蛍光スペクトルと見かけの量子収率を測定する新規LEDIF手法を開発・実証しており、植物の生理形質取得が研究の中心である。
abstractHere we present first canopy scale measurements of chlorophyll a fluorescence spectra emitted from Scots pine at two times of year, and also from a lingonberry dominated understory.
As an important indicator of plant photosynthetic activity, leaf chlorophyll content (LCC) has often been estimated non-destructively in the past decades from reflectance spectra measured with various spectrometers and leaf-holding accessories. Most studies established LCC predictive models with either integrating sphere measured directional-hemispherical reflectance factor (DHRF) spectra or leaf clip measured bidirectional reflectance factor (BRF) spectra. Given the effect of specular reflection on leaf reflectance, it remains unclear how DHRF spectra differ from the corresponding BRF spectra and whether the derived predictive models could be transferred between these two types of spectral data. To fill the gap in spectral examination and model comparison, this study aimed to examine the effect of specular reflection on leaf reflectance properties, the commonly used spectral indices or indicators, and the derived regression models across a broad variety of leaf DHRF and BRF spectra. Specifically, we quantified the difference between leaf BRF and DHRF spectra measurements and evaluated the effect of specular reflection on the estimation of LCC with 20 spectral features in four categories (simple ratio, SR; modified SR, mSR; double difference index, DD and red edge position, REP). Seven measured datasets collected from a combination of species, growing conditions, sites, and years were used to evaluate the model difference and calibrate the unified models across DHRF and BRF spectra. The robustness of those predictive models was validated with independent measured and simulated datasets comprised of both BRF and DHRF spectra.Our results demonstrated that the BRF spectra exhibited systematically higher amplitude than DHRF spectra. The widely used vegetation indices (VIs) in the SR category exhibited the most noticeable sensitivity to specular reflection as a significant proportion in BRF spectra. The adverse effect of specular reflection can be alleviated by the other three categories to different extents. With further assessment of model comparisons, we determined four unified models from the pooled data with two DD indices and two REP metrics. Application of the unified models to the validation spectra yielded low RMSE values up to 4.5 μg/cm2 and would not result in a significant loss in accuracy as compared to the BRF-specific or DHRF-specific LCC predictive models derived from the four spectral features. The assessment of unified models with comprehensive measured and simulated data could help us better understand the mechanism underlying the specular reflection effect on the relationship of LCC with sensitive spectral features. It will also facilitate the direct estimation of LCC with the common types of leaf reflectance spectra, which is beneficial to rapid and non-destructive determination of LCC for the plant science and agronomy communities.
Why it matches plant phenotyping methods葉の反射スペクトルからクロロフィル含量を推定する測定・予測モデルを比較、統合、独立データで検証しており、植物形質取得法が中心である。
abstractwe quantified the difference between leaf BRF and DHRF spectra measurements and evaluated the effect of specular reflection on the estimation of LCC
Knowledge of crop phenology assists in making agricultural decisions such as appropriate irrigation and fertilization applications in order to optimize crop yield. The objective of this study is to monitor crop phenology using Synthetic Aperture Radar (SAR) polarimetric decompositions and a random forest algorithm applied to a multi-temporal RADARSAT-2 dataset, acquired during the Soil Moisture Active Passive (SMAP) Validation Experiment 2016 in Manitoba (SMAPVEX16-MB). The model-based and eigen-based polarimetric parameters are used to separate the vegetation and soil scattering contributions in the total radar signal. As the crop morphological shape and structure vary with phenological growth, our study assumes that the polarimetric parameters related to the volume scattering mechanism have the potential to track the crop phenology. The sensitivity of the polarimetric parameters to the ground identified crop phenology is analyzed for different crop types. For canola, a single polarimetric parameter is sufficient to characterize the crop phenology, due to the high volume scattering power and large temporal dynamic. For corn, soybean and wheat, combinations of multiple polarimetric parameters are required. For each crop type, the Random Forest algorithm trained using 60% of the data is used to retrieve the crop phenology. Performances are compared to Artificial Neural Network, Support Vector Machine Regression and k-Nearest neighborhood algorithms. The Random Forest algorithm provides the best phenology retrieval with significant (p-value < 0.01) spearman correlation coefficients (between the retrieved and ground identified phenology) of 0.93, 0.90, 0.85 and 0.91 for canola, corn, soybean and wheat, respectively. While a single polarimetric parameter demonstrates limited sensitivity to corn phenology, the retrieved phenology from the Random Forest algorithm using multiple polarimetric parameters agrees well with the ground measurements. Furthermore, the importance of different polarimetric parameters for phenology retrieval using the Random Forest algorithm is quantified for different crop types. These findings will be of interest in developing future analytical retrieval models.
Why it matches plant phenotyping methodsSAR偏波分解と機械学習を用いて作物の生育フェノロジーを推定し、地上観測との比較検証や手法間比較を行っており、植物形態・生育状態の取得手法が中心である。
abstractThe objective of this study is to monitor crop phenology using Synthetic Aperture Radar (SAR) polarimetric decompositions and a random forest algorithm applied to a multi-temporal RADARSAT-2 dataset
Data from remote sensing with finer spectral and spatial resolution are increasingly available. While this allows more accurate prediction of plant traits at different spatial scales, it raises concerns about a lack of independence between observations. Hyperspectral wavelengths are serially correlated provoking multicollinearity among the predictors. As collection of ground reference points for validation remains time-consuming and difficult in many environments, empirical models are trained with a limited number of observations compared to the number of wavelengths. Moreover, any set of observations collected from a continuous surface is also likely to be spatially autocorrelated. Machine learning regression facilitates the task of selecting the most informative wavelengths, and then transforming them into latent variables to avoid the problem of multicollinearity. However, these regression methods do not solve the problem of spatial autocorrelation in the model residuals. In this study we show that, when significant spatial autocorrelation is observed, models that explicitly deal with spatial information and use a spectral index as a covariate exhibit a higher prediction accuracy than machine learning regressions do. However, for these models to work, the number of (hyperspectral) bands included in the models has to be drastically reduced and the model can not be directly extrapolated to a new (unobserved) location in another area. We conclude that quantifying spatial autocorrelation a-priori in the data can help in deciding whether the spatial and the spectral dimensions should be modelled together or not.
Why it matches plant phenotyping methods植物形質予測におけるハイパースペクトル情報と空間自己相関を扱うモデルを比較・評価しており、形質測定・推定手法が研究の中心である。
titleSpatially-explicit modelling with support of hyperspectral data can improve prediction of plant traits
Soil and vegetation water content are closely coupled via complex physiological and ecohydrological processes. Joint passive microwave retrievals of soil moisture θ and vegetation optical depth τ potentially provide unparalleled insight into these couplings on a global scale. However, this requires careful data analyses. Using a novel coupling distortion metric Rs2, we show that snapshot dual-polarization retrievals of τ and θ– widely used in vegetation studies – are spuriously correlated for SMAP L-band observations. Naive estimates of τ–θ coupling metrics are thus grossly distorted across a range of time scales. To mitigate the spurious correlations, we introduce a regularized retrieval algorithm. Our regularization algorithm exploits the assumed slowly changing nature of τ by penalizing rapid variations in the τ estimates. The degree of regularization r must balance a trade-off, as we find overregularization due to the oversmoothing of τ also distorts coupling estimates. When r is chosen to balance the trade-off according to Rs2, the spurious correlations are found to essentially vanish. The estimates of τ–θ correlation change substantially compared to non-regularized retrievals. The changes are largest (∼0.5) over high-biomass at time scales of up to two weeks, but sizeable differences are also found on longer time scales. Our analyses show that estimating vegetation–soil moisture coupling metrics benefits from dedicated retrievals and data analysis approaches. Provided the uncertainties are carefully accounted for, satellite radiometry offers exciting opportunities to study ecohydrological interactions such as plant water uptake and hydraulics.
Why it matches plant phenotyping methods植物光学的厚さτ（植生水分状態の指標）を衛星マイクロ波から推定する正則化検索アルゴリズムと結合指標を開発・評価しており、植生状態の取得手法が研究の中心です。
abstractTo mitigate the spurious correlations, we introduce a regularized retrieval algorithm.
Human activities have fundamentally changed Earth's climate and land surface in the latter half of the 20th century, leading to the proposal of a new geologic epoch known as the Anthropocene. One major ecological perturbation underway over the past several decades of the Anthropocene is extensive tree and shrub mortality, or forest die-off, triggered by elevated temperatures and prolonged drought, and/or insect and pathogen outbreaks. This increasingly common disturbance has affected forests and woodlands on all vegetated continents and may alter terrestrial carbon fluxes in the biosphere. Remote sensing has enabled mapping of the extent and ecological patterns of tree loss and damage, assessing potential carbon emissions and monitoring ecosystem trajectories after tree mortality. In this review article, we cover drought-induced changes in plant physiology, chemistry, and structure that occur as an individual tree progresses from healthy to stressed to standing dead or coarse woody debris, and corresponding responses in remotely sensed data that provide the opportunity and potential for observation and analysis at large spatial scales and early detection. The linkages between changes associated with tree mortality and remote sensing show exceptional promise for strategic and adaptive natural resource management as climate models project warmer and drier climates in the coming decades.
Why it matches plant phenotyping methods樹木の生理・構造変化や枯死状態をリモートセンシングで観測・早期検出する方法論的レビューであり、植物状態の取得手法が中心です。
abstractRemote sensing has enabled mapping of the extent and ecological patterns of tree loss and damage
Plant functional diversity (FD) is an important component of biodiversity that characterizes the variability of functional traits within a community, landscape, or even large spatial scales. It can influence ecosystem processes and stability. Hence, it is important to understand how and why FD varies within and between ecosystems, along resources availability gradients and climate gradients, and across vegetation successional stages. Usually, FD is assessed through labor-intensive field measurements, while assessing FD from space may provide a way to monitor global FD changes in a consistent, time and resource efficient way. The potential of operational satellites for inferring FD, however, remains to be demonstrated. Here we studied the relationships between FD and spectral reflectance measurements taken by ESA's Sentinel-2 satellite over 117 field plots located in 6 European countries, with 46 plots having in-situ sampled leaf traits and the other 71 using traits from the TRY database. These field plots represent major European forest types, from boreal forests in Finland to Mediterranean mixed forests in Spain. Based on in-situ data collected in 2013 we computed functional dispersion (FDis), a measure of FD, using foliar and whole-plant traits of known ecological significance. These included five foliar traits: leaf nitrogen concentration (N%), leaf carbon concentration (%C), specific leaf area (SLA), leaf dry matter content (LDMC), leaf area (LA). In addition they included three whole-plant traits: tree height (H), crown cross-sectional area (CCSA), and diameter-at-breast-height (DBH). We applied partial least squares regression using Sentinel-2 surface reflectance measured in 2015 as predictive variables to model in-situ FDis measurements. We predicted, in cross-validation, 55% of the variation in the observed FDis. We also showed that the red-edge, near infrared and shortwave infrared regions of Sentinel-2 are more important than the visible region for predicting FDis. An initial 30-m resolution mapping of FDis revealed large local FDis variation within each forest type. The novelty of this study is the effective integration of spaceborne and in-situ measurements at a continental scale, and hence represents a key step towards achieving rapid global biodiversity monitoring schemes.
Why it matches plant phenotyping methodsSentinel-2のスペクトル測定から植物機能形質に基づく機能多様性を推定し、交差検証と重要波長の評価を行う手法研究であり、植物表現型の取得・推定が中心です。
abstractassessing FD from space may provide a way to monitor global FD changes in a consistent, time and resource efficient way
Over the last decade, a series of global moderate resolution leaf area index (LAI) products have become available and been widely applied in many disciplines. At the same time, there is an increasing demand for the uncertainties associated with these products, which has to be determined through rigorous validation studies. This study validated seven global LAI products — EPS, GEOV2, GLASS, GLOBMAP, MODIS, PROBA-V, and VIIRS — over typical agricultural croplands in northeastern China. Seasonal continuous LAI measurements were obtained in field campaigns from paddy rice fields in 2012 and 2013, and from maize, soybean, and sorghum fields in 2016. High resolution reference LAI maps were first derived from HJ-1, Landsat 7, and Sentinel-2A images with the look-up table (LUT) inversion method and the results were evaluated with the field measured LAI (R2 = 0.85 and RMSE = 0.66). Subsequently, the moderate resolution LAI products were validated with the upscaled high resolution reference LAI.All LAI products show typical seasonal variation patterns of agricultural crops, but distinct differences exist among the products. The product quality indicators show large deviations during the peak growing season, whereas the relative uncertainties are higher during the green-up and senescent phases. Both EPS and GLASS show some saturation effects at LAI ~ 4.0 and underestimate the reference LAI (>0.5), whereas GLOBMAP shows the largest overestimation (bias = 0.96). GEOV2 and PROBA-V significantly overestimate the LAI of all crops. In contrast, MODIS and VIIRS underestimate and show high variations (RMSE >1.50, RRMSE >47%) compared with the reference LAI. In general, the global moderate resolution LAI products show moderate agreement with the reference LAI (RMSE: 0.80–2.0 and RRMSE: 25–60%). The product uncertainties are higher over paddy rice fields than those over the other crop fields. The uncertainties are mainly attributed to the lack of regional tuning of the global algorithms for agricultural crops at different growth stages. Further algorithm improvement and validation studies are necessary to improve the global LAI products for regional applications.
Why it matches plant phenotyping methods作物のLAIという植物形質を対象に、複数の全球LAIプロダクトをフィールド測定および高解像度基準マップと比較して厳密に検証しており、形質取得・推定手法の技術的妥当性評価が中心である。
abstractThis study validated seven global LAI products — EPS, GEOV2, GLASS, GLOBMAP, MODIS, PROBA-V, and VIIRS — over typical agricultural croplands in northeastern China.
NASA's Soil Moisture Active Passive (SMAP) and ESA's Soil Moisture Ocean Salinity (SMOS) carry satellite L-band radiometers whose primary missions are to measure soil moisture. However, they also allow retrieving of vegetation optical depth (VOD), the degree to which vegetation attenuates microwave radiation. Because attenuation is primarily a function of the total amount of liquid water in a vegetation canopy that is contained within vegetation tissue, VOD could be used to monitor seasonal changes in this quantity, which we call crop water, in major agricultural regions such as the US Corn Belt. There are two main advantages of L-band VOD: it observes the entire canopy volume because soil moisture sensitivity is maintained throughout the growing season; and since it is unaffected by cloud cover there are close to daily measurements. To show its value, we compare SMAP and SMOS VOD to satellite-scale estimates of crop productivity created using the Agricultural Integrated BIosphere Simulator (Agro-IBIS) and observed weather at the South Fork SMAP Core Validation Site in the Corn Belt state of Iowa. We find that SMAP and SMOS VOD are directly proportional to crop water. New empirical models that relate crop water to crop dry mass were required to make this finding. We created these models with in situ data spanning multiple years and stages of crop development. The value of the proportionality constant (or “b-parameter”) relating VOD to crop water at the satellite scale is about half as large as previous estimates. Because L-band VOD is directly proportional to crop water at the satellite scale, and because we understand the relationship between crop water and crop dry mass, SMAP and SMOS have the potential to evaluate the large-scale performance of crop models in the Corn Belt on a near daily basis.
Why it matches plant phenotyping methods衛星LバンドVODを作物キャノピーのcrop waterおよび乾物量推定に利用し、現地データで経験モデルを作成・検証しているため、植物形質取得手法が中心的である。
abstractSMAP and SMOS VOD to satellite-scale estimates of crop productivity
Quantification of microwave vegetation properties (i.e. vegetation optical depth (VOD) and single scattering albedo (ω)) is of interest not only to reliably estimate soil moisture from the Earth's microwave emission, but also for broad applications across plant physiology and hydrology. Estimated VOD is often used as a biomass proxy or to investigate plant water dynamics on multiple scales, as it is dependent on the vegetation water content, dry woody biomass, and canopy structure. For most but not all microwave satellite remote sensing applications, VOD and ω are assigned a priori from auxiliary vegetation information and used as inputs to retrieve soil moisture. Alternatively, polarized brightness temperature has been used to estimate VOD and ω. Retrieval algorithms typically use the zeroth-order solution to the radiative transfer equation (tau-omega model) to simultaneously characterize surface and vegetation emission. In this study we investigate one year of estimated VOD and ω at L- (1.4 GHz), C– (6.9 GHz) and X-band (10.7 GHz), from SMAP and AMSR2 satellites respectively. Since VOD and ω describe absorption and scattering integrated over a path length, we derive estimates of relative attenuation and scattering through normalization by a vegetation height derived from GLAS light detection and ranging (lidar) measurements.We assess whether relative attenuation is independent of vegetation height and land cover class. We also test how the relative attenuation and scattering estimates follow a spectrum across microwave frequencies. We apply this analysis globally and find relative canopy absorption and scattering to follow distinct patterns, relatively independent of the spatial distributions of vegetation height. We find that relative canopy attenuation generally peaks for shorter vegetation (5–10 m) and high density (shrub lands) while high forest canopies have relatively lower values. The VOD values that include the path length (taller vegetation) however are higher for forests relative to shorter vegetation. Seasonal amplitudes of VOD are higher for C- and X-band than for L-band and have maxima in regions with strong rainfall seasonality. This study presents first results for global relative canopy attenuation, based on one consistent retrieval for three frequencies, and discusses temporal dynamics as well as the role of multiple frequencies in assessing attenuation in canopies.
Why it matches plant phenotyping methods衛星マイクロ波とLiDARを用いて植生光学的厚さ、相対的な樹冠吸収・散乱を推定し、周波数・植生高・土地被覆との関係を全球的に評価している。樹冠状態の取得・解析が中心で、単なる生物学実験のルーチン測定ではない。
abstractQuantification of microwave vegetation properties (i.e. vegetation optical depth (VOD) and single scattering albedo (ω))
Satellite monitoring of plant phenology in tundra and grassland ecosystems using conventional vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), can be biased by effects of snow. Snow-free VIs that take advantage of the shortwave infrared (SWIR) band have been proposed to overcome this problem, viz., the phenology index (PI) and the normalized difference phenology index (NDPI). However, the PI cannot properly capture the presence of sparse vegetation, and the NDPI does not account for the influence of dry vegetation. Here, we propose a novel snow-free VI, designated the normalized difference greenness index (NDGI), that uses reflectance in the green, red, and near-infrared (NIR) bands. The NDGI is a semi-analytical index based on a linear spectral mixture model and the spectral characteristics of vegetation, snow, soil, and dry grass. Its performance at estimating the start and end of the growing season (SOS and EOS) was evaluated using simulation datasets, time-lapse camera data at tundra sites, and flux tower gross primary production (GPP) data at grassland sites. Simulation results demonstrated that the NDGI can exclude the influence of snow on estimates of SOS and EOS. At the tundra sites, the NDGI markedly outperformed the NDVI, PI, NDPI, NIRv (near-infrared reflectance of vegetation), EVI2 (two-band enhanced vegetation index), PPI (plant phenology index), and DVI+ (difference vegetation index plus) for SOS estimation, with a root mean square error (RMSE) of 6.5 days and a Bias of −1.3 days, and for EOS estimation, with an RMSE of 8.3 days and a Bias of 0.11 days. At the grassland sites, the NDGI also outperformed the other VIs at SOS estimation, with an RMSE of 10.3 days and a Bias of −4.9 days. Although its performance was poorer at monitoring EOS than SOS at grassland (GPP) sites, its performance was comparable to that of the PI and superior to that of the other VIs at estimating EOS. These results indicate the potential of the NDGI for operational monitoring of plant phenology in tundra and grassland ecosystems based on satellite observations.
Why it matches plant phenotyping methods植物フェノロジー（生育季節の開始・終了）を推定する新規衛星植生指数を開発し、シミュレーション、タイムラプス画像、GPPデータで性能評価しており、表現型取得・抽出法が研究の中心である。
abstractHere, we propose a novel snow-free VI, designated the normalized difference greenness index (NDGI)
Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. However, it remains challenging to efficiently scale this approach to large areas while maintaining reliable prediction at field scales. In this paper, we explored the factors limiting the generalization of the data assimilation approach and found that the accuracy of crop model prediction and the systematic model errors can significantly affect the performance of data assimilation and the yield estimation. To address these issues, we propose a hierarchical data assimilation framework, which enables maize yield estimation at field levels across large areas for the Midwestern US with no a priori knowledge about the management of individual fields. This approach applies data assimilation algorithms at two spatial scales. At the county scale, we adopted a Markov Chain Monte Carlo algorithm to recalibrate uncertain and sensitive model parameters based on aggregated Leaf Area Index (LAI) time series derived from Landsat images and county-level yield statistics. Using the county-specific models, we assimilated LAI time series into crop model simulations using Ensemble Kalman Filter for individual fields or pixels. This method was validated by multiple field-level maize yield datasets across major production states in the US Midwest. The Root Mean Squared Error ranges from 1.4 to 2.3 ton/ha, and the percentage error is between 9% and 21%. The hierarchical data assimilation framework provides a novel solution that downscales county-level yield statistics to 30-meter resolution yield maps, which can inform between and within field maize yield variability. This study contributes valuable insights towards practical large-scale crop yield mapping at high resolutions.
Why it matches plant phenotyping methods圃場単位のトウモロコシ収量という植物形質を、Landsat LAIと階層的データ同化で推定する手法を開発・検証しており、収量取得方法が研究の中心である。
abstractwe propose a hierarchical data assimilation framework, which enables maize yield estimation at field levels across large areas
Accurate measurements of maize yields at field or subfield scales are useful for guiding agronomic practices and investments and policies for improving food security. Data on smallholder maize systems are currently sparse, but satellite remote sensing offers promise for accelerating learning about these systems. Here we document the use of Google Earth Engine (GEE) to build “wall-to-wall” 10 m resolution maps of (i) cropland presence, (ii) maize presence, and (iii) maize yields for the main 2017 maize season in Kenya and Tanzania. Mapping these outcomes at this scale is extremely challenging because of very heterogeneous landscapes, lack of cloud-free satellite imagery, and the low quantity of quality ground-based data in these regions.First, we computed seasonal median composites of Sentinel-1 radar backscatter and Sentinel-2 optical reflectance measures for each pixel in the region, and used them to build both crop/non-crop and maize/non-maize Random Forest (RF) classifiers. Several thousand crop/non-crop labels were collected through an in-house GEE labeler, and thousands of crop type labels from the 2015–2017 growing seasons were obtained from various sources. Results show that the crop/non-crop classifier successfully identified cropland with over 85% out-of-sample accuracy in both countries, with Sentinel-1 being particularly useful for prediction. Among the cropped pixels, the maize/non-maize classier had an accuracy of 79% in Tanzania and 63% in Kenya.To map maize yields, we build on past work using a scalable crop yield mapper (SCYM) that utilizes simulations from a crop model to train a regression that predicts yields from observations. Here we advance past approaches by (i) grouping simulations by Global Agro-Environmental Stratification (GAES) zones across the two countries, in order to account for landscape heterogeneity, (ii) utilizing gridded datasets on soil and sowing and harvest dates to setup model simulations in a scalable way; and (iii) utilizing all available satellite observations during the growing season in a parsimonious way by using harmonic regression fits implemented in GEE. SCYM estimates were able to capture about 50% of the variation in the yields at the district level in Western Kenya as measured by objective ground-based crop cuts.Finally, we illustrated the utility of our yield maps with two case studies. First, we document the magnitude and interannual variability of spatial heterogeneity of yields in each district, and how it varies for different parts of the region. Second, we combine our estimates with recently released soil databases in the region to investigate the most important soil constraints in the region. Soil factors explain a high fraction (72%) of variation in predicted yields, with the predominant factor being soil nitrogen levels. Overall, this study illustrates the power of combining Sentinel-1 and Sentinel-2 imagery, the GEE platform, and advanced classification and yield mapping algorithms to advance understanding of smallholder agricultural systems.
Why it matches plant phenotyping methods衛星画像、GEE、作物モデル、機械学習を統合してトウモロコシ収量を推定・地図化する手法が中心で、地上収量との検証も行っているため、地域マッピングであっても植物形質（収量）の実質的な推定手法研究に該当する。
abstractHere we document the use of Google Earth Engine (GEE) to build “wall-to-wall” 10 m resolution maps of (i) cropland presence, (ii) maize presence, and (iii) maize yields
Invasive plant species can pose major threats to biodiversity, ecosystem functioning and services. Satellite based remote sensing has evolved as an important technology to spatially map the occurrence of invasive species in space and time. With the new era of the Sentinel missions, Synthetic Aperture Radar (SAR) and multispectral data are now freely available and repeatedly acquired on a high spatial and temporal resolution for the entire globe. However, the high potential of such sensors for automatic mapping procedures cannot be fully harnessed without sufficient and appropriate reference data for model calibration. Reference data are commonly acquired in field surveys, which however, are often relatively expensive and affected by sampling and observer bias. Moreover, a direct transferability to the remote sensing perspective and scale is difficult. Accordingly, we firstly assess the potential of Unmanned Aerial Vehicles (UAV) for semi-automatic reference data acquisition on species cover of three woody invasive species Pinus radiata, Ulex europaeus and Acacia dealbata occurring in Chile. Secondly, we test the upscaling of the estimated species cover to the spatial scale of Sentinel-1 and Sentinel-2. The proposed workflow includes the visual sampling of respective canopies in UAV orthomosaics and the subsequent spatial extrapolations using MaxEnt with spectral (RGB, Hyperspectral), textural (2D) and canopy structural (3D) predictors derived from UAV-based photogrammetry. These UAV-based maps are then used to train random forest models with multitemporal Sentinel-1 and Sentinel-2 data to map the invasive species cover on large spatial scales. Our results show that the semi-automatic UAV-based mapping of the three invasive species results in accurate predictions. Depending on the predictor combination, the correlation was 0.70, 0.77 and 0.90 for Pinus radiatia, Ulex europaeus, Acacia dealbata, respectively. Among the three species, we observed clear differences in the model performance between the tested photogrammetric predictors and their combinations (spectral, 2D texture or 3D structure). For scaling up the UAV-based estimates to the satellite-scale, the Sentinel-2 data (multispectral) were more important than Sentinel-1 data (SAR). An independent validation revealed that the R2 of the upscaling accounted for 0.78 or higher for all species and RMSE lower than 12%. Our results hence demonstrate that UAV-based reference data acquisitions are a promising alternative to traditional field surveys if the target species are directly identifiable in the UAV data.
Why it matches plant phenotyping methodsUAV画像から樹冠および侵入植物種の被覆率を推定する半自動ワークフローを開発・検証し、衛星データへのスケールアップも独立検証しているため、植物状態の取得手法が中心である。
abstractwe firstly assess the potential of Unmanned Aerial Vehicles (UAV) for semi-automatic reference data acquisition on species cover
Airborne laser scanning (ALS) data provide a detailed representation of forest canopy structure and are highly suitable for forest inventory applications. Providing three-dimensional data at a lower cost, digital aerial photogrammetry (DAP) has emerged as an alternative to ALS. Previous studies have compared the utility of ALS and DAP data for predicting forest attributes, however none of those studies used data acquired as part of large-area operational inventories. We used ALS, DAP and field data obtained from 836 plots and five operational inventories conducted in southeastern Norway. We compared local and regional modelling approaches for predicting basal area, number of stems, volume, Lorey's mean height, and dominant height. First, we developed district-, forest type-, and data source-specific nonlinear prediction models for all forest attributes (local models). Second, we fitted forest type- and data-source specific nonlinear models with pooled data from all districts (regional models), in which we included dummy variables to account for district-specific effects. We compared the accuracies of ALS- and DAP-based predictions made with local and regional models. Finally, we assessed how a reduced number of calibration plots affected the accuracies of ALS- and DAP- based predictions made with regional models. In general, ALS-based predictions made with local models were most accurate. District-specific effects needed to be accounted for in the regional models. ALS models required substantially fewer calibration plots than DAP models. The accuracies obtained with regional ALS models fitted with 50% of the available data were better than the accuracies obtained with local DAP models fitted with all the available data. Thus, by fitting regional ALS models with data from multiple inventories, field efforts can be reduced substantially while still obtaining better prediction accuracies than by fitting local DAP models.
Why it matches plant phenotyping methodsALS・DAPによる森林の構造形質推定を比較・検証し、モデル精度と必要な校正プロット数を評価しており、植物群落・プロットの形質取得法が中心である。
abstractWe compared local and regional modelling approaches for predicting basal area, number of stems, volume, Lorey's mean height, and dominant height.
The Simplified Level 2 Product Prototype Processor (SL2P) for estimating Leaf Area index (LAI), fraction of vegetation cover (fCover) and Canopy Water Content (CWC) from Sentinel-2/MSI and Landsat-8/OLI data was validated over an agricultural region. In-situ data collected during the SMAP Validation Experiment 2016 field campaign were used as a reference. SL2P processor performance varied substantially between crop type and biophysical variable. Over all crops, SL2P underestimated in-situ LAI and CWC measurements when using either MSI (slope (bias) of 0.70 (−0.37) for LAI and 0.42 (−0.37 kg/m2) for CWC) or OLI (slope (bias) of 0.59 (−1.21) for LAI and 0.24 (−0.23 kg/m2) for CWC) data. The accuracy of SL2P fCover estimates, over all crops, was higher (slope (bias) of 0.99 (1.84%) using MSI and 0.93 (−3.75%) using OLI). The RMSE between biophysical variables estimated using SL2P from MSI (OLI) in comparison to in-situ data was 0.98 (1.63) for LAI, 11.39% (10.95%) for fCover and 0.66 kg/m2 (0.96 kg/m2) for CWC. Slightly better results are generally obtained using locally calibrated vegetation indices models, when compared to SL2P estimates using the corresponding sensor data. Uncertainty metrics of vegetation biophysical variables derived from both MSI and OLI, when compared to interpolated in-situ data time series, are found comparable to results obtained for cross-validation suggesting the possibility of using interpolated in-situ data time series for validating decametric resolution remote sensing products sparsely sampled in time.
Why it matches plant phenotyping methodsSL2Pというリモートセンシング処理プロセッサによるLAI、植生被覆率、群落含水量の推定性能を実測値と比較検証しており、植物形質取得手法の技術検証が中心である。
abstractThe Simplified Level 2 Product Prototype Processor (SL2P) for estimating Leaf Area index (LAI), fraction of vegetation cover (fCover) and Canopy Water Content (CWC) from Sentinel-2/MSI and Landsat-8/OLI data was validated over an agricultural region.
Satellite remote sensing has been widely used in the last decades for agricultural applications, both for assessing vegetation condition and for subsequent yield prediction. Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for an advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 and Landsat 8 optical remote sensing data and machine learning methods in order to estimate crop GPP. With this approach, we by-pass the need for an intermediate step to retrieve the set of vegetation biophysical parameters needed to accurately model photosynthesis, while still accounting for the complex processes of the original physically-based model. Several implementations of the machine learning models are tested and validated using simulated and flux tower-based GPP data. Our final neural network model is able to estimate GPP at the tested flux tower sites with r2 of 0.92 and RMSE of 1.38 gC d−1 m−2, which outperforms empirical models based on vegetation indices. The first test of applicability of this model to Landsat 8 data showed good results (r2 of 0.82 and RMSE of 1.97 gC d−1 m−2), which suggests that our approach can be further applied to other sensors. Modeling and testing is restricted to C3 crops in this study, but can be extended to C4 crops by producing a new training dataset with SCOPE that accounts for the different photosynthetic pathways. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
Why it matches plant phenotyping methods衛星リモートセンシングと機械学習により作物の光合成機能を示すGPPを推定する手法を開発し、シミュレーションおよびフラックスタワーデータで検証しており、植物生理形質の取得法が中心である。
abstractwe combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 and Landsat 8 optical remote sensing data and machine learning methods in order to estimate crop GPP.
Lidar is an established tool for mapping forest structure, but its sparse spatial and temporal coverage often preclude its use in studying forest disturbance. In contrast, aerial imagery has been and continues to be regularly collected in many regions, and advances in stereo image matching have automated the creation of dense photogrammetric point clouds, which can also be used to map forest structure when paired with an accurate digital terrain model. As part of a study of the physical and ecological impacts of the 2012 High Park fire in Colorado, we generated a photogrammetric point cloud from pre-fire aerial imagery collected in 2008 and combined it with a digital terrain model generated from a 2013 post-fire lidar collection to produce canopy metrics commonly used in modelling of forest structure. We explore the correlation structure between the lidar and photogrammetry-derived canopy metrics, and the relationships between those metrics and forest structure attributes measured at unburned plots in the vicinity of the burn scar. Most corresponding lidar- and photogrammetry- derived canopy metrics had strong linear relationships between them (median r = 0.82), and metrics from both datasets yielded similar root mean square errors in multiple regression models of aboveground biomass (29.3% and 31.0%), basal area (29.8% and 27.7%), and several other forest structure attributes. We found the source of the canopy metrics (lidar or photogrammetry) to be a non-significant factor in some models of forest structure, suggesting that these datasets may be interchangeable in particular cases. Models derived from pre-fire aerial imagery were combined with burn severity information to quantify loss of biomass (2.31 ± 0.001 Tg) and examine possible relationships between forest structure and burn severity. These applications illustrate how the broad spatial and temporal coverage of aerial imagery and growing coverage of lidar can be utilized to improve understanding of changes in forest structure, including assessments of forest carbon flux.
Why it matches plant phenotyping methods森林キャノピー構造指標を抽出する航空画像フォトグラメトリとLiDARを比較・検証し、森林構造属性やバイオマス推定性能を評価しており、植物形質取得法が中心である。
abstractWe explore the correlation structure between the lidar and photogrammetry-derived canopy metrics, and the relationships between those metrics and forest structure attributes measured at unburned plots in the vicinity of the burn scar.
This study explores the potential of vegetation indices (VIs) for crop leaf area index (LAI) estimation, with a focus on comparing red-edge reflectance based (RE-based) and the visible reflectance based (VIS-based) VIs. Seven VIs were derived from multi-temporal RapidEye images to correlate with LAI of two crop species having contrasting leaf structures and canopy architectures: spring wheat (a monocot) and canola (a dicot) in northern Ontario, Canada. The relationship between LAI and the selected VIs (LAI-VI) was characterized using a semi-empirical model. The Markov Chain Monte Carlo (MCMC) sampling method was used to estimate the model parameters, including the extinction coefficient (KVI) and VI value for dense green canopy (VI∞). Results showed that crop-specific regression models were much closer to a generic regression model using the RE-based VIs than using the VIS-based VIs. Furthermore, the joint posterior probability distribution of the KVI and VI∞ of the RE-based VIs tended to converge for the two crops. This suggests that the RE-based VIs are not as sensitive to canopy structure, e.g., the average leaf angle (ALA), as the VIS-based VIs. This is also demonstrated by the sensitivity analyses using both PROSAIL simulations and field measurements. Hence, the RE-based VIs can be used to develop a more generic LAI estimation algorithm for different crops. Further studies are required to assess the impact of soil reflectance and other factors, such as illumination-target-viewing geometries and atmospheric conditions, on LAI retrieval.
Why it matches plant phenotyping methods作物のLAIという植物形質を対象に、RapidEye画像由来の植生指数を比較し、LAI推定モデルの性能と汎用性を評価しているため、形質取得手法が中心的です。
abstractThis study explores the potential of vegetation indices (VIs) for crop leaf area index (LAI) estimation, with a focus on comparing red-edge reflectance based (RE-based) and the visible reflectance based (VIS-based) VIs.
Fractional cover of green vegetation (FCover) is a key variable when observing Arctic vegetation under a changing climate. Vegetation changes over large areas are traditionally monitored by linking plot-scale measurements to satellite data. However, integrating field and satellite data is not straightforward. Typically, the satellite data are at a much coarser scale in comparison to field measurements. Here, we studied how Unmanned Aerial Systems (UASs) can be used to bridge this gap. We covered three 250 m × 250 m sites in Fennoscandian tundra with varying productivity and FCover, ranging from barren vegetation to shrub tundra. The UAS sites were then used to train satellite data-based FCover models. First, we created a binary vegetation classification (absent, present) by using UAS-derived RGB-orthomosaics and logistic regression. Secondly, we used the classification to calculate FCover to Planet CubeSat (3 m), Sentinel-2A MSI (10 m, 20 m), and Landsat 8 OLI (30 m) grids, and examined how well FCover is explained by various spectral vegetation indices (VI) derived from satellite data. The overall classification accuracies for the UAS sites were ≥90%. The UAS-FCover were strongly related to the tested VIs (D 2 89% at best). The explained deviance was generally higher for coarser resolution data, indicating that the effect of data resolution should be taken into account when comparing results from different sensors. VIs based on red-edge (at 740 nm, 783 nm), or near-infrared and shortwave infrared (SWIR) had the highest performance. We recommend wider inspection of red-edge and SWIR bands for future Arctic vegetation research. Our results demonstrate that UASs can be used for observing FCover at multiple scales. Individual UAS sites can serve as focus areas, which provide information at the finest resolution (e.g. individual plants), whereas a sample of several UAS sites can be used to train satellite data and examine vegetation over larger extents.
Why it matches plant phenotyping methodsUAS画像と衛星データを用いて植物の緑色植生被覆率（FCover）を抽出・推定し、分類精度と複数センサー間の性能を評価しているため、植物形質取得法が中心です。
abstractHere, we studied how Unmanned Aerial Systems (UASs) can be used to bridge this gap.
The thermal domain (TIR; 2.5–15 μm) delivers unique measurements of plant characteristics that are not possible in other parts of the electromagnetic spectrum. However, these TIR measurements have largely been restricted to laboratory leaf level or coarse spatial resolutions due to the lack of suitable data from airborne and spaceborne instruments. The airborne Hyperspectral Thermal Emission Spectrometer (HyTES) provides an opportunity to retrieve high spectral resolution emissivity and land surface temperature (LST) that can be exploited for canopy level vegetation research. This study is a high spatial resolution analysis of plant species' emissivity and LST using HyTES imagery acquired in the Huntington Botanical Gardens on 2014 July 5 and 2016 Jan 25. Leaf and canopy emissivity variation was identified among 24 plant species and used to determine leaf to canopy scaling capabilities. HyTES LST patterns among species and dates were quantified and correlated to LiDAR derived tree canopy attributes. At the leaf scale, one third of the species showed distinct spectral separation from other species. However, at the canopy scale most species were not spectrally separable. Random forest classification demonstrates the high level of confusion between species with overall accuracies <40%. LST data, derived from TIR measurements, showed that species exhibited significantly different distributions between dates and species. These distributions were largely explained by canopy structure (e.g. tree height and canopy density) and composition of neighboring pixels (e.g. presence of pavement versus trees). While species do not exhibit unique emissivity signatures at the canopy level, the LST variation among species provides a stronger understanding of LST variability in coarser resolution TIR imagery. This study represents the analysis of vegetation characteristics using the NASA's HyTES TIR sensor, opening the door for future remote sensing vegetation studies that include using the recently launched ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission.
Why it matches plant phenotyping methodsHyTES熱赤外画像から植物種の葉・樹冠放射率と表面温度を抽出し、葉から樹冠へのスケーリングや種間差を評価しており、植物特性の取得・解析が中心である。
abstractThis study is a high spatial resolution analysis of plant species' emissivity and LST using HyTES imagery acquired in the Huntington Botanical Gardens on 2014 July 5 and 2016 Jan 25.
Prolonged periods of wet soil conditions, when present during critical crop development stages, can significantly elevate the risk of some crop diseases. Wet soils in fields of flowering canola are a concern with respect to the development of sclerotinia as this pathogen feeds on the petals of the canola flower. As such, determining if canola is in bloom during periods of high moisture is important in deciding whether to take action to mitigate this disease. In this paper, RADARSAT-2 quad polarization and TerraSAR-X dual polarization Synthetic Aperture Radar (SAR) data were used with a novel dynamic filtering framework to estimate canola growth stages. In this process, a new crop growth stage indicator was developed and SAR polarimetric parameters sensitive to changes in phenology were identified. Model development used multi-year SAR satellite and field data for one site in Manitoba, Canada. The crop growth estimator was then tested on unseen data from three sites, one in each of Canada's Prairie provinces. This independent validation established that the growth estimator was able to accurately determine canola growth stage and date of flowering with high accuracy. Correlation coefficients (r-values) between observed and estimated phenology ranged from 0.91 to 0.96. Given that this method performed well on test data from other sites and years, this approach could be widely adopted for monitoring the development of canola over extended regions.
Why it matches plant phenotyping methodsSARデータと動的フィルタリングによりカノーラの生育段階・開花日を推定する手法を開発し、複数地点・年のデータで独立検証しており、植物フェノタイピング手法が中心である。
abstracta new crop growth stage indicator was developed
Digital terrain models (DTMs) and vegetation canopy height models (CHMs) are used in a wide range of earth and environmental sciences. An increasing number of CHM products are available from active, passive, and photogrammetric remotely sensed data; however, high-resolution (≤5 m), wall-to-wall CHMs for the arctic and northern boreal domains that are suitable for detailed spatial analysis are lacking. Recently, a 5-m spatial resolution pan-arctic digital surface model – the ArcticDEM – was created using automated stereopair analysis of high-resolution satellite data. The ArcticDEM is unprecedented in extent and spatial resolution, yet the product generally follows the uppermost surface elevation (i.e., representing a digital surface model, DSM) without regard to whether the surface is comprised of vegetation or bare-earth terrain. To address this limitation, we developed and tested an approach to map vegetation canopy height at a 5-m spatial resolution (hereafter called the local ArcticCHM), and then subtracted these estimated canopy heights from the ArcticDEM in order to create a 5-m resolution DTM (local ArcticDTM). We selected three pilot study areas (total 58 km²) across a north-south gradient in Alaska, representing a range of vegetation types and topographic conditions. We estimated and mapped canopy height using randomForest and imputation modeling approaches, with the ArcticDEM and high spatial resolution multispectral satellite data (WorldView-2) used as predictors. Airborne laser scanning (ALS) data was used for model calibration and independent validation. Canopy height was reliably predicted across the three study areas, with the best models ranging from root mean square errors (RMSE) 2.2 to 2.6 m and R² ranging from 0.59 to 0.76 relative to ALS-based CHM reference data. Similarly, the RMSE between the new local ArcticDTM product and ALS-based DTM reference data was 45–68% less than similar comparisons with the ArcticDEM. Our results offer a means to extend these local ArcticDTM and CHM products to establish high-resolution products elsewhere in Alaska of high value for a wide range of earth and environmental sciences research investigations.
Why it matches plant phenotyping methodsWorldView-2とArcticDEMを用いて植生キャノピー高を推定・地図化する手法を開発し、ALSデータで校正・独立検証しており、植物形態形質の取得方法が中心である。
abstractwe developed and tested an approach to map vegetation canopy height at a 5-m spatial resolution
This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then aggregated to Plant Functional Types (PFTs). Next, the spatial abundance of PFTs at MODIS resolution (500 m) is calculated using Landsat data (30 m). Based on these PFT abundances, representative trait values are calculated for MODIS pixels with nearby trait data. Finally, different regression algorithms are applied to globally predict trait estimates from these MODIS pixels using remote sensing and climate data. The methods were compared in terms of precision, robustness and efficiency. The best model (random forests regression) shows good precision (normalized RMSE≤ 20%) and goodness of fit (averaged Pearson's correlation R = 0.78) in any considered trait. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales. The new data products – the gap-filled trait matrix, a global map of PFT abundance per MODIS gridcells and the high-resolution global leaf trait maps – are complementary to existing large-scale observations of the land surface and we therefore anticipate substantial contributions to advances in quantifying, understanding and prediction of the Earth system.
Why it matches plant phenotyping methodsリモートセンシング、気候データ、機械学習を統合して葉形質を推定する処理チェーンを開発・比較検証しており、植物形質取得が中心的である。
abstractThis paper introduces a modular processing chain to derive global high-resolution maps of leaf traits.
The Chlorophyll Fluorescence Imaging Spectrometer (CFIS) is an airborne high resolution imaging spectrometer built at NASA's Jet Propulsion Laboratory (JPL) for evaluating solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2). OCO-2 is a NASA mission designed to measure atmospheric CO₂ but one of the novel data products is SIF, retrieved using reductions in the optical depth of Fraunhofer lines in OCO-2’s O₂ A-band, covering 757–775 nm at 0.042 nm spectral resolution. CFIS was specifically designed to retrieve SIF within the wavelength range of OCO-2, but extends further down to 737 nm, nearly maintaining the high spectral resolution of the OCO-2 instrument (0.07 vs. 0.042 nm). Here, we provide an overview of the instrument calibration and performance as well as the retrieval strategy based on non-linear weighted least-squares. To illustrate the retrieval performance using actual flight data, we focus on data acquired over agricultural fields in Mead, Nebraska from an unpressurized Twin Otter (DHC-6) aircraft at a flight altitude of 3000 m above ground level (AGL). Spectral residuals are consistent with expected detector noise, which enables us to compute realistic 1-σ precision errors of 0.5–0.7 W/m²/sr/μm for typical SIF retrievals, which can be reduced to <0.2 W/m²/sr/μm when individual data is gridded at 30 m spatial resolution. The 30 m resolution also enabled direct comparison with the Crop Data Layer from the National Agricultural Statistics Service as well as Landsat imagery (NDVI, EVI, Tₛₖᵢₙ), taken just a day prior to the CFIS overflights. Results show consistently higher vegetation indices and SIF values over soy fields compared to corn, likely due to the respective phenological stage, which might already have affected chlorophyll content and canopy structure (August 15, 2016). While this work is intended to highlight the technical capabilities and performance of CFIS, the comparisons against Landsat and crop types provide insights into how CFIS can be used to study mechanisms related to photosynthesis at fine spatial scales, with the fidelity needed to obtain un-biased SIF retrievals void of atmospheric correction.
Why it matches plant phenotyping methods航空機搭載分光イメージング装置の開発、校正、性能評価、蛍光抽出手法が中心であり、植物キャノピーの光合成関連状態（SIF）を測定する方法論研究である。
abstractCFIS was specifically designed to retrieve SIF within the wavelength range of OCO-2
The main goal of this study was to assess the potential of SAR backscatter signatures (RH and RV) retrieved from hybrid-polarized RISAT-1 SAR data in providing relevant information about the wheat growth parameters (leaf area index or LAI, plant water content or PWC, plant volume or PV and wet biomass or WB) over the entire growing season. The study was carried out over the parts of Bharatpur and Mathura districts located in Rajasthan and Uttar Pradesh (India), respectively. The three-date time series hybrid-polarized dataset was collected coincident to which a comprehensive ground truth campaign was organised. We propose that refining the total backscatter (σₜₒₜₐₗ⁰) values after minimising the effect of underlying/background soil cover, would result in more accurate retrieval of plant parameters since it is the vegetation backscatter, which ultimately has a direct correlation with the crop biophysical parameters. It was achieved using a semi-empirical water cloud model (WCM) based approach. The applicability of four different combinations of canopy descriptors, i.e. leaf area index (LAI), plant water content (PWC), leaf water area index (LWAI) and interaction factor (IF that takes into consideration the moisture distribution per unit volume) was tested on the RH and RV backscatter. We found that WCM based on LAI and IF as the two canopy descriptors modelled the total backscatter with a significantly high coefficient of determination (R² = 0.90 and 0.85, respectively) and RMSE of 1.18 and 1.25 dB, respectively. Subsequently, this set was used to retrieve the soil-corrected vegetation backscatter (σᵥₑg⁰) values. A comparative evaluation of the retrieval accuracy between plant parameters estimated from σₜₒₜₐₗ⁰ (σT_RHᵒ, σT_RVᵒ) and σᵥₑg⁰ (σV_RHᵒ, σV_RVᵒ) was performed using rigorously trained multi-layer perceptron (MLP) neural networks. The findings suggest that the prediction accuracy considerably improved when the backscatter of underlying/background soil cover was eliminated. The designed networks (with σₜₒₜₐₗ⁰ as input) retrieved plant water content and plant volume with the highest accuracy of 0.82 and 0.80, respectively while it increased dramatically to 0.87 and 0.89 when the inputs were substituted by σᵥₑg⁰. The present study is a first step towards retrieving crop parameters from hybrid-polarized data and thus possesses the potential to serve as a reference for further research initiatives.
Why it matches plant phenotyping methodsSARデータと水雲モデル・MLPを用いて、LAI、植物含水量、植物体積、湿潤バイオマスを推定する方法が研究の中心であり、精度評価も実施している。
abstractThe main goal of this study was to assess the potential of SAR backscatter signatures (RH and RV) retrieved from hybrid-polarized RISAT-1 SAR data in providing relevant information about the wheat growth parameters (leaf area index or LAI, plant water content or PWC, plant volume or PV and wet biomass or WB) over the entire growing season.
Tropical forest vegetation structure is highly variable, both vertically and horizontally, and provides habitat to a large diversity of species. The forest-savanna mosaic in the northern part of Lopé National Park, Gabon, has a large and complex variation in vegetation structure along a successional gradient. The goal of this research is to assess whether large footprint full-waveform lidar data can be used to distinguish successional vegetation types based on their vertical structure in this area. Eleven vegetation metrics were derived from the lidar waveforms: canopy height, canopy fractional cover, total Plant Area Index (PAI) and vertical profile of PAI. The PAI profiles from airborne waveform lidar showed good agreement with those from Terrestrial Laser Scanning, sampled at eight field plots across different vegetation types (r² = 0.95, RMSE = 0.63, bias = 0.41). The agreement further strengthened our confidence that lidar waveforms can be used to distinguish between the five vegetation types, within the limits of the sampled structure, because TLS was known to provide distinct PAI profiles for these vegetation types. We then employed a Random Forest model, trained with 193 locations of known vegetation type, to classify the entire study area into five successional vegetation types (classification accuracy = 81.3%). The resulting predictive map revealed the overall spatial pattern of vegetation types across the study area. Our results suggest that lidar-derived vegetation profiles can provide valuable information on vegetation type and successional stage. This, in turn, can further help to improve habitat and biodiversity conservation and forest management activities.
Why it matches plant phenotyping methods航空波形LiDARから樹冠高、被覆率、PAI、垂直構造を抽出し、TLSとの一致度を検証したうえで植生構造・遷移段階を分類しており、植物状態の取得手法が中心です。
abstractEleven vegetation metrics were derived from the lidar waveforms: canopy height, canopy fractional cover, total Plant Area Index (PAI) and vertical profile of PAI.
Sun-induced chlorophyll fluorescence (SiF) is increasingly used as a proxy for vegetation canopy photosynthesis. While ground-based, airborne, and satellite observations have demonstrated a strong linear relationship between SiF and gross primary production (GPP) at seasonal scales, their relationships at high temporal resolution across diurnal to seasonal scales remain unclear. In this study, far-red canopy SiF, GPP, and absorbed photosynthetically active radiation (APAR) were continuously monitored using automated spectral systems and an eddy flux tower over an entire growing season in a rice paddy. At half-hourly resolution, strong linear relationships between SiF and GPP (R² = 0.76) and APAR and GPP (R² = 0.76) for the whole growing season were observed. We found that relative humidity, diffuse PAR fraction, and growth stage influenced the relationships between SiF and GPP, and APAR and GPP, and incorporating those factors into multiple regression analysis led to improvements up to R² = 0.83 and R² = 0.88, respectively. Relationships between LUEₚ (=GPP/APAR) and LUEf (=SiF/APAR) were inconsistent at half-hourly and weak at daily resolutions (R² = 0.24). Both at diurnal and seasonal time scales with half-hourly resolution, we found considerably stronger linear relationships between SiF and APAR than between either SiF and GPP or APAR and GPP. Overall, our results indicate that for subdiurnal temporal resolution, canopy SiF in the rice paddy is above all a very good proxy for APAR at diurnal and seasonal time scales and that therefore SiF-based GPP estimation needs to take into account relevant environmental information to model LUEₚ. These findings can help develop mechanistic links between canopy SiF and GPP across multiple temporal scales.
Why it matches plant phenotyping methods自動分光システムによるイネ群落の蛍光計測を用い、SiFと光合成・吸収光の関係および推定性能を評価しており、植物生理状態の取得・検証が研究の中心である。
abstractfar-red canopy SiF, GPP, and absorbed photosynthetically active radiation (APAR) were continuously monitored using automated spectral systems and an eddy flux tower over an entire growing season in a rice paddy.
The temporal dynamics of optimum stomatal conductance (gsmax), as well differences between C3 and C4 crops, have rarely been considered in previous remote sensing (RS)-based Jarvis-type canopy conductance (Gc) models. To address this issue, a RS-based two-leaf Jarvis-type Gc model, RST-Gc, was optimized and validated for C3 and C4 crops using 19 crop flux sites across Europe, North America, and China. RST-Gc included restrictive functions for air temperature, vapor pressure deficit, and soil water deficit, and it used satellite-retrieved NDVI to formulate the temporal variation of gsmax defined at a photosynthetic photon flux density (PPFD) of 2000 μmol m−2 s−1 (gsm, 2000). Results showed that the parameters of RST-Gc differed between C3 and C4 crops. RST-Gc successfully simulated variations in Penman–Monteith (PM)-derived daytime Gc with R2 = 0.57 for both C3 and C4 crops. RST-Gc was incorporated into a revised evapotranspiration (ET) model and a new gross primary productivity (GPP) model. The two models were validated at 19 crop flux sites. Daily mean inputs were generally incorporated into a PM approach to model daily transpiration. This is inappropriate because available energy and stomatal conductance vary significantly on a diurnal basis, with both non-linearly regulating transpiration rate. The PM approach with daily mean inputs produced unreasonable transpiration rate estimates. Efforts were made in the revised ET model (denoted as RS-WBPM2), which was modified from the water balance based RS-PM (RS-WBPM) model of Bai et al. (2017), to address this issue by calculating transpiration using daytime inputs. The photosynthesis-based stomatal conductance model, developed by Ball et al. (1987a) and improved by Leuning (1995) (BBL model), was inverted to calculate GPP using canopy conductance; the inverted model was denoted as IBBL. Cross validation showed good agreement between flux tower measurements and modeled ET (R2 = 0.79, RMSE (root mean standard error) = 20.66 W m−2 for daily ET and R2 = 0.87, RMSE = 15.32 W m−2 for 16-day ET) and GPP (R2 = 0.83, RMSE = 2.49 gC m−2 d−1 for daily GPP and R2 = 0.86, RMSE = 1.96 gC m−2 d−1 for 16-day GPP) for the two models. Within-site validations demonstrated the successful performance of the two models at 18 sites (albeit with one outlier). Inter-site variations in ET and GPP were also successfully reproduced by the models. NDVI-derived gsm, 2000 outperformed the fixed gsm, 2000 in both ET and GPP estimates. The results imply that the RS-WBPM2 and IBBL models are useful tools for modeling regional and global ET and GPP.
Why it matches plant phenotyping methods衛星NDVIと気象・土壌情報から作物キャノピーコンダクタンスという生理状態を推定するモデルを開発し、19地点で最適化・検証しており、植物状態の取得手法が中心である。
abstracta RS-based two-leaf Jarvis-type Gc model, RST-Gc, was optimized and validated for C3 and C4 crops using 19 crop flux sites across Europe, North America, and China.
We present a novel approach for the prediction of forest growing stock volume based on explanatory variables from unmanned aerial vehicle (UAV) image photogrammetry without relying on the availability of a digital terrain model. This DTM-independent approach was developed to avoid the need for a detailed DTM, which is instead required in traditional photogrammetry to obtain relative heights above the terrain. The method, following an Area Based Approach (ABA), was tested in a boreal forest on a flat area in Norway and in a temperate mixed forest in a mountain steep terrain in Italy, on the basis of aerial images acquired with a SenseFly eBee Ag fixed-wing UAV.The plot level predictive performance of the models based on the DTM-independent metrics were evaluated against the results based on two more traditional approaches based on: (i) metrics from UAV photogrammetric data normalized using a DTM from airborne laser scanning (ALS), and (ii) metrics from ALS data. Percent root mean square error of predictions against measured values (RMSE%) was used for quantifying the performance of the different tests.Results revealed that the DTM-independent approach produced comparable results with both the traditional photogrammetric and ALS methods (the RMSE% ranged between 15.9% and 16.7% in Italy, and between 16.3% and 17.9% in Norway).Our results demonstrated that UAV photogrammetry can be used effectively for predicting forest growing stock volume even when high-resolution DTMs are not available, hence increasing the potentiality of UAVs in forest monitoring and inventory.
Why it matches plant phenotyping methodsUAV写真測量から森林の成長蓄積量を推定するDTM非依存手法を開発し、従来法と性能比較しており、植物群落レベルの形質推定が中心である。
abstractWe present a novel approach for the prediction of forest growing stock volume based on explanatory variables from unmanned aerial vehicle (UAV) image photogrammetry without relying on the availability of a digital terrain model.
A novel approach to characterize the physiological conditions of plants from hyperspectral remote sensing data through the numerical inversion of a light version of the SCOPE model is proposed. The combined retrieval of vegetation biochemical and biophysical parameters and Sun-induced chlorophyll fluorescence (F) was investigated exploiting high resolution spectral measurements in the visible and near-infrared spectral regions. First, the retrieval scheme was evaluated against a synthetic dataset. Then, it was applied to very high resolution (sub-nanometer) canopy level spectral measurements collected over a lawn treated with different doses of a herbicide (Chlorotoluron) known to instantaneously inhibit both Photochemical and Non-Photochemical Quenching (PQ and NPQ, respectively). For the first time the full spectrum of canopy F, the fluorescence quantum yield (ΦF), as well as the main vegetation parameters that control light absorption and reabsorption, were retrieved concurrently using canopy-level high resolution apparent reflectance (ρ*) spectra. The effects of pigment content, leaf/canopy structural properties and physiology were effectively discriminated. Their combined observation over time led to the recognition of dynamic patterns of stress adaptation and stress recovery. As a reference, F values obtained with the model inversion were compared to those retrieved with state of the art Spectral Fitting Methods (SFM) and SpecFit retrieval algorithms applied on field data. ΦF retrieved from ρ* was eventually compared with an independent biophysical model of photosynthesis and fluorescence. These results foster the use of repeated hyperspectral remote sensing observations together with radiative transfer and biochemical models for plant status monitoring.
Why it matches plant phenotyping methods放射伝達モデル逆解析により、植物キャノピーの蛍光、量子収率、生化学・生物物理パラメータを推定する手法を開発・評価しており、植物生理状態の取得方法が研究の中心である。
abstractA novel approach to characterize the physiological conditions of plants from hyperspectral remote sensing data through the numerical inversion of a light version of the SCOPE model is proposed.
Chlorophyll (Chl) is an important indicator of photosynthetic capacity and stress of vegetation. Remote sensing provides fast and nondestructive methods for estimating leaf Chl content based on its optical characteristics in visible and near-infrared spectrum. Multispectral lidar (MSL) systems have been developed to combine spectral and spatial detection abilities. Statistical relationships of plant biochemical constituents can be established through MSL measurements. However, empirical models cannot be readily extended to independent datasets. Simultaneously, the few spectral bands of MSL limit the use of a physical model. Hence, the development of hyperspectral lidar (HSL) systems offers a wider range of spectrum. This study investigated the possibility of adopting an HSL system with 32 channels covering 539–910 nm to estimate foliar Chl through a physical model. This study aimed to (1) Determine whether reflectance at the 32 channels is sufficient to retrieve Chl content through PROSPECT model inversion and (2) Considering the difference between passively and actively measured reflectance, investigate whether HSL measurements can be applied into PROSPECT model inversion for leaf biochemical constituents. Three kinds of datasets were used: a synthetic dataset simulated by running the PROSPECT model in forward mode, a public dataset ANGERS taking the channels of the HSL system, and an experimental dataset of paddy rice measured by the HSL system. Results showed HSL measurements can be directly used to retrieve leaf Chl content through PROSPECT-4 model inversion (R² = 0.55). These measurements also exhibit higher accuracy than that of support vector regression (threefold cross validation; 100 repetitions: median R² = 0.47). This validation work provides basis in the determination of vegetation physiological status directly from HSL measurements through model inversion with the PROSPECT model.
Why it matches plant phenotyping methodsHSLセンサーとPROSPECTモデル逆解析を用いて葉クロロフィル量を推定する方法を開発・検証しており、植物生理形質の取得が研究の中心である。
abstractThis study aimed to (1) Determine whether reflectance at the 32 channels is sufficient to retrieve Chl content through PROSPECT model inversion
The xanthophyll cycle regulates the energy flow to photosynthetic reaction centres of plant leaves. Changes in the de-epoxidation state (DEPS) of xanthophyll cycle pigments can be observed as changes in the leaf absorption of light with wavelengths between 500 to 570 nm. These spectral changes can be a good remote sensing indicator of the photosynthetic efficiency, and are traditionally quantified with a two-band physiologically based optical index, the Photochemical Reflectance Index (PRI). In this paper, we present an extension of the plant leaf radiative transfer model Fluspect (Fluspect-CX) that reproduces the spectral changes in a wide band of green reflectance: a radiative transfer analogy to the PRI. The idea of Fluspect-CX is to use in vivo specific absorption coefficients for two extreme states of carotenoids, representing the two extremes of the xanthophyll de-epoxidation, and to describe the intermediate states as a linear mixture of these two states. The ‘photochemical reflectance parameter’ (Cₓ) quantifies the relative proportion of the two states. Fluspect-CX simulates leaf chlorophyll fluorescence (ChlF) excitation-emission matrices, as well as reflectance (R) and transmittance (T) spectra as a function of leaf structure, pigment contents and Cₓ. We describe the calibration of the model and test its performance using various experimental datasets. Furthermore, we retrieved Cₓ from optical measurements of various datasets. The retrieved Cₓ correlates well with xanthophyll DEPS (R² = 0.57), as well with non-photochemical quenching (NPQ) of fluorescence (R² = 0.78). The correlation with NPQ enabled us to incorporate Fluspect-CX in the model SCOPE to scale the processes to the canopy level. Introducing the dynamic green reflectance into a radiative transfer model provides new means to study chlorophyll fluorescence and PRI dynamics on leaf and canopy scales, which is crucial for the remote sensing.
Why it matches plant phenotyping methods葉の反射・蛍光スペクトルからキサントフィル状態や光合成関連状態を推定する放射伝達モデルを拡張・較正し、複数データセットで検証しており、植物フェノタイピング手法が中心である。
abstractwe present an extension of the plant leaf radiative transfer model Fluspect (Fluspect-CX)
This article describes a new algorithm for the detection and delineation of tree crowns using optical sub-meter resolution satellite images. The algorithm focuses on detecting individual semi-isolated trees in a variety of environments defined as trees outside forests (TOF). The concept of Marked Point Processes (MPPs), which alternates phases of “birth” and “death” iterations to satisfy a density factor was used as a theoretical basis. The “mark” in the MPP represents the object being sought. Unlike most applications of MPP to object recognition, the mark used in our algorithm is computed from a 3D geometrical optical model artificially lit using the same illumination parameters as the image itself. Because trees differ in size, the process also incorporates a tree crown radius variable. The algorithm is tested on four sub-meter satellite images, each in a different environment. Validation was performed on both detection and delineation. The detection was based on tree crown counting and yielded an accuracy ranging from 0.81 to 0.95 for the four images. The delineation accuracy was estimated based on the crown pixel count and yielded an accuracy of ≈ 0.63 (0.57–0.72).
Why it matches plant phenotyping methods衛星画像から個体樹冠を検出・ delineate し、樹冠数と樹冠画素数として植物の形態特性を推定するアルゴリズムを開発・検証しており、フェノタイピング手法が中心である。
abstractThis article describes a new algorithm for the detection and delineation of tree crowns using optical sub-meter resolution satellite images.
Crop sowing date information is important for driving crop models and estimating crop yield. However, data on sowing dates are often scarce for both developed and developing countries, making remote sensing approaches to map sowing date an attractive approach. Yet the relative merits of different satellite sensors and metrics to estimate sowing date have not been well studied. We assess and compare the accuracies and uncertainties of satellite-derived sowing date estimates using different metrics (inflection point and threshold approaches) and satellite sensors that cover the range in the electromagnetic spectrum (optical, fluorescence, and radar). We validate these estimates using a county-level dataset derived from area-weighted field-level reported sowing dates for maize and soybean in the US states of Iowa, Illinois, and Indiana, and conduct our analysis both at the county level and at the aggregated tri-state region. We specifically use the Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS), solar-induced fluorescence (SIF) from Global Ozone Monitoring Experiment-2 (GOME-2) and Ku-band backscattering from QuikSCAT (dB). We also compare the inflection point method and the threshold approach that defines sowing date as a specific threshold value (10%, 30% and 50%) between the minimum and maximum value in a logistic-curve smoothed phenology. We find that satellite-based estimate of sowing dates from all methods and sensors shows a wide range of predictive power at the county level, with R² ranging between 0.15 and 0.90, with significant variation across sensors. SIF has excellent prediction for more counties than the other sensors, but EVI performs more consistently with moderate to good prediction for the greatest bulk of counties among all sensors, while dB's predicted sowing dates are negatively correlated with observed dates for most counties. For each sensor, aggregation to the regional level produces time series of predicted sowing dates that more successfully capture linear time trends and inter-annual variability. Adjusting for spring temperatures and crop areal coverage significantly improves the accuracies of the estimated sowing dates at both the county and regional scales. Among the combinations of the three satellite products and four metrics, we find using SIF and/or EVI and the 30% threshold metric have the highest accuracies in terms of reproducing inter-annual variability and minimizing RMSE at regional scales. This study provides a systematic assessment of using different satellite sensors and metrics to estimate county- to regional-scale crop sowing dates, which has important implications for mapping sowing date in data-limited regions across the globe.
Why it matches plant phenotyping methods衛星センサーと複数指標を用いた作物の播種日（生育状態）の推定法を体系的に比較・検証しており、植物状態の取得手法が中心的である。
abstractWe assess and compare the accuracies and uncertainties of satellite-derived sowing date estimates using different metrics (inflection point and threshold approaches) and satellite sensors
Information on crop phenological development stages such as emergence, flowering, fruiting, maturing and senescence is essential for crop production surveillance and yield prediction. It has long been related to optical spectral signatures such as the Normalized Difference Vegetation Index (NDVI) or spectral shifts in the red-edge range. In recent years, more efforts have been made to explore the sensitivity of Synthetic Aperture Radar (SAR), particularly polarimetric SAR signatures, to crop biophysical parameters or phenological stages. In this study, phenological metrics of canola (Brassica napus) and spring wheat (Triticum spp.) are related with temporal evolution of polarimetric SAR parameters derived from the C-band RADARSAT-2 full polarimetric SAR data. Both crops are very common in north eastern Ontario, Canada, but have very anatomically different development processes. From multi-temporal RADARSAT-2 data acquired in three consecutive years (2012–2014), significant correlations were observed between a number of SAR polarimetric parameters and the growth parameters of both crops. Strong correlation was observed between plant height and the Alpha angle of the Cloude-Pottier decomposition, with the R² of 0.91 and 0.66 for canola and wheat, respectively. The R² increased when the polarimetric parameters were smoothed in the time domain (R² of 0.98 for canola and 0.88 for wheat). Strong correlation was also observed for the two crops between the effective leaf area index (LAIe) and the Beta angle, and between days-after-seeding (DAS) and a combination of the Alpha and the Beta angles. These findings show that multi-temporal C-band polarimetric SAR parameters could be used for tracking crop phenological development stages.
Why it matches plant phenotyping methodsマルチテンポラル偏波SARを用いて作物の生育段階や草高・葉面積指数を推定・追跡し、相関と性能を評価しており、表現型取得手法が中心である。
abstractphenological metrics of canola (Brassica napus) and spring wheat (Triticum spp.) are related with temporal evolution of polarimetric SAR parameters derived from the C-band RADARSAT-2 full polarimetric SAR data.
The Evaporative Stress Index (ESI) quantifies temporal anomalies in a normalized evapotranspiration (ET) metric describing the ratio of actual-to-reference ET (fRET) as derived from satellite remote sensing. At regional scales (3–10 km pixel resolution), the ESI has demonstrated the capacity to capture developing crop stress and impacts on regional yield variability in water-limited agricultural regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to spatial and temporal limitations in the standard ESI products. In this study, we investigated potential improvements to ESI by generating maps of ET, fRET, and fRET anomalies at high spatiotemporal resolution (30-m pixels, daily time steps) using a multi-sensor data fusion method, enabling separation of landcover types with different phenologies and resilience to drought. The study was conducted for the period 2010–2014 covering a region around Mead, Nebraska that includes both rainfed and irrigated crops. Correlations between ESI and measurements of maize yield were investigated at both the field and county level to assess the potential of ESI as a yield forecasting tool. To examine the role of crop phenology in yield-ESI correlations, annual input fRET time series were aligned by both calendar day and by biophysically relevant dates (e.g. days since planting or emergence). At the resolution of the operational U.S. ESI product (4 km), adjusting fRET alignment to a regionally reported emergence date prior to anomaly computation improves r² correlations with county-level yield estimates from 0.28 to 0.80. At 30-m resolution, where pure maize pixels can be isolated from other crops and landcover types, county-level yield correlations improved from 0.47 to 0.93 when aligning fRET by emergence date rather than calendar date. Peak correlations occurred 68 days after emergence, corresponding to the silking stage for maize when grain development is particularly sensitive to soil moisture deficiencies. The results of this study demonstrate the utility of remotely sensed ET in conveying spatially and temporally explicit water stress information to yield prediction and crop simulation models.
Why it matches plant phenotyping methods衛星マルチセンサー融合により圃場レベルの作物水ストレス指標を高時空間解像度で推定し、収量との相関で性能評価しているため、植物状態の取得・推定法が中心です。
abstractgenerating maps of ET, fRET, and fRET anomalies at high spatiotemporal resolution (30-m pixels, daily time steps) using a multi-sensor data fusion method
Attenuation of surface microwave emission due to the overlying vegetation is proportional to the density of the canopy and to its water content. The vegetation optical depth (VOD) parameter measures this attenuation. VOD could be a valuable source of information on agroecosystems, especially at lower frequencies for which greater portion of the vegetation canopy contributes to the observed brightness temperature. In the past, visible-infrared indices have been used to provide yield estimates based on measuring the photosynthetic activity from the surface canopy layer. These indices are affected by clouds and apply only in the presence of solar illumination. In this study we instead use the L-band microwave radiometer on board of the SMAP mission that provides VOD estimates in all weather and regardless of illumination. This study proposes a series of L-band VOD metrics for crop yield assessment using the first annual cycle of SMAP data (April 2015 to March 2016) over north-central United States. Maps of yield and crop proportion from the US Department of Agriculture are compared to VOD retrieved from SMAP with the Multi-Temporal Dual Channel Algorithm (MT-DCA). The yield-VOD relationship is explored using principal components regressions. Results show that 66% of yield variance is explained over the whole region by the first principal component (PC1). In corn-soy crops, PC1 explains 78% of yield amount, and maximum, standard deviation, and range of VOD capture the yield spatial patterns. Mixture of crops and scene heterogeneity reduced the unique relationships between VOD metrics and yield for specific crops. Hence, in wheat and mixed crops, PC1 explains 43% of yield variance. Results suggest that complementary information on maximum biomass, growth rate, and VOD amplitude can provide robust yield estimates, and that the uncertainty of these estimates depends on crop composition and heterogeneity. This study provides evidence that L-band VOD metrics can potentially be used to enhance crop yield forecasts.
Why it matches plant phenotyping methodsLバンドVODから作物の収量・バイオマス関連特性を推定するセンサーベース手法を提案・評価しており、表現型推定が研究の中心である。
abstractThis study proposes a series of L-band VOD metrics for crop yield assessment
The structural loss rates of standing dead trees (SDTs) affect a variety of processes of interest to ecologists and foresters, yet the decomposition of SDTs has been traditionally characterized by qualitative decay classes, reductions in wood density as decay progresses, and sampling schemes focused on estimating snag longevity. By establishing a methodology to accurately and efficiently quantify SDT structural loss over time, these estimated structural loss rates would improve the performance of a variety of models and potentially provide new insight as to the manner in which SDTs undergo degradation in various conditions. The specific objective of this study were: 1) utilize the TreeVolX algorithm to estimate the volume of 29 SDTs scanned with terrestrial lidar; 2) develop a novel, voxel-based change detection algorithm capable of providing automated structural loss estimates with multitemporal terrestrial lidar observations; and 3) estimate and characterize the structural loss rates of Pinus taeda and Quercus stellata in southeastern Texas.A voxel-based change detection methodology was developed to accurately detect and quantify structural losses and incorporated several methods to mitigate the challenges presented by shifting tree and branch positions as SDT decay progresses. The volume and structural loss of 29 SDTs, composed of Pinus taeda and Quercus stellata, were successfully estimated using multitemporal terrestrial lidar observations over elapsed times ranging from 71 to 753 days. Pine and oak structural loss rates were characterized by estimating the amount of volumetric loss occurring in 20 equal-interval height bins of each SDT. Results showed that large pine snags exhibited more rapid structural loss in comparison to medium-sized oak snags in southeastern Texas.
Why it matches plant phenotyping methods植物の樹体構造・体積損失を地上LiDAR画像から抽出するボクセルベース手法を開発し、複数時点データで検証・適用しており、表現型計測手法が研究の中心です。
abstractdevelop a novel, voxel-based change detection algorithm capable of providing automated structural loss estimates with multitemporal terrestrial lidar observations
The clumping index (CI) characterizes the grouping of foliage relative to a random spatial distribution of leaves and is an important structural parameter for plant canopies that can influence canopy radiation regimes. Consequently, the CI is very useful for ecological and meteorological models. One method used to retrieve the CIs of plant canopies is to construct a linear relationship between the CI and the normalized difference between hotspot and dark spot (NDHD) angular index. This method requires a particularly accurate reconstruction of hotspot signatures, which are difficult to measure. In this study, we propose a framework to retrieve CIs from Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) parameters, which are generally based on linear CI-NDHD equations. The main algorithm is designed to retrieve CIs in the closed interval [0.33, 1.00]. This range is derived from the CI-NDHD equations and is thus called as the physical range here, although a modified lower boundary can be implemented in the future if necessary. If CIs are outside of this range, then a backup algorithm is designed to reprocess these so-called outlier CIs. The hotspot-adjusted version of the RossThick-LiSparseReciprocal (RTLSR) model (i.e., the RTCLSR model) is employed to reconstruct the hotspot signatures for the MODIS BRDF parameters. This method simplifies the hotspot reconstruction by using two hotspot parameters that are not distinctly scale-dependent particularly in the context of an inhomogeneous coarse spatial resolution. To evaluate this algorithm framework, we collect dozens of global field-measured CIs and calculate their determination coefficient (R²), root mean square error (RMSE) and bias relative to MODIS CIs derived using both the main algorithm and the backup algorithm. Our results show that this framework can derive MODIS CIs with a high accuracy (i.e., R² = 0.80 (0.72), RMSE = 0.07 (0.12), bias = −0.03 (−0.10)) using the main (backup) algorithms and that it shows promise for various ecological applications, especially in combination with the leaf area index (LAI).
Why it matches plant phenotyping methodsMODIS BRDFデータから植物群落のクラッピング指数という構造形質を推定するアルゴリズムを開発し、実測CIで精度検証しており、植物フェノタイピング手法が中心である。
abstractIn this study, we propose a framework to retrieve CIs from Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) parameters
Remote sensing estimates of forest canopy cover have frequently been used to support a variety of applications including wildlife habitat modeling, monitoring of watershed health, change detection, and are also correlated to various aspects of forest structure and ecosystem function. Although data from the long running Landsat earth observation program (1972–present) have been previously utilized to characterize forest canopy cover, the variability in spatial and spectral resolutions between the Landsat sensors has generally limited analyses to readily comparable imagery from the TM and ETM+ sensors, which omits large portions of the full temporal record. In this study, we present an R package, LandsatLinkr, which automates the processes for harmonizing Landsat MSS and OLI imagery to the spatial and spectral qualities of TM and ETM+ imagery, allowing for the generation of annual cloud-free composites of tasseled cap spectral indices across the entire Landsat archive. We demonstrate the utility of LandsatLinkr products, further enhanced through the LandTrendr segmentation algorithm, for characterizing forest attributes through time by developing annual forest masks and maps of estimated canopy cover for the state of Minnesota from 1973 to 2015. The forest mask model had an overall accuracy of 87%, with omission and commission errors for the forest class of 17% and 10%, respectively, and 9% and 16% for non-forest classification. Our resulting maps depicted a significant positive trend in forest cover across all ecological provinces of Minnesota during the study period. A random forest model used to predict continuous canopy cover had a pseudo R² of 0.75, with a cross validation RMSE of 5%. Our results are comparable to previous Landsat-based canopy cover mapping efforts, but expand the evaluation time period as we were able to utilize the entire Landsat archive for assessment.
Why it matches plant phenotyping methodsLandsatLinkrという画像 harmonization パッケージの開発と精度検証が中心で、森林キャノピー被覆という植物群落形質を時系列推定しているため、植物フェノタイピング手法として採用する。
abstractIn this study, we present an R package, LandsatLinkr, which automates the processes for harmonizing Landsat MSS and OLI imagery to the spatial and spectral qualities of TM and ETM+ imagery
Accurate classification of plant functional types (PFTs) reduces the uncertainty in global biomass and carbon estimates. Airborne small-footprint waveform lidar data are increasingly used for vegetation classification and above-ground carbon estimates at a range of spatial scales in woody or homogeneous grass and savanna ecosystems. However, a gap remains in understanding how waveform features represent and ultimately can be used to constrain the PFTs in heterogeneous semi-arid ecosystems. This study evaluates lidar waveform features and classification performance of six major PFTs, including shrubs and trees, along with bare ground in the Reynolds Creek Experimental Watershed, Idaho, USA. Waveform lidar data were obtained with the NASA Airborne Snow Observatory (ASO). From these data we derived waveform features at two spatial scales (1 m and 10 m rasters) by applying a Gaussian decomposition and a frequency-domain deconvolution. An ensemble random forest algorithm was used to assess classification performance and to select the most important waveform features. Classification models developed with the 10 m waveform features outperformed those at 1 m (Kappa (κ) = 0.81–0.86 vs. 0.60–0.70, respectively). At 1 m resolution, lidar height features improved the PFT classification accuracy by 10% compared to the analysis without these features. However, at 10 m resolution, the inclusion of lidar derived heights with other waveform features decreased the PFT classification performance by 4%. Pulse width, rise time, percent energy, differential target cross section, and radiometrically calibrated backscatter coefficient were the most important waveform features at both spatial scales. A significant finding is that bare ground was clearly differentiated from shrubs using pulse width. Though the overall accuracy ranges between 0.72 and 0.89 across spatial scales, the two shrub PFTs showed 0.45–0.87 individual classification success at 1 m, while bare ground and tree PFTs showed high (0.72–1.0) classification accuracy at 10 m. We conclude that small-footprint waveform features can be used to characterize the heterogeneous vegetation in this and similar semi-arid ecosystems at high spatial resolution. Furthermore, waveform features such as pulse width can be used to constrain the uncertainty of terrain modeling in environments where vegetation and bare ground lidar returns are close in time and space. The dependency on spatial resolution plays a critical role in classification performance in tree-shrub co-dominant ecosystems.
Why it matches plant phenotyping methods波形LiDAR特徴量を用いて植物機能タイプを分類し、空間スケール別の分類性能を評価する測定・解析手法が研究の中心であるため、植物状態のフェノタイピング手法として採用する。
abstractThis study evaluates lidar waveform features and classification performance of six major PFTs, including shrubs and trees, along with bare ground
Measurements of vegetation structure have become a valuable tool for ecological research and environmental management. However, data describing the thermal 3D structure of canopies and how they vary both spatially and temporally remain sparse. Coincident RGB and thermal imagery from a UAV platform were collected of both a standalone tree and a relatively dense forest stand in the sub-alpine Eastern Swiss Alps. For the first time, SfM-MVS methods were used to develop 3D RGB and thermal point clouds of the two sites with point densities of 35,245 and 776 points per m², respectively, compared to 78 points per m² for an airborne LiDAR dataset of the same area. Despite the low resolution of the thermal imagery compared to RGB photosets, forest structural elements were accurately resolved in both point clouds. Improvements in the quality of the thermal 3D data were gained through the application of a distance filter based on the proximity of these data to the RGB 3D point data. Vertical temperature gradients of trees were negative with increasing height at the standalone tree, but were positive in the dense stand largely due to increased self-shading of incoming shortwave energy. Repeat surveys across a single morning during the snowmelt period revealed changes in the spatial distribution of canopy temperatures which are consistent with canopy warming from direct solar radiation. This is the first time that coincidentally acquired RGB and thermal imagery have been combined to generate separate RGB and thermal point clouds of 3D structures. These methods and findings demonstrate important implications for atmospheric, hydrological and ecological modeling, and have wide application for effective thermal measurements of remote environmental landscapes.
Why it matches plant phenotyping methodsUAVのRGB・熱画像とSfM-MVSを統合し、樹冠の3D構造および温度分布を抽出する手法を開発・実証しており、植物状態の取得方法が研究の中心である。
abstractFor the first time, SfM-MVS methods were used to develop 3D RGB and thermal point clouds of the two sites
A new method was developed for reconstructing the geometric structure of large plants such as trees at the leaf-scale by utilizing terrestrial LiDAR data. The primary goal of the work was to develop a feasible means for accurately and rapidly reconstructing or “digitizing” entire trees in order to specify the position, orientation, and size of every leaf in digital tree models that provide geometric inputs for high-resolution biophysical models or analyses. As with any optical measurement technique, a primary challenge is accurately accounting for plant matter that is occluded from view of the sensor. The present method is termed “semi-direct” because it uses a triangulation procedure to approximately directly reconstruct as many leaves as possible that are in view of the scanner. For plant matter obstructed from view, a statistical backfilling procedure was used to add additional leaves such that the three-dimensional distribution of leaf area and orientation of the reconstructed plant matched that of the actual plant on average. In a best case scenario such as when leaf density is low, nearly all leaf area is directly reconstructed from the scan and the branch and clumping structure is preserved within the reconstruction. In the worst case scenario such as when the leaf density is very high and nearly all leaves are occluded from view of the scanner, only a small fraction of leaves can be directly reconstructed, but at a minimum the distribution of leaf area and the leaf angle distribution across the reconstructed plant will be consistent with that of the actual plant. Unlike many other approaches, the present method does not rely on the woody matter of the plant to provide a skeleton for reconstruction, and can be used in dense plants where little woody matter is visible from the scanner.
Why it matches plant phenotyping methods樹木の葉レベル形状・位置・向きをLiDARから再構成する手法の開発が研究の中心であり、植物表現型の取得・抽出に直接該当する。
abstractA new method was developed for reconstructing the geometric structure of large plants such as trees at the leaf-scale by utilizing terrestrial LiDAR data.
Forest canopy gaps play an important role in forest dynamics. Airborne laser scanning (ALS) data provide demonstrated capacity to systematically and accurately detect and map canopy gaps over large forest areas. Digital aerial photogrammetry (DAP) is emerging as an alternative, lower-cost source of three-dimensional information for characterizing forest structure and modelling forest inventory attributes. In this study we compared the relative capacities of ALS and DAP data to map canopy gaps in a complex coastal temperate rainforest on Vancouver Island, British Columbia, Canada. We applied fixed- and variable-height threshold approaches for gap detection using both ALS and DAP data, and validated outcomes using independent data derived via visual image interpretation. Overall accuracies for ALS-derived gaps were 96.50% and 89.50% for the fixed- and variable-height threshold approaches respectively, compared to 59.50% and 50.00% for the DAP-derived gaps, with DAP data having large errors of omission (>88%). We found that 70% of ALS-derived gaps were identified in old seral stage stands (age > 250 years), while 65% of DAP-derived gaps were located in early seral stage stands (age < 40 years). For the DAP data, gap detection accuracy was 80% in early seral stands, compared to 50% in old seral stands. In contrast, ALS detection accuracy varied by only ~6% between early and old seral stages. We compared detected gaps using a variety of metrics and found significant differences in the number and average size of gaps detected using ALS and DAP data. Using the fixed-height threshold, the ALS data identified 16 times more gaps and 6.5 times more gap area than the DAP data, with a mean ALS-derived gap size that was half that of the DAP data. The average amount of overlap between ALS- and DAP-detected gaps was 13.26% and 42.90% for the variable and fixed thresholds, respectively. We attribute these differences in gap detection to the nature of the DAP data itself, which characterizes primarily the outer canopy envelope, as well as to the confounding effects of canopy complexity and related occlusions and shadows on image matching algorithms. We conclude that DAP data do not provide analogous results to ALS data for canopy gap detection and mapping in coastal temperate rainforests, and that ALS data enable markedly superior accuracy and detailed gap characterizations.
Why it matches plant phenotyping methodsALSとDAPによる森林キャノピーギャップ検出を比較し、独立データで精度検証している。植物群落の構造状態を測定する方法が研究の中心である。
abstractIn this study we compared the relative capacities of ALS and DAP data to map canopy gaps
Fine-scale architectural tree models serve as an effective representation of three-dimensional plant material distributions. They can help to quantify wood volume and biomass, to estimate leaf area distributions on a detailed scale, and can be exploited for physically based modelling approaches. If architectural tree models can be derived for multiple acquisition dates, they permit the detailed investigation of phenological effects. Although promising approaches for the generation of architectural tree/forest models from terrestrial LiDAR data are available, they are often non-trivial and their application to forest plots is often difficult. This is restricting the flexibility of these reconstruction approaches especially for multi-temporal analyses. In this paper, forest models of two Larix decidua forest plots are reconstructed by making use of terrestrial LiDAR data and digital hemispherical photographs (DHP). Recent modelling strategies are enhanced and developed further in order to improve the robustness and usability of the architectural tree model reconstruction process.Raw point cloud data are directly used as input to solve both tree delineation and tree reconstruction in a single processing pipeline. This includes terrain filtering, intensity filtering, and trunk extraction. These steps are followed by a hierarchical and iterative multi-tree branch and twig reconstruction. Based on multi-temporal DHPs, various foliage states are documented. These DHPs and the reconstructed branching architectures are used to flexibly generate and update multi-temporal 3D models of foliage. In order to quantify the modelling performance with respect to various forest characteristics, a test setup based on simulated forest and acquisition geometries is build up. It can be shown, that typical sources of error in the tree reconstruction process are minimized by the proposed approach. It is possible to estimate wood volume distributions, trunk tapering and leaf area distributions with an error of only 10–14%. Except for strongly overlapping tree crowns, the overall accuracy of the single tree delineation in interlinked tree crowns is higher than 80%. Considering these error margins, we apply the modelling strategy to two forest plots and derive architectural models for three dates during the growing season. Using DHPs as reference data, it can be shown, that the estimated gap fraction values derived from the generated models show an error of only 10–15%.
Why it matches plant phenotyping methodsLiDARと全天空写真から樹木の3D構造・葉面積・木材体積などの植物形質を抽出する再構成パイプラインを開発し、シミュレーションで性能検証しているため、フェノタイピング手法が中心である。
abstractRecent modelling strategies are enhanced and developed further in order to improve the robustness and usability of the architectural tree model reconstruction process.
With the increasing availability of spectral sensors and consumer-grade data processing software, a democratization of imaging spectroscopy is taking place. In particular, novel lightweight 2D spectral imagers in combination with UAVs are increasingly being adapted for imaging spectroscopy. In contrast to traditional line-scanners, these sensors capture spectral information as a 2D image within every exposure. With computer vision algorithms embedded in consumer grade software packages, these data can be processed to hyperspectral digital surface models that hold spectral and 3D spatial information in very high resolution. To understand the spectral signal, however, one must comprehend the complexity of the capturing and data processing process in imaging spectroscopy with 2D imagers.This study establishes the theoretical background to comprehend the properties of spectral data acquired with 2D imagers and investigates how different data processing schemes influence the data. To improve the interpretability of a spectral signal derived for an area of interest (AOI), the specific field of view is introduced as a concept to understand the composition of pixels and their angular properties used to characterize a specific AOI within a remote sensing scene.These considerations are applied to a multi-temporal field study carried out under different illumination conditions in a barley field phenotyping experiment. It is shown that data processing significantly affects the angular properties of the spectral data and influences the apparent spectral signature. The largest differences are found in the red domain, where the signal differs by approximately 10% relative to a single nadir image. Even larger differences of approximately 14% are found in comparison with ground-based non-imaging field spectrometer measurements. The differences are explained by investigating the interaction between the angular properties of the data and canopy anisotropy, which are wavelength and growth stage dependent. Additionally, it is shown that common vegetation indices cannot normalize the differences and that the retrieval of chlorophyll is affected. In conclusion, this study helps to understand the process of imaging spectroscopy with 2D imagers and provides recommendations for future missions.
Why it matches plant phenotyping methods2Dイメージング分光の取得・処理特性を理論的に検討し、処理手法がスペクトル、植生指数、クロロフィル推定に与える影響を圃場フェノタイピングで検証しており、植物形質取得法が中心です。
abstractThis study establishes the theoretical background to comprehend the properties of spectral data acquired with 2D imagers and investigates how different data processing schemes influence the data.
Chlorophyll fluorescence is widely used as an indicator of photosynthesis and physiological state of plants. Remote acquisition of fluorescence allows the diagnosis of large field extensions, even from satellite measurements. Nevertheless, fluorescence emerging from chloroplasts, the one directly connected to plant physiology, undergoes re-absorption processes both within the leaf and the canopy. Therefore, corrections of the observed canopy fluorescence, taking into account these two re-absorption processes may help to draw accurate inferences about plant health. Here, we show the theoretical development and experimental validation of a model that allows to retrieve the spectral distribution of the leaf fluorescence spectrum from that on top of canopy (TOC) using a correction factor which is a function of both canopy and soil reflectance, and canopy transmittance. Canopy fluorescence spectra corrected by our theoretical approach and normalized shows 95% correlation with the normalized fluorescence spectrum at leaf-level, thus validating the model. Therefore, our results provide a physical explanation and quantification for fluorescence re-absorption within the canopy, a phenomenon which has only been mentioned but never measured up to the date. From a more general perspective, this new analytical tool together with the one previously developed by Ramos and Lagorio (2004) allows to obtain the spectral distribution of chloroplast fluorescence spectrum from that on top of canopy (TOC).
Why it matches plant phenotyping methods葉からキャノピーへのクロロフィル蛍光を補正し、植物の生理状態を推定するモデルを開発・実験検証しており、表現型取得法が研究の中心である。
abstractthe theoretical development and experimental validation of a model that allows to retrieve the spectral distribution of the leaf fluorescence spectrum from that on top of canopy (TOC)
Amazonian tropical forests play a significant role in global water, carbon and energy cycles. Considering the importance of this biome and climate change projections, the monitoring of vegetation status of these rainforests becomes of significant importance. In this context vegetation temperature is presented as a key variable linked with plant physiology. In particular some studies showed the relationship between this variable and the CO₂ absorption capacity and biomass loss of these tropical forests proving the potential use of vegetation temperature in the monitoring of the vegetation status. Nevertheless, the use of thermal remote sensing data over tropical forests still has some limitations being of special importance the atmospheric correction under very humid conditions and the possible high occurrence of cloudy pixels. In order to mitigate these limitations over the Amazon region, we present in this paper a new processing methodology to derive a LST product from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The LST product was generated using a tuned split-window equation and cloud information derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. This LST product was validated using simulated and in situ data, and intercompared to the MODIS LST standard product (MOD11). Validation analysis shows that the new LST product reduces the RMSE by 0.6 to 1K when compared to the MODIS standard LST product, mainly because of a reduction of the bias. We also show a preliminary intercomparison between MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) LST spatial patterns to illustrate the feasibility of VIIRS to extend forward the MODIS LST temporal series.
Why it matches plant phenotyping methodsAmazon森林の植生温度（LST）を推定する衛星リモートセンシング処理法を開発し、シミュレーション・現地データおよび標準製品との比較で検証しており、植物状態の取得手法が中心である。
abstractwe present in this paper a new processing methodology to derive a LST product from Moderate Resolution Imaging Spectroradiometer (MODIS) data.
The vertical heterogeneity of leaf biophysical and biochemical properties may have a large effect on the bidirectional reflectance and fluorescence of vegetation canopies. This has implications for the interpretation of remote sensing data. We developed a model for light interaction and energy balance in vegetation canopies in which leaf biophysical and biochemical properties vary in the vertical. The model mSCOPE is an extension of the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model, which simulates spectral and bidirectional reflectance, fluorescence, and photosynthesis of vertically heterogeneous vegetation canopies. The modelling of radiative transfer in mSCOPE is based on the classical SAIL theory. A solution to the radiative transfer equation for multi-layer canopies is given, which allows calculating top-of-canopy (TOC) reflectance and the flux profile. The latter is used for the simulation of fluorescence emission and photosynthesis of every leaf through the leaf radiative transfer model Fluspect and a biochemical model. The radiative transfer of fluorescence in multi-layer canopies is solved numerically in mSCOPE to obtain TOC bidirectional fluorescence. The significant effect of vertical heterogeneity of leaf properties on TOC reflectance, fluorescence and photosynthesis is demonstrated by different scenarios with customized vertical profiles of leaf chlorophyll content and leaf water content, and also with measured vertical profiles of leaf chlorophyll content in corn canopies. A preliminary validation of the reflectance calculating routine of mSCOPE is conducted by comparing measured and simulated TOC reflectance spectra of the corn canopies. We conclude that it is important to consider the vertical heterogeneity of leaf properties for the prediction of reflectance, fluorescence and photosynthesis. The model mSCOPE could serve as a tool to better understand vertically heterogeneous vegetation canopies.
Why it matches plant phenotyping methods垂直不均一な植物キャノピーの反射、蛍光、光合成を推定するモデルを開発し、実測反射スペクトルで予備検証しており、植物状態の取得・推定手法が中心である。
abstractWe developed a model for light interaction and energy balance in vegetation canopies in which leaf biophysical and biochemical properties vary in the vertical.
Grasslands are one of the ecosystems that have been strongly affected by anthropogenic impacts. The state-of-the-art in monitoring changes in grassland species composition is to conduct repeated plot-based vegetation surveys that assess the occurrence and cover of plants. These plot-based surveys are typically limited to comparably small areas and the quality of the cover estimates depends strongly on the experience and performance of the surveyors. Here, we investigate the possibility of a semi-automated, image-based method for cover estimates, by analyzing the applicability of very high spatial resolution hyperspectral data to classify grassland species at the level of individuals. This individual-oriented approach is seen as an alternative to community-oriented remote sensing depicting canopy reflectance as the total of mixed species reflectance. An AISA+ imaging spectrometer mounted on a scaffold was used to scan 1m² grassland plots and assess the impact of four sources of variation on the predicted species cover: (1) the spatial resolution of the scans, (2) complexity, i.e. species number and structural diversity, (3) the species cover and (4) the share of functional types (graminoids and forbs). Classifications were conducted using a support vector machine classification with a linear kernel, obtaining a median Kappa of ~0.8. Species cover estimations reached median r² and root mean square errors (RMSE) of ~0.6 and ~6.2% respectively. We found that the spatial resolution and diversity level (mainly structural diversity) were the most important sources of variation affecting the performance of the proposed approach. A spatial resolution below 1cm produced relatively good models for estimating species-specific coverages (r²=~0.6; RMSE=~7.5%) while predictions using pixel sizes over that threshold failed in this individual-oriented approach (r²=~0.17; RMSE=~20.7%). Areas with low inter-species overlap were better suited than areas with frequent inter-species overlap. We conclude that the application of very high resolution hyperspectral remote sensing in environments with low structural heterogeneity is suited for individual-oriented mapping of grassland plant species.
Why it matches plant phenotyping methods高空間分解能ハイパースペクトル画像と分類手法により、草地植物の個体別種被度という植物形質を推定し、空間分解能や構造多様性が推定性能に与える影響も評価しているため、フェノタイピング手法の実質的応用に該当する。
abstractwe investigate the possibility of a semi-automated, image-based method for cover estimates, by analyzing the applicability of very high spatial resolution hyperspectral data to classify grassland species at the level of individuals.
A wide range of ecological, agricultural, hydrological and meteorological applications at local to regional scales requires decametric biophysical data. However, before the launch of SENTINEL-2A, only few decametric products are produced and most of them remain limited by the small number of available observations, mostly due to a moderate revisit frequency combined with cloud occurrence. Conversely, kilometric and hectometric biophysical products are now widely available with almost complete and continuous coverage, but the associated spatial resolution limits the application over heterogeneous landscapes. The objective of this study is to combine unfrequent decametric spatial resolution products with frequent hectometric spatial resolution products to improve the temporal frequency and completeness of decametric observations. The study focuses on the fraction of photosynthetically active radiation absorbed by the green vegetation (FAPAR) because of its important role in canopy models and small dependency to scaling issues.An algorithm is developed to provide near real time estimates of FAPAR called DHF (for Decametric Hectometric Fusion) at a decametric resolution and dekadal time step. It is assumed that the FAPAR time course is described by a second-degree polynomial function over a limited 60-days temporal window for each decametric pixel. To reduce the dimensionality of the problem, landcover classes are considered instead of each individual pixel. For each class, the coefficients of the polynomial function are adjusted using the temporal course of the available decametric FAPAR products, under the constraint of providing a good match with the time course of the hectometric dekadal FAPAR products. The point spread function associated to the hectometric FAPAR products and the possible biases between the decametric and hectometric FAPAR products are explicitly accounted for.The algorithm was evaluated over a time series of decametric Landsat-8 FAPAR images (30m) and hectometric (330m) dekadal GEOV3 FAPAR derived from PROBA-V images acquired in 2014 over a site in the South-West of France.Results show that the estimated DHF FAPAR products capture well the expected seasonal variation and spatial distribution while improving the temporal frequency and spatial and temporal completeness of the original Landsat-8 products. A leave one out exercise shows that the DHF values are in very good agreement with the Landsat-8 FAPAR (RMSE=0.05–0.14) that were not used when computing the DHF. This demonstrates the robustness of the algorithm and interest under cloudy regions. Additional comparison with ground measurements collected over 14 sunflower fields along the growth season confirms the good performances of the DHF FAPAR products (RMSE=0.11).
Why it matches plant phenotyping methods衛星観測を融合して植物キャノピーのFAPARという生理・生物物理形質を推定するDHFアルゴリズムを開発し、衛星データと地上測定で技術検証しており、フェノタイピング手法が中心である。
abstractAn algorithm is developed to provide near real time estimates of FAPAR called DHF (for Decametric Hectometric Fusion) at a decametric resolution and dekadal time step.
Accurate estimation of leaf chlorophyll content (Cab) from remote sensing is of tremendous significance to monitor the physiological status of vegetation or to estimate primary production. Many vegetation indices (VIs) have been developed to retrieve Cab at the canopy level from meter- to decameter-scale reflectance observations. However, most of these VIs may be affected by the possible confounding influence of canopy structure. The objective of this study is to develop methods for Cab estimation using millimeter to centimeter spatial resolution reflectance imagery acquired at the field level.Hyperspectral images were acquired over sugar beet canopies from a ground-based platform in the 400–1000nm range, concurrently to Cab, green fraction (GF), green area index (GAI) ground measurements. The original image spatial resolution was successively degraded from 1mm to 35cm, resulting in eleven sets of hyperspectral images. Vegetation and soil pixels were discriminated, and for each spatial resolution, measured Cab values were related to various VIs computed over four sets of reflectance spectra extracted from the images (soil and vegetation pixels, only vegetation pixels, 50% darkest and brightest vegetation pixels). The selected VIs included some classical VIs from the literature as well as optimal combinations of spectral bands, including simple ratio (SR), modified normalized difference (mND) and structure insensitive pigment index (SIPI). In the case of mND and SIPI, the use of a blue reference band instead of the classical near-infrared one was also investigated.For the eleven spatial resolutions, the four pixel selections and the five VI formats, similar band combinations are obtained when optimizing VI performances: the main bands of interest are generally located in the blue, red, red-edge and near-infrared domains. Overall, mNDblue[728,850] defined as (R440−R728)/(R440+R850) and computed over the brightest green pixels obtains the best correlations with Cab for spatial resolutions finer than 8.8cm with a root mean square error of prediction better than 2.6μg/cm². Conversely, mNDblue[728,850] poorly correlates with variations in GF and GAI, thus reducing the risk of deriving non-causal relationships with Cab that would actually be due to the covariance between Cab and these canopy structure variables. As mNDblue[728,850] can be calculated from most current multispectral sensors, it is therefore a promising VI to retrieve Cab from millimeter- to centimeter-scale reflectance imagery.
Why it matches plant phenotyping methods高解像度ハイパースペクトル画像からサトウダイコンの葉クロロフィル含量を推定する方法を開発・評価しており、植物形質の取得が研究の中心である。
abstractThe objective of this study is to develop methods for Cab estimation using millimeter to centimeter spatial resolution reflectance imagery acquired at the field level.
Leaf area index estimates in dense evergreen tropical moist forest almost exclusively rest on indirect methods most of which being of limited accuracy or spatial resolution. In this study we examine the potential of full waveform Aerial Laser Scanning (ALS) to derive accurate spatially explicit estimates of Plant Area Index (PAI).A discrete representation of the forest canopy is introduced in the form of a 3D voxelized space. For each voxel (elementary volume, typically one cubic m) a first estimate of local transmittance of vegetation is computed as the ratio of the sum of energy exiting a voxel to the sum of energy entering the same voxel. A spatially hierarchical model is subsequently applied to refine estimates of individual voxel transmittance. Plant area density (PAD) profiles are then computed from the local transmittance values by applying Beer Lambert's turbid medium approximation. PAI values are obtained from vertical integration of PAD profiles. The model is shown to be robust to low sampling intensity and high occlusion rates.We further compared simulated values of gap fraction obtained by ray tracing for 5 angular sectors with in situ LAI2200 measurements taken at 135 positions in a 0.5ha forest plot located in the center of the scene. The overall patterns of simulated and measured values (average value per inclination and pattern of variation along a 70m transect line) were highly consistent. A slight but systematic discrepancy was observed along the inclination gradient, gap fractions derived from ray tracing in the voxelized scene being slightly lower than the measured values. This difference might be the consequence of multiple reflections which have been found to bias gap fractions estimates produced by LAI2200.PAI estimates derived from LAI2200 measurements (either simulated 6.8 or observed 5.9) are much lower than the PAI derived from vertical integration of local PAD (13.6). This large difference reflects the fact that distribution of foliage is strongly spatially structured and that this structural information is not properly accounted for in PAI estimates derived from mean gap fraction per elevation angle. After adjusting local transmittance to match mean LAI 2200 profiles the PAI at plot level was found to be 13.2m²·m⁻².We conclude that Aerial Laser Scanning can produce accurate maps of Plant Area Index over large areas with unmatched efficacy, accuracy and ease. This should be of major relevance for many forest ecological studies.
Why it matches plant phenotyping methods航空レーザースキャンから植物面積指数（PAI）を推定する手法を開発し、LAI2200との交差検証も行っており、森林キャノピーの明示的な植物形質計測が研究の中心である。
abstractIn this study we examine the potential of full waveform Aerial Laser Scanning (ALS) to derive accurate spatially explicit estimates of Plant Area Index (PAI).
Plant density is useful variable that determines the fate of the wheat crop. The most commonly used method for plant density quantification is based on visual counting from ground level. The objective of this study is to develop and evaluate a method for estimating wheat plant density at the emergence stage based on high resolution imagery taken from UAV at very low altitude with application to high throughput phenotyping in field conditions. A Sony ILCE α5100L RGB camera with 24Mpixels and equipped with a 60mm focal length lens was flying aboard an hexacopter at 3 to 7m altitude at about 1m/s speed. This allows getting ground resolution between 0.20mm to 0.45mm, while providing 59–77% overlap between images. The camera was looking with 45° zenith angle in a compass direction perpendicular to the row direction to maximize the cross section viewed of the plants and minimize the effect of the wind created by the rotors. Agisoft photoscan software was then used to derive the position of the cameras for each image. Images were then projected on the ground surface to finally extract subsamples used to estimate the plant density. The extracted images were first classified to separate the green pixels from the background and the rows were then identified and extracted. Finally, image object (group of connected green pixels) was identified on each row and the number of plants they contain was estimated using a Support Vector Machine whose training was optimized using a Particle Swarm Optimization.Three experiments were conducted in Gréoux, Avignon and Clermont sites with some variability in the sowing dates, densities, genotypes, flight altitude, and growth stage at the time of the image acquisition. The application of the method on the 270 samples available over the three sites provides a RMSE and relative RMSE on estimates of 34.05 plants/m² and 14.31% with a bias of 9.01 plants/m². However, differences in performances were observed between the three sites, mostly related to the growth stage at the time of the flight. Plants should have between one to two leaves when images are taken. Further, a specific sensitivity analysis shows that the ground resolution of the images should be better than 0.40mm. Finally, the repeatability of the method is good especially when images are taken from similar observational geometries. The current limits and possible improvements of the method proposed are finally discussed.
Why it matches plant phenotyping methodsUAV画像からコムギの出芽時植物密度を推定する画像ベース手法を開発・評価し、高スループット圃場フェノタイピングへの適用、精度・感度・再現性を検証しているため、方法が中心的である。
abstractThe objective of this study is to develop and evaluate a method for estimating wheat plant density at the emergence stage based on high resolution imagery taken from UAV at very low altitude with application to high throughput phenotyping in field conditions.
We investigated the use of multispectral thermal imagery to retrieve land surface emissivity and temperature. Conversely to concurrent methods, the temperature emissivity separation (TES) method simply requires single overpass without any ancillary information. This is possible since TES makes use of an empirical relationship that estimates the minimum emissivity ε-min from the emissivity spectral contrast captured over several channels, so-called maximum-minimum difference (MMD). In previous studies, the ε-min - MMD empirical relationship of TES was calibrated and validated for various sensor spectral configurations, where the proposed calibrations involved single or linearly mixed spectra of emissivity at the leaf or soil level. However, cavity effect should be taken into account at the vegetation canopy level, to avoid an underestimation of emissivity, especially for intermediate vegetation conditions between bare soil and full vegetation cover.The current study aimed to evaluate the performances of the TES method when applied to vegetation canopies with cavity effect. We used the SAIL-Thermique model to simulate a library of emissivity spectra for a wide range of soil and plant conditions, and we addressed the spectral configurations of recent and forthcoming sensors. We obtained good results for calibration and validation over the simulated library, except for full cover canopies because of the TES gray body problem. Consistent with previous studies, the calibration/validation results were better with more channels that capture emissivity spectral contrast more efficiently. Our TES calibrations provided larger ε-min values as compared to former studies, especially for intermediate vegetation cover. We explained this trend by the simulated spectral library that involved numerous vegetation canopies with cavity effect, thereby shifting up the ε-min - MMD empirical relationship. Consequently, our TES calibration provided larger (respectively lower) estimates of emissivity (respectively radiometric temperature) that were likely to be more realistic as compared to previous calibrations. Finally, SAIL-Thermique simulations permitted to show that increasing Leaf Area Index induced a displacement of the (ε-min, MMD) pairs along the empirical relationship. This was consistent with the TES underlying assumption, where any change in ε-min induces changes in MMD since ε-max is bounded on [0.98–1]. Further investigations should focus on validating the outcomes of the current study against ground-based measurements, and on assessing TES performances when accounting for instrumental and atmospheric perturbations.
Why it matches plant phenotyping methods植生キャノピーの放射率・放射温度を推定する熱画像手法（TES）を、植生キャノピーのシミュレーションで評価・校正・検証しており、植物状態の測定法が研究の中心である。
abstractThe current study aimed to evaluate the performances of the TES method when applied to vegetation canopies with cavity effect.
At the plant or stand level, leaf orientation is often highly anisotropic and heterogeneous, yet most analyses neglect such complexity. In many cases, this is due to the difficulty in measuring the spatial variation of the leaf angle distribution function. There is a critical need for a technique that can rapidly measure the leaf angle distribution function at any point in space and time. A new method was developed and tested that uses terrestrial LiDAR scanning data to rapidly measure the three-dimensional distribution of leaf orientation for an arbitrary volume of leaves. The method triangulates laser-leaf intersection points recorded by the LiDAR scan, which allows for easy calculation of normal vectors. As a byproduct, the triangulation also yields continuous surfaces that reconstruct individual leaves. In order to produce a probability density function for leaf orientation from triangle normal vectors, it is critical that the proper weighting be applied to each triangle. Otherwise, results will heavily bias toward normal vectors pointed toward the the LiDAR scanner. The method was validated using artificially generated LiDAR data where the exact leaf angle distributions were known, and in the field for an isolated tree and a grapevine canopy by comparing LiDAR-generated distribution functions to manual measurements. The artificial test cases demonstrated the consistency of the method, and quantitatively showed that errors in the predicted leaf angle distribution functions decreased as scan resolution was increased or as the density of leaves was increased. The isolated tree field validation showed qualitatively similar trends between manual and LiDAR measurements of distribution functions. Manual measurements of leaf orientation in the vineyard were shown to have large errors due to high leaf curvature, which illustrated the benefits of the more detailed LiDAR measurement method.
Why it matches plant phenotyping methodsLiDARを用いて葉の三次元配向と葉角度分布を抽出する手法を開発し、人工データおよび圃場で検証しており、植物形質取得手法が研究の中心である。
abstractA new method was developed and tested that uses terrestrial LiDAR scanning data to rapidly measure the three-dimensional distribution of leaf orientation for an arbitrary volume of leaves.
Accurate three-dimensional information on canopy structure contributes to better understanding of radiation fluxes within the canopy and the physiological processes associated with them. Small-footprint airborne laser scanning (ALS) data proved valuable for characterising the three-dimensional structure of forest canopies and the retrieval of biophysical parameters such as plant and leaf area index (PAI and LAI), fractional cover or canopy layering. Nevertheless, few studies analysed combined occluded and observed canopy elements in dense vegetation as a result of airborne laser scanning geometries. The occluded space contains a substantial amount of vegetation elements (i.e. leaf, needle and wood material), which are missing in the analysis of the three-dimensional canopy structure. Consequently, this will lead to erroneous retrieval of biophysical parameters. In this study, we introduce a voxel traversal algorithm to characterise ALS observation patterns inside a voxel grid. We analyse the dependence of occluded and unobserved canopy volume on pulse density, flight strip overlap and season of overflight in a temperate mixed forest. ALS measurements under leaf-on and leaf-off conditions were used. For cross-comparison purposes, terrestrial laser scanning (TLS) measurements on a 50×50m2 subplot under leaf-on conditions were used. TLS acquisitions were able to depict the three-dimensional structure of the forest plot in high detail, ranging up to the top-most canopy layer.Our results at 1m voxel size show that even with the highest average pulse density of 11pulses/m2, at least 25% of the forest canopy volume remains occluded in the ALS acquisition under leaf-on conditions. Comparison with TLS acquisitions further showed that roughly 28% of the vegetation elements detected by the TLS acquisitions were not detected by the ALS system due to occlusion effects. By combining leaf-on and leaf-off acquisitions, we were able to recover roughly 7% of the occluded vegetation elements from the leaf-on acquisition. We find that larger flight strip overlap can significantly increase the amount of observed canopy volume due to the added observation angles and increased pulse density.
Why it matches plant phenotyping methods森林キャノピーの観測・構造量を推定するボクセル走査アルゴリズムを開発し、ALSとTLSで検証しており、植物形質取得手法が研究の中心である。
abstractIn this study, we introduce a voxel traversal algorithm to characterise ALS observation patterns inside a voxel grid.
Accurate estimation of light use efficiency (LUE) of plant canopies is essential for calculating gross primary productivity (GPP) using LUE models and is also useful for calibrating process-based models for regional and global applications. A promising method for estimating LUE is through remote sensing of the photochemical reflectance index (PRI). However, there are internal (e.g. pigment concentrations) and external factors (e.g. environmental conditions and sun-target-view geometry) that affect PRI signals. Considering the reflectance difference between sunlit and shaded leaves, the ratio of observed canopy reflectance to leaf reflectance is used to represent the observed fraction of sunlit leaves, and the observed fraction of shaded leaves is calculated with a geometrical optical model. Thus, a canopy-level PRI observation is separated into sunlit and shaded PRI values, and a two-leaf canopy PRI (PRIt) is calculated as sum of these two values weighted by their respective sunlit and shaded leaf area indices. The usefulness of PRIt in assessing the canopy-level LUE is evaluated with automated multi-angle PRI observations acquired on a flux tower from April to September 2013 over a sub-tropical coniferous forest in southern China. In each 15-minute observation cycle, PRI is observed at four view zenith angles fixed at (37°, 47°, 57°) or (42°, 52°, 62°) and the instantaneous solar zenith angle in the azimuth angle range from 45° to 325° (from the geodetic north). In both the half-hourly and daily time steps, PRIt can effectively improve (>50% and >35% increases in R2, respectively) the ability as a proxy of LUE derived from the tower flux measurements over the big-leaf PRI taken as the arithmetic average of the multi-angle measurements in a given time interval. In the dry season from July to September, correlations of PRI with LUE at daily time steps are much stronger in the two-leaf case than in the big-leaf case. The correlation between PRIt and LUE is the strongest (R2=0.785, p<0.001) in July. PRIt is very effective in detecting the light and low-moderate drought stress on LUE at half-hourly time steps, while ineffective in detecting severe atmospheric water and heat stress, which is probably due to alternative radiative energy sink, i.e. photorespiration. Overall, the two-leaf approach well overcomes some external effects (e.g. sun-target-view geometry) that interfere with PRI signals.
Why it matches plant phenotyping methodsキャノピーPRIを日向葉・陰葉に分離してLUEを推定する計算・リモートセンシング手法を開発し、タワー観測で技術評価しており、植物生理状態の取得が中心である。
abstractThus, a canopy-level PRI observation is separated into sunlit and shaded PRI values, and a two-leaf canopy PRI (PRIt) is calculated as sum of these two values weighted by their respective sunlit and shaded leaf area indices.
Real-time prediction of vegetation phenology is critical for assisting crop monitoring, natural resource management, and land modeling in weather prediction systems. However, due to the lack of timely available satellite datasets and the inherent noise in time series, little attention has been paid to real-time and short-term predictions of vegetation phenology. The successful launch of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard operational Suomi National Polar-orbiting Partnership (Suomi NPP) satellite makes this research possible because it can provide land surface observations in a timely fashion. This study introduces an operational system that provides real-time and short-term predictions of vegetation phenology. Specifically, the system integrates timely available VIIRS observations and the climatology (expectation and standard deviation) of vegetation phenology from long-term MODIS data to simulate a set of potential temporal trajectories of greenness development at a given time for each pixel. These potential trajectories are then applied to identify spring green leaf development in real time, predict the occurrence of greenup onset, mid-greenup phase and maturity onset, and analyze the uncertainty of the prediction across a variety of ecosystems in North America. The accuracy of real-time and short-term predictions was evaluated by comparing with standard VIIRS detections and near-surface PhenoCam observations in both 2014 and 2015 across North America. The results showed that the real-time prediction of spring phenological metrics from VIIRS were all significantly correlated with those derived from PhenoCam datasets (R2>0.96, P<0.01) and closely comparable to the standard VIIRS detections with a mean absolute difference of <10days, 5days and 5days in greenup onset, mid-greenup phase and maturity onset, respectively. The mean absolute difference in the northern region for all three events was relatively smaller than that in the southern region. These findings demonstrate the capability of VIIRS observations to effectively predict temporal dynamics of vegetation phenology in real time at a continental scale.
Why it matches plant phenotyping methodsVIIRS衛星観測から植物の春季フェノロジー指標をリアルタイム推定・予測する運用システムを開発し、PhenoCam等で精度検証しており、植物状態の取得・推定手法が中心である。
abstractThis study introduces an operational system that provides real-time and short-term predictions of vegetation phenology.
Automatic detection and monitoring of freezing injury in crops is of vital importance for assessing plant physiological status and yield losses. This study investigates the potential of hyperspectral techniques for detecting leaves at the stages of freezing and post-thawing injury, and for quantifying the impacts of freezing injury on leaf water and pigment contents. Four experiments were carried out to acquire hyperspectral reflectance and biochemical parameters for oilseed rape plants subjected to freezing treatment. Principal component analysis and support vector machines were applied to raw reflectance, first and second derivatives (SDR), and inverse logarithmic reflectance to differentiate freezing and the different stages of post-thawing from the normal leaf state. The impacts on biochemical retrieval using particular spectral domains were also assessed using a multivariate analysis. Results showed that SDR generated the highest classification accuracy (>95.6%) in the detection of post-thawed leaves. The optimal ratio vegetation index (RVI) generated the highest predictive accuracy for changes in leaf water content, with a cross validated coefficient of determination (R²cv) of 0.85 and a cross validated root mean square error (RMSEcv) of 2.4161mg/cm². Derivative spectral indices outperformed multivariate statistical methods for the estimation of changes in pigment contents. The highest accuracy was found between the optimal RVI and the change in carotenoids content (R²CV=0.70 and RMSECV=0.0015mg/cm²). The spectral domain 400–900nm outperformed the full spectrum in the estimation of individual pigment contents, and hence this domain can be used to reduce redundancy and increase computational efficiency in future operational scenarios. Our findings indicate that hyperspectral remote sensing has considerable potential for characterizing freezing injury in oilseed rape, and this could form a basis for developing satellite remote sensing products for crop monitoring.
Why it matches plant phenotyping methods凍害状態のハイパースペクトル検出と葉の水分・色素含量推定手法を開発・評価しており、植物表現型の取得・抽出が研究の中心である。
abstractThis study investigates the potential of hyperspectral techniques for detecting leaves at the stages of freezing and post-thawing injury, and for quantifying the impacts of freezing injury on leaf water and pigment contents.
Remote sensing of plant carbon uptake, or gross primary production (GPP), in a repeatable and consistent manner remains a key element of a comprehensive understanding of the role of vegetation within the global carbon cycle. To further this understanding at a landscape level or global scale, accurate remote sensing of photosynthetic light-use efficiency (LUE) is required to understand photosynthetic down-regulation and environmental constraints to plant photosynthesis. The past decade has seen advances in detecting both leaf- and canopy-level physiological stress behaviours using the photochemical reflectance index (PRI), a narrow-waveband normalized difference index that relates LUE to a xanthophyll-induced absorption feature at 531nm. To date, however, much of this research has occurred using top of canopy measurements, while our understanding of the vertical distribution of LUE within the crown is limited. In this study, we demonstrate an approach which could be used to scale photosynthetic behaviour of vegetation vertically and horizontally using estimates of vertical canopy structure obtained from terrestrial Light Detection and Ranging (LiDAR) data to predict proportions of shaded and sunlit canopy which are then linked to predictions of LUE. We apply the approach over a mature Aspen study site located in central Saskatchewan, Canada utilising full-waveform LiDAR data provided by the ground-based laser scanner system and canopy spectra obtained by the AMSPEC II spectro-radiometer. Agreement between predictions of Gross Primary Productivity (GPP) using the developed approach compared to independent observations was highly significant at hourly intervals (R2=0.80, p<0.01) under clear sky conditions. A range of LUE vertical profiles for different stand structures across the growing season were developed providing estimations of how crown structure can impact LUE vertically in the crown. We conclude with a recommendation for ongoing research to verify these types of trends using concurrently acquired, independently derived leaf LUE from photosynthesis light-response curves, and forest structure variation from LiDAR, to provide a more exact quantification of these patterns.
Why it matches plant phenotyping methodsLiDARと分光計を統合し、樹冠構造から光利用効率とGPPの垂直分布を推定する植物生理形質計測手法を開発・検証しており、フェノタイピング手法が中心である。
abstractwe demonstrate an approach which could be used to scale photosynthetic behaviour of vegetation vertically and horizontally using estimates of vertical canopy structure obtained from terrestrial Light Detection and Ranging (LiDAR) data to predict proportions of shaded and sunlit canopy which are then linked to predictions of LUE.
Stereogrammetry applied to globally available high resolution spaceborne imagery (HRSI; Larix forests with slopes 35° and < 25° (during snow-free conditions) produced characteristic and consistently distinct distributions of elevation differences from reference lidar. The former include DSMs of near-ground surfaces with root mean square errors < 0.68 m relative to lidar. The latter, particularly those with angles < 10°, show distributions with larger differences from lidar that are associated with open canopy forests whose vegetation surface elevations are captured. Terrain aspect did not have a strong effect on the distribution of vegetation surfaces. Using the two DSM types together, the distribution of DSM-differenced heights in forests (μ = 6.0 m, σ = 1.4 m) was consistent with the distribution of plot-level mean tree heights (μ = 6.5 m, σ = 1.2 m). We conclude that the variation in sun elevation angle at time of stereopair acquisition can create illumination conditions conducive for capturing elevations of surfaces either near the ground or associated with vegetation canopy. Knowledge of HRSI acquisition solar geometry and snow cover can be used to understand and combine stereogrammetric surface elevation estimates to co-register and difference overlapping DSMs, providing a means to map forest height at fine scales, resolving the vertical structure of groups of trees from spaceborne platforms in open canopy forests.
Why it matches plant phenotyping methodsステレオ画像と太陽高度を用いて森林樹冠高を推定し、LiDARおよびプロット平均樹高と比較検証する手法が中心であるため。
abstractUsing the two DSM types together, the distribution of DSM-differenced heights in forests (μ = 6.0 m, σ = 1.4 m) was consistent with the distribution of plot-level mean tree heights (μ = 6.5 m, σ = 1.2 m).
Forest vegetation classification and structure measurements are fundamental steps for planning, monitoring, and evaluating large-scale forest changes including restoration treatments. High spatial and spectral resolution remote sensing data are critically needed to classify vegetation and measure their 3-dimensional (3D) canopy structure at the level of individual species. Here we test high-resolution lidar, hyperspectral, and multispectral data collected from unmanned aerial vehicles (UAV) and demonstrate a lidar-hyperspectral image fusion method in treated and control forests with varying tree density and canopy cover as well as in an ecotone environment to represent a gradient of vegetation and topography in northern Arizona, U.S.A. The fusion performs better (88% overall accuracy) than either data type alone, particularly for species with similar spectral signatures, but different canopy sizes. The lidar data provides estimates of individual tree height (R²=0.90; RMSE=2.3m) and crown diameter (R²=0.72; RMSE=0.71m) as well as total tree canopy cover (R²=0.87; RMSE=9.5%) and tree density (R²=0.77; RMSE=0.69 trees/cell) in 10m cells across thin only, burn only, thin-and-burn, and control treatments, where tree cover and density ranged between 22 and 50% and 1–3.5 trees/cell, respectively. The lidar data also produces highly accurate digital elevation model (DEM) (R²=0.92; RMSE=0.75m). In comparison, 3D data derived from the multispectral data via structure-from-motion produced lower correlations with field-measured variables, especially in dense and structurally complex forests. The lidar, hyperspectral, and multispectral sensors, and the methods demonstrated here can be widely applied across a gradient of vegetation and topography for monitoring landscapes undergoing large-scale changes such as the forests in the southwestern U.S.A.
Why it matches plant phenotyping methodsUAV LiDAR・ハイパースペクトル融合による個体樹木の高さ、樹冠径、被覆率、密度推定を開発・検証しており、植物形質取得が研究の中心である。
abstractdemonstrate a lidar-hyperspectral image fusion method
The 3D distribution of plant material is a key parameter to describe vegetation structure, which influences several processes such as radiation interception and ecosystem functioning. Vegetation covers are often described using Leaf Area Index (LAI) or Plant Area Index (PAI) for monitoring or modeling purposes. Characterizing vegetation 3D structure at fine scale is increasingly required, notably in order to be able to apply radiative transfer simulations at scales consistent with the spatial resolution of recent remote sensing sensors. To assess 3D PAI of a vegetation plot, this paper evaluates the potential of a voxelization method using Terrestrial Laser Scanning (TLS) data, based on the Beer-Lambert transmittance computation law. The theoretical validation was performed using a simulation framework based on a radiative transfer model (DART). The framework allowed simulating TLS acquisition on a theoretical distribution of leaves and a realistic representation (single tree), for which all characteristics are well known. Hence, a sensitivity analysis was performed to study the influence of instrument parameters (i.e. single- or multi-echo, beam divergence), scanning configuration (scan angle step), vegetation characteristics (leaf size and density, leaf angle distribution), and voxel parameters (cubic versus spherical geometry, at different resolutions, with and without occlusion) on the estimation of PAI. For a theoretical distribution of leaves, results showed good accuracy of the voxelization method (R²=0.91 and RMSE = 20% for a mean case, at voxel level) with a high resolution multi-echo TLS scan, cubic voxels over 0.5-m resolution, low inter-voxel occlusion, small leaves, and up to a surface density of 2 m².m⁻³. Error increased with a larger scan angle resolution, single echo TLS systems, and vegetation density. Also, without clumping, error increased with smaller voxels or larger leaves. Best results were obtained with multi-echo TLS scans (angular resolution of 0.05°), cubic voxels at 1-m resolution when occlusion is low (voxel sampling higher than 50% of maximum sampling at 15m) and small leaves (e.g. 10 cm²), which provided very good agreement (RMSD=7.6%, R²=0.98, p=0.99). On a realistic isolated tree, PAI was correctly assessed with cubic voxels at 0.25m resolution. A method to merge voxelized scans was proposed to deal with inter-voxel occlusion effects.
Why it matches plant phenotyping methodsTLSデータのボクセル化により植物群落の3D構造およびPAIを推定する手法を開発・理論検証し、機器・走査・植生・ボクセル条件の感度分析を行っているため、植物フェノタイピング手法が中心である。
abstractTo assess 3D PAI of a vegetation plot, this paper evaluates the potential of a voxelization method using Terrestrial Laser Scanning (TLS) data, based on the Beer-Lambert transmittance computation law.
As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤5m) from very-high-resolution (VHR) data. We assessed a 432ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severity was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2m spatial resolution. We first evaluated our approach using 1m by 1m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15m by 15m fixed-area plots (inter-crown scale) with the post-fire 0.10m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). This work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the <30m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.
Why it matches plant phenotyping methods高解像度衛星画像とスペクトル解析により森林の火災被害（burn severity）を樹冠・区画スケールで推定し、航空写真等で精度検証しており、植物状態の取得手法が研究の中心である。
abstractThe mapping of forest burn severity was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2m spatial resolution.
Recent advancements in remote sensing technology, specifically Light Detection and Ranging (LiDAR) sensors, provide the data needed to quantify forest characteristics at a fine spatial resolution over large geographic domains. From an inferential standpoint, there is interest in prediction and interpolation of the often sparsely sampled and spatially misaligned LiDAR signals and forest variables. We propose a fully process-based Bayesian hierarchical model for above ground biomass (AGB) and LiDAR signals. The process-based framework offers richness in inferential capabilities, e.g., inference on the entire underlying processes instead of estimates only at pre-specified points. Key challenges we obviate include misalignment between the AGB observations and LiDAR signals and the high-dimensionality in the model emerging from LiDAR signals in conjunction with the large number of spatial locations. We offer simulation experiments to evaluate our proposed models and also apply them to a challenging dataset comprising LiDAR and spatially coinciding forest inventory variables collected on the Penobscot Experimental Forest (PEF), Maine. Our key substantive contributions include AGB data products with associated measures of uncertainty for the PEF and, more broadly, a methodology that should find use in a variety of current and upcoming forest variable mapping efforts using sparsely sampled remotely sensed high-dimensional data.
Why it matches plant phenotyping methodsLiDAR信号から森林の地上部バイオマス（AGB）を推定・補間する階層ベイズ手法を開発し、シミュレーションと実データで評価しているため、植物群落レベルの形質推定法が中心である。
abstractWe propose a fully process-based Bayesian hierarchical model for above ground biomass (AGB) and LiDAR signals.
This work presents for the first time a demonstration with satellite data of polarimetric SAR interferometry (PolInSAR) applied to the retrieval of vegetation height in rice fields. Three series of dual-pol interferometric SAR data acquired with large baselines (2–3 km) by the TanDEM-X system during its science phase (April–September 2015) are exploited. A novel inversion algorithm especially suited for rice fields cultivated in flooded soil is proposed and evaluated. The validation is carried out over three test sites located in geographically different areas: Sevilla (SW Spain), Valencia (E Spain), and Ipsala (W Turkey), in which different rice types are present. Results are obtained during the whole growth cycle and demonstrate that PolInSAR is useful to produce accurate height estimates (RMSE 10–20 cm) when plants are tall enough (taller than 25–40 cm), without relying on external reference information.
Why it matches plant phenotyping methods衛星PolInSARによるイネ群落高という明示的な植物形質の推定手法を開発・評価し、複数地域で検証しているため、方法が中心的である。
abstractThis work presents for the first time a demonstration with satellite data of polarimetric SAR interferometry (PolInSAR) applied to the retrieval of vegetation height in rice fields.
Leaf pigments provide valuable information about plant physiology. High resolution monitoring of their dynamics will give access to better understanding of processes occurring at different scales, and will be particularly important for ecologists, farmers, and decision makers to assess the influence of climate change on plant functions, and the adaptation of forest, crop, and other plant canopies. In this article, we present a new version of the widely-used PROSPECT model, hereafter named PROSPECT-D for dynamic, which adds anthocyanins to chlorophylls and carotenoids, the two plant pigments in the current version. We describe the evolution and improvements of PROSPECT-D compared to the previous versions, and perform a validation on various experimental datasets. Our results show that PROSPECT-D outperforms all the previous versions. Model prediction uncertainty is decreased and photosynthetic pigments are better retrieved. This is particularly the case for leaf carotenoids, the estimation of which is particularly challenging. PROSPECT-D is also able to simulate realistic leaf optical properties with minimal error in the visible domain, and similar performances to other versions in the near infrared and shortwave infrared domains.
Why it matches plant phenotyping methods葉の光学特性と色素量を推定するPROSPECT-Dモデルの開発・改良および実験データによる検証が中心であり、植物形質取得手法に該当する。
abstractwe present a new version of the widely-used PROSPECT model, hereafter named PROSPECT-D for dynamic, which adds anthocyanins to chlorophylls and carotenoids
Stereogrammetry applied to globally available high resolution spaceborne imagery (HRSI; 35° and <25° (during snow-free conditions) produced characteristic and consistently distinct distributions of elevation differences from reference lidar. The former include DSMs of near-ground surfaces with root mean square errors<0.68m relative to lidar. The latter, particularly those with angles<10°, show distributions with larger differences from lidar that are associated with open canopy forests whose vegetation surface elevations are captured. Terrain aspect did not have a strong effect on the distribution of vegetation surfaces. Using the two DSM types together, the distribution of DSM-differenced heights in forests (μ=6.0m, σ=1.4m) was consistent with the distribution of plot-level mean tree heights (μ=6.5m, σ=1.2m). We conclude that the variation in sun elevation angle at time of stereopair acquisition can create illumination conditions conducive for capturing elevations of surfaces either near the ground or associated with vegetation canopy. Knowledge of HRSI acquisition solar geometry and snow cover can be used to understand and combine stereogrammetric surface elevation estimates to co-register and difference overlapping DSMs, providing a means to map forest height at fine scales, resolving the vertical structure of groups of trees from spaceborne platforms in open canopy forests.
Why it matches plant phenotyping methods衛星ステレオ画像と太陽高度を利用して森林の植生・樹高を推定し、LiDARおよびプロット平均樹高と比較検証する測定手法が研究の中心であるため。
abstractUsing the two DSM types together, the distribution of DSM-differenced heights in forests (μ=6.0m, σ=1.4m) was consistent with the distribution of plot-level mean tree heights (μ=6.5m, σ=1.2m).
Accurate estimates of the quality and quantity of remnant habitats is critical for planning management activities for the conservation of threatened species. Although habitat quality usually is understood from a multidimensional niche space approach, the availability of foraging substrates can be a suitable and more proximate index of habitat quality for species with specialized habitat requirements, like woodpeckers that feed almost exclusively on larvae of wood-boring beetles in the trunks and branches of infested trees. Recent approaches use simple mathematical algorithms on spectral bands called Vegetation Indices (VI) to identify infestations, providing a new opportunity to assess habitat quality for woodpeckers. In this paper, we tested the ability of 102 VI to estimate tree attributes explaining habitat quality for Magellanic woodpeckers for its usage as a reliable foraging habitat quality estimator. We hypothesized that space use of Magellanic woodpeckers is positively associated with the spatial distribution of decayed trees in the landscape. We developed a methodological framework based on high-resolution, multispectral imagery with three basic steps. First, we mapped individual Nothofagus trees based on estimates of species composition from a supervised classification procedure, VI estimates and image segmentation. Second, we selected the best VI predicting the tree quality for Magellanic woodpeckers. Third, we tested these habitat quality predictors, the species composition and tree age, by using two Synoptic Models of Space Use (SMSU) of Magellanic woodpeckers based on very high-frequency (VHF) radio-telemetry and global positioning system (GPS) telemetry data.Generalized Linear Mixed Models (GLMM) showed that the VI that best predicted habitat quality at the tree-scale was the Plant Senescence Reflectance Index (PSRI, computed as [Red-Blue]/Red-edge), included in almost all the most parsimonious models. The most parsimonious SMSU included only PSRI as an independent covariate, with a strong positive relation. Although coefficient differences were found between telemetry data (VHF vs. GPS data) both showed a positive overall response. Consequently, Red-edge based PSRI can be considered a reliable estimator of tree-scale foraging habitat quality at landscape extents for future research and management activities including Magellanic woodpeckers living on heterogeneous Nothofagus forests.
Why it matches plant phenotyping methods高解像度マルチスペクトル画像と102種の植生指数を用いて個体樹木の状態・品質を推定し、最適指数を検証する測定ワークフローが研究の中心であるため、植物状態の画像ベース計測として含める。
abstractIn this paper, we tested the ability of 102 VI to estimate tree attributes explaining habitat quality for Magellanic woodpeckers for its usage as a reliable foraging habitat quality estimator.
This paper deals with the retrieval of agricultural crop height from space by using multipolarization Synthetic Aperture Radar (SAR) images. Coherent and incoherent crop height estimation methods are discussed for the first time with a unique TanDEM-X dataset acquired over rice cultivation areas. Indeed, with its polarimetric and interferometric capabilities, the TanDEM-X mission enables the tracking of crop height through interferometric SAR (InSAR), polarimetric interferometric SAR (PolInSAR) and the inversion of radiative transfer-based backscattering model. The paper evaluates the three aforementioned techniques simultaneously with a data set acquired in September 2014 and 2015 over rice fields in Turkey during their reproductive stage. The assessment of the absolute height accuracy and the limitations of the approaches are provided. In-situ measurements conducted in the same cultivation periods are used for validation purposes. The PolInSAR and morphological backscattering model results showed better performance with low RMSEs (12 and 13cm) compared to the differential InSAR result having RMSE of 18cm.The spatial baseline, i.e. the distance between satellites, is a key parameter for coherent methods such as InSAR and PolInSAR. Its effect on the absolute height accuracy is discussed using TanDEM-X pairs separated by a baseline of 101.7m and 932m. Although the InSAR based approach is demonstrated to provide sufficient crop height accuracy, the availability of a precise vegetation-free digital elevation model and a structurally dense crop are basic requirements for achieving high accuracy. The PolInSAR approach provides reliable crop height estimation if the spatial baseline is large enough for the inversion. The impact of increasing spatial baseline on the absolute accuracy of the crop height estimation is evident for both methods. However, PolInSAR is more cost-efficient, e.g. there is no need for phase unwrapping and any external vegetation free surface elevation data. Instead, the usage of radiative transfer based backscattering models provides not only crop height but also other biophysical properties of the crops with consistent accuracy. The efficient retrieval of crop height with backscattering model is achieved by metamodelling, which makes the computational cost of backscattering inversion comparable to the ones of the coherent methods. However, effectiveness depends on not only the backscattering model, but also the integration of agronomic crop growth rules. Motivated by these results, a combination of backscattering and PolInSAR inversion models would provide a successful method of future precision farming studies.
Why it matches plant phenotyping methodsSARを用いた作物高さという明示的な植物形質の推定手法を比較・評価し、現地測定で精度検証しているため、フェノタイピング手法が中心です。
abstractThis paper deals with the retrieval of agricultural crop height from space by using multipolarization Synthetic Aperture Radar (SAR) images.
Crop biomass information is of great importance for a variety of applications, ranging from supporting farm management decisions to modeling the crop-environment system. Dimensionless spectral vegetation index values derived from satellite imagery are commonly used to derive crop biomass. However, the highly empirical nature of spectrally derived biomass estimates requires frequent and costly calibration with manually collected ground data. Recently, low cost, autonomously operating terrestrial laser scanners (ATLSs) have become available for near-surface applications. In contrast to the dimensionless nature of spectral index values, autonomous light detection and ranging (lidar) technology measures physical vegetation structure by recording the x, y, z coordinates of canopy components at very high spatial (<10cm), and temporal (<2days) resolution. The objective of this study was to assess the suitability of an ATLS to i) monitor crop growth dynamics and ii) calibrate satellite imagery for estimating crop biomass. Wheat (Triticum aestivum spp.) growth was monitored by acquiring hypertemporal (every 28h for a full growing season) ATLS data at three different field sites across a range of experimentally manipulated crop growth conditions. The ATLS-derived crop height explained nearly three-quarters of the variability in destructively sampled wheat biomass (r2=0.74, RMSE=514.20kgha−1), showing a slightly stronger correlation to crop biomass than did leaf area index (LAI) measurements collected in the field using a LAI-2000 Plant Canopy Analyzer (r2=0.71, RMSE=546kgha−1). Satellite-based crop biomass estimates calibrated with ATLS data captured the variability in wheat biomass throughout a farm field with a biomass error of 730.96 and 727.60kgha−1 (RMSE) during the jointing (Development stage: Zadoks 37) and heading (Development stage: Zadoks 50) growth stages, respectively. These findings suggest that the hypertemporal lidar information provided via ATLS technology could constitute a major step forward in operational monitoring and mapping of crop biomass.
Why it matches plant phenotyping methodsATLSハイパーテンポラルLiDARを用いて作物高を自動測定し、バイオマス推定・衛星校正への適用性を検証することが中心であり、植物形質取得手法の開発・技術評価に該当する。
titleAn automated method to quantify crop height and calibrate satellite-derived biomass using hypertemporal lidar
We present the Fluspect-B model (generally referred to as Fluspect), which simulates leaf chlorophyll fluorescence (ChlF), reflectance and transmittance spectra. The existing PROSPECT model and its concept of a compact leaf are used as a starting point, and the differential equations for radiative transfer within the leaf are solved by an efficient doubling algorithm. Due to the simplicity of these equations, Fluspect offers a high computational speed. With incident light provided as the main input parameter, Fluspect calculates the emission of ChlF on both the illuminated and shaded side of the leaf. Other input parameters are chlorophyll and carotenoid concentrations, leaf water, dry matter and senescent material (brown pigments) content, leaf mesophyll structure parameter and ChlF quantum efficiency for the two photosystems, PS-I and PS-II. We investigated the model performance using measurements of leaf reflectance, transmittance and ChlF spectra, collected for barley and sugar beet leaves in both a laboratory and outdoors setting. The plants had been grown under various illumination conditions to increase between-leaf variability of leaf biochemical and structural properties. We retrieved the model parameters, compared them to corresponding destructive measurements and finally, used them to simulate ChlF on either side of the leaf at several light intensities. The results show that the model reproduces observed SIF accurately, especially for leaves measured under natural illumination. Most of the observed between-leaf variability of ChlF could be explained from differences in leaf biochemical and structural properties, with potential additional information held by ChlF emission efficiency parameters.
Why it matches plant phenotyping methods葉の蛍光・反射・透過スペクトルから生理・構造特性を推定するFluspectモデルを開発し、実測スペクトルとの比較で性能検証しており、植物表現型取得・推定手法が研究の中心です。
abstractWe present the Fluspect-B model (generally referred to as Fluspect), which simulates leaf chlorophyll fluorescence (ChlF), reflectance and transmittance spectra.
Global vegetation distribution is the result of environmental conditions and the genetic make-up of plants. In view of the intensive discussions on climate change and its effect on living organisms on earth, it has become necessary to develop new methods and strategies to monitor and follow the changes in global plant distribution.In this research, we focused on a vegetation analysis using spectral reflectance profiles linked to biochemical concentrations. We performed the spectroscopic measurements of Pistacia species at canopy and leaf levels. Concomitant chemical analyses of leaf and bark materials enabled the development of remote sensing (RS) indices of structure-related biochemicals and pigments. We developed RS indices for cellulose, lignin, chlorophyll, carotenoid, anthocyanin and wax. Since the wax was the least studied in this context and due to the fact that it is the first layer of the plant surface that interacts with the incident light, it deserved special attention and was assumed to affect the spectral reflectance and, in combination with the other biochemicals, to contribute to better species identification.In the modeling process, we showed that apart from the major energy absorption spectral bands related to a given biochemical, there were supplementary bands that had a significant effect on the accuracy of the biochemical content estimation. Another factor affecting the accuracy of the biochemical estimation was the season. Thus, we divided the biochemical content estimation models into seasonal groups of spring, summer and fall. The RS indices developed in this work, together with literature-reported RS indices, were used for Pistacia classification. The accuracy (69%) of species classification was significantly higher in spring at an early vegetation stage than in summer (50%), and fall (37%). As a proof of concept, the same sets of RS indices were also used for classifying the genera and families of various plants in the Mediterranean forest using EO1 Hyperion images. The accuracy of the classification maps was 79%, when the full set of RS indices developed in this work was used.
Why it matches plant phenotyping methods植物の生化学物質・色素量を推定するリモートセンシング指標とモデルを開発し、季節別の推定精度および植物分類で評価しているため、植物表現型取得法が中心です。
abstractenabled the development of remote sensing (RS) indices of structure-related biochemicals and pigments
Satellite remote sensing provides continuous observations of vegetation properties that can be used to estimate global terrestrial ecosystem gross primary production (GPP). The Photochemical Reflectance Index (PRI) has been shown to be sensitive to vegetation photosynthetic light use efficiency (LUE), GPP and canopy water-stress. Here, we use the NASA EOS MODIS (Moderate Resolution Imaging Spectroradiometer) based PRI with eddy covariance CO₂ flux measurements and meteorological observations from 20 tower sites representing major plant functional type (PFT) classes within the continental USA (CONUS) to assess GPP sensitivity to soil moisture related water stress. The sPRI (scaled PRI) metric derived using MODIS band 13 as a reference channel (sPRI₁₃) shows generally higher correspondence with tower GPP estimates than other potential MODIS reference bands. The sPRI₁₃ observations were used as a proxy for soil moisture related water supply constraints to LUE within a satellite data driven terrestrial carbon flux model to estimate GPP (GPPPRI). The GPPPRI calculations show generally favorable correspondence with tower GPP estimates (0.457≤R²≤0.818), except for lower GPPPRI performance over evergreen needleleaf forest (ENF) sites. A regional model sensitivity analysis using the sPRI₁₃ as a water supply proxy indicated that water restrictions limit GPP over more than 21% of the CONUS domain, particularly in drier climate areas where atmospheric moisture deficits (VPD) alone are insufficient to represent both atmosphere demand and water supply controls affecting productivity. Our results indicate strong potential of the MODIS sPRI₁₃ to represent soil moisture related water supply controls influencing photosynthesis, with enhanced (1-km resolution) delineation of these processes closer to the scale of in situ tower observations. These observations may provide an effective tool for characterizing sub-grid spatial heterogeneity in soil moisture related controls that inform coarser scale observations and estimates determined from other satellite observations and earth system models.
Why it matches plant phenotyping methodsMODIS由来のPRIを用いて植生キャノピーの水ストレス・光合成効率・GPPを推定し、タワー観測と比較検証しているため、植物生理状態の取得手法が中心です。
abstractThe sPRI₁₃ observations were used as a proxy for soil moisture related water supply constraints to LUE within a satellite data driven terrestrial carbon flux model to estimate GPP (GPPPRI).
Landsat time-series multi-spectral data, GLAS (Geoscience Laser Altimeter System) height data and a regression tree model were used to estimate tree height for a transect in Sub-Saharan Africa ranging from the Sahara Desert through the Congo Basin to the Kalahari Desert (+22 to −22° latitude and 23 to 24° longitude). Objectives included comparing the performance of Landsat 7- and 8-derived inputs separately and combined in mapping tree height at a regional scale, assessing the relative value of good observation counts and different Landsat spectral inputs for tree height estimation across a range of environments, and describing tree height distributions and discontinuities in Sub-Saharan Africa. A total of 5371 images were processed and per pixel quality assessed to create a set of multi-temporal metrics for the 2013 and 2014 calendar years for Landsat 7 only, Landsat 8 only and both Landsat 7 and 8 combined. Differences in performance were slight between different sensor inputs. However, performance generally improved with increasing numbers of good observations. Metrics derived from red reflectance data contributed most in estimating tree height. The regression tree algorithm accurately reproduced the LiDAR-derived height training data with an overall mean absolute error (MAE) for tree height estimation of 2.45m using integrated Landsat 7 and 8 data. Significant underestimations were quantified for tall tree cover (MAE of 4.65m for >20m heights) and overestimations for low/no tree cover (MAE 1.61 for <5m heights). Resulting tree distributions were found to be discontinuous with a primary dry seasonal woodlands cluster of 5–10m in height, a second cluster of primarily dry evergreen forest tree cover from 11–17m, and a third cluster of humid evergreen forest tree cover ≥18m. The integration of Landsat 7 and 8 and forthcoming Sentinel 2 time-series optical data to extend the value of LiDAR forest structure measurements is recommended.
Why it matches plant phenotyping methods衛星・LiDARデータと回帰木により樹高を推定し、センサー入力の比較と推定誤差の検証を行っており、植物形質の取得・検証が研究の中心である。
abstractObjectives included comparing the performance of Landsat 7- and 8-derived inputs separately and combined in mapping tree height at a regional scale
Advances in phenotyping technology are critical to ensure the genetic improvement of crops meet future global demands for food and fuel. Field-based phenotyping platforms are being evaluated for their ability to deliver the necessary throughput for large scale experiments and to provide an accurate depiction of trait performance in real-world environments. We developed a dual-camera high throughput phenotyping (HTP) platform on an unmanned aerial vehicle (UAV) and collected time course multispectral images for large scale soybean [Glycine max (L.) Merr.] breeding trials. We used a supervised machine learning model (Random Forest) to measure crop geometric features and obtained high correlations with final yield in breeding populations (r=0.82). The traditional yield estimation model was significantly improved by incorporating plot row length as covariate (p<0.01). We developed a binary prediction model from time-course multispectral HTP image data and achieved over 93% accuracy in classifying soybean maturity. This prediction model was validated in an independent breeding trial with a different plot type. These results show that multispectral data collected from the UAV-based HTP platform could improve yield estimation accuracy and maturity recording efficiency in a modern soybean breeding program.
Why it matches plant phenotyping methodsUAV搭載マルチスペクトル画像による作物形状・収量推定および成熟期予測のHTPプラットフォームとモデルを開発し、独立試験で検証しており、表現型取得・推定手法が中心である。
abstractWe developed a dual-camera high throughput phenotyping (HTP) platform on an unmanned aerial vehicle (UAV) and collected time course multispectral images for large scale soybean [Glycine max (L.) Merr.] breeding trials.
Our ability to measure and map plant function at multiple ecological scales is critical for understanding current and future changes in Earth's ecosystems and the global carbon budget. Conventional plant functional types (cPFTs) based on a few productivity-related traits have been previously used to simplify and represent major differences in global plant functions, but more recent research has directly focused on the use of functional trait information. Still, sampling limitations have constrained efforts to truly understand the variance and covariance of functional traits globally. Reflectance spectra offer a fast, repeatable, simultaneous measurement of a wide variety of leaf functional traits and could be used to optically define leaf functional types. To evaluate this concept, we measured leaf reflectance from a wide range of species in a diverse set of ecosystems across central and northern California, including observations from multiple individuals, sites, and seasons. Using principal components analysis, we analyzed spectral variation in relation to categorical attributes such as species and cPFTs, as well as to a set of functional trait metrics calculated from the spectra. We found the first three principal components (PCs) to be weakly related to categorical attributes and more strongly related to spectrally-derived functional metrics. Each PC was more strongly associated with different portions of the spectrum and contained different functional information. We applied a hybrid clustering algorithm to the PC coordinates of the observations to define potential optical leaf functional types. Twelve spectral clusters were identified, and these did not correspond directly to either single cPFTs or species. However, each cluster had a unique functional metric profile. Clusters represented both inter- and intra-species and cPFT functional differences driven by taxonomy, trait evolution and environmental responses, demonstrating their value as optical leaf functional types and the value of the clustering approach used here for defining optical types from leaf spectra. Our findings support the notion that cPFTs do not adequately capture differences in leaf function. They demonstrate that spectral measurements can be used to improve both the definition of PFTs as well as our knowledge regarding the covariance of functional traits within these classes.
Why it matches plant phenotyping methods葉の反射スペクトルから機能形質を推定し、PCAとクラスタリングで光学的葉機能タイプを定義する方法が研究の中心であるため、植物フェノタイピング手法として収載する。
abstractReflectance spectra offer a fast, repeatable, simultaneous measurement of a wide variety of leaf functional traits and could be used to optically define leaf functional types.
The screening of sensitive spectral indictors is essential for quantitative diagnosis of ozone (O3)-induced stress in plants. Four wheat (Triticum aestivum L.) cultivars with different degrees of O3-tolerance were grown under an elevated O3 concentration (E-O3) in fully open-air field conditions for two consecutive growth seasons from 2012 to 2013. The aim was to find sensitive hyperspectral indictors for real-time detection of O3 effects. The results showed that E-O3 caused a significant decrease in leaf thickness and pigment concentrations, resulting in a change in leaf reflectance. The effects of E-O3 on both physiological variables and reflectance characteristics were wheat cultivar-specific, with a greater and earlier O3 effect found in the O3-sensitive wheat cultivars than in the O3-tolerant cultivars. Spectral indices that were previously developed to detect leaf biological variables were examined, and highly correlated relations were found between the chlorophyll content and the three spectral parameters ND705, mND705 and R550, with Pearson r values of 0.896, 0.892 and −0.872, respectively. When independently determined data from 2013 were used to test the derived equations, the coefficients (R2) of correlation between the measured and estimated chlorophyll were 0.817 (ND705), 0.833 (mND705) and 0.776 (R550); the root mean square errors (RMSE) were 0.227 (ND705), 0.228 (mND705) and 0.254 (R550) (mgkg−1); and the mean relative errors (RE) were 8.7% (ND705), 8.1% (mND705) and 9.1% (R550). Furthermore, O3-induced changes in the three optical parameters were in accordance with the leaf chlorophyll responses in wheat. Our study suggested that the reflectance indices mND705, ND705 and R550, especially the former two spectral indices that contained information from several bands, could help to support the diagnosis and real-time monitoring of O3-induced damage in wheat. To estimate the wheat yield accurately using the selected spectral indices, the filling stage was found to be the best time for measuring canopy reflectance.
Why it matches plant phenotyping methods小麦のオゾン障害を反射スペクトルから診断する指標を検討し、独立データでクロロフィル推定式を検証しているため、植物フェノタイピング手法が中心である。
abstractThe aim was to find sensitive hyperspectral indictors for real-time detection of O3 effects.
Remote sensing (RS) approaches commonly applied to constrain estimates of gross primary production (GPP) employ greenness-based vegetation indices derived from surface reflectance data. Such approaches cannot capture dynamic changes of photosynthesis rates as caused by environmental stress. Further, applied vegetation indices are often affected by background reflectance or saturation effects. Sun. induced chlorophyll fluorescence (F) provides the most direct measure of photosynthesis and has been recently proposed as a new RS approach to improve estimates of GPP and tracing plant stress reactions. This work aims to provide further evidence on the complementary information content of F and its relation to changes in photosynthetic activity compared to traditional RS approaches. We use the airborne imaging spectrometer HyPlant to obtain several F products including red fluorescence (F687), far-red fluorescence (F760), F760 yield (F760yield) and the ration between F687 and F760 (Fratio). We calculate several vegetation indices indicative for vegetation greenness. We apply a recently proposed F-based semi-mechanistic approach to improve the forward modeling of GPP using F760 and compare this approach with a traditional one based on vegetation greenness and ground measurements of GPP derived from chamber measurements. In addition, we assess the sensitivity of F760yield and Fratio for environmental stress. Our results show an improved predictive capability of GPP when using F760 compared to greenness-based vegetation indices. F760yield and Fratio show a strong variability in time and between different crop types suffering from different levels of water shortage, indicating a strong sensitivity of F products for plant stress reactions. We conclude that the new RS approach of F provides complements to the set of commonly applies RS: The use of F760 improves constraining estimates of GPP while the ratio of red and far-red F shows large potential for tracking spatio-temporal plant adaptation in response to environmental stress conditions.
Why it matches plant phenotyping methods航空機搭載分光イメージャでクロロフィル蛍光を取得し、光合成活性・植物ストレス・GPP推定への有効性を比較評価しており、植物生理状態のセンシング手法が中心である。
abstractWe use the airborne imaging spectrometer HyPlant to obtain several F products including red fluorescence (F687), far-red fluorescence (F760), F760 yield (F760yield) and the ration between F687 and F760 (Fratio).
This study aims at developing a robust and generic methodology, based on the use of high resolution remote sensing data to provide accurate estimates of maize biomass and yield over large areas (i.e. at regional scale). We propose here a strategy of calibration and spatialization independent as much as possible of in situ measurements and reliable over large areas and under various climatic conditions. For this purpose, we combine the Simple Algorithm For Yield estimates (SAFY) model with high spatial and temporal resolution remote sensing data from several sensors: Formosat-2, SPOT4-Take5, Landsat-8 and Deimos-1. SPOT4-Take5 experiment conducted in 2013 was designed to simulate the temporal sampling of ESA's Sentinel-2 mission. This study led to a new version of the SAFY model that takes into account the seasonal variation of specific leaf area (SLA) and effective light use efficiency (ELUE). The study takes place in a temperate agrosystem located in the south west of France. The SAFY outputs were validated with local measurements of biomass and yield estimates at both local and regional scales using a multiannual dataset. Good results were obtained for both local biomass (R=0.98; RRMSE=14%) and yield (R=0.81; RRMSE=8.9%), and for yield estimations at regional scale (R=0.96; RRMSE=4.6%). Results also showed that the use of a double logistic function to interpolate Green Area Index (GAI) time series permits to improve the estimations of biomass and yield when remote sensing data are missing. This work demonstrates the potential of high resolution remote sensing data to calibrate a simple crop model without resorting to in situ data and thus foreshadows the future applications using Sentinel-2 data.
Why it matches plant phenotyping methods高解像度リモートセンシングとSAFYモデルを組み合わせ、トウモロコシのバイオマス・収量を広域推定する方法を開発・検証しており、植物形質の取得・推定が研究の中心である。
abstractThis study aims at developing a robust and generic methodology, based on the use of high resolution remote sensing data to provide accurate estimates of maize biomass and yield over large areas
Remote sensing data allow large scale observation of forested ecosystems. Forest assessment benefits from information about individual trees. Multibaseline SAR interferometry (InSAR) is able to generate dense point clouds of forest canopies, similar to airborne laser scanning (ALS). This type of point cloud was generated using data from the Ka-band MEMPHIS system, acquired over a mainly coniferous forest near Vordemwald in the Swiss Midlands. This point cloud was segmented using an advanced clustering technique to detect individual trees and derive their positions, heights, and crown diameters. To evaluate the InSAR point cloud properties and limitations, it was compared to products derived from ALS and stereo-photogrammetry. All point clouds showed similar geolocation accuracies with 0.2–0.3m relative shifts. Both InSAR and photogrammetry techniques yielded points predominantly located in the upper levels of the forest vegetation, while ALS provided points from the top of the canopy down to the understory and forest floor. The canopy height models agreed very well with each other, with R² values between 0.84 and 0.89. The detected trees and their estimated physical and structural parameters were validated by comparing them to reference forestry data. A detection rate of ~90% was achieved for larger trees, corresponding to half of the reference trees. The smaller trees were detected with a success rate of ~50%. The tree height was slightly underestimated, with a R² value of 0.63. The estimated crown diameter agreed on an average sense, however with a relatively low R² value of 0.19. Very high success rates (>90%) were obtained when matching the trees detected from the InSAR-data with those detected from the ALS- and photogrammetry-data. There, InSAR tree heights were in the mean 1–1.5m lower, with high R² values ranging between 0.8 and 0.9. Our results demonstrate the use of millimeter wave SAR interferometry data as an alternative to ALS- and photogrammetry-based data for forest monitoring.
Why it matches plant phenotyping methods航空機SAR干渉計による点群から個体樹木を検出し、樹高・樹冠径などの植物形態形質を抽出する手法を開発・比較検証しており、森林モニタリングへの応用が中心である。
abstractThis point cloud was segmented using an advanced clustering technique to detect individual trees and derive their positions, heights, and crown diameters.
The remote monitoring of plant canopies is critically needed for understanding of terrestrial ecosystem mechanics and biodiversity as well as capturing the short- to long-term responses of vegetation to disturbance and climate change. A variety of orbital, sub-orbital, and field instruments have been used to retrieve optical spectral signals and to study different vegetation properties such as plant biochemistry, nutrient cycling, physiology, water status, and stress. Radiative transfer models (RTMs) provide a mechanistic link between vegetation properties and observed spectral features, and RTM spectral inversion is a useful framework for estimating these properties from spectral data. However, existing approaches to RTM spectral inversion are typically limited by the inability to characterize uncertainty in parameter estimates. Here, we introduce a Bayesian algorithm for the spectral inversion of the PROSPECT 5 leaf RTM that is distinct from past approaches in two important ways: First, the algorithm only uses reflectance and does not require transmittance observations, which have been plagued by a variety of measurement and equipment challenges. Second, the output is not a point estimate for each parameter but rather the joint probability distribution that includes estimates of parameter uncertainties and covariance structure. We validated our inversion approach using a database of leaf spectra together with measurements of equivalent water thickness (EWT) and leaf dry mass per unit area (LMA). The parameters estimated by our inversion were able to accurately reproduce the observed reflectance (RMSEVIS=0.0063, RMSENIR-SWIR=0.0098) and transmittance (RMSEVIS=0.0404, RMSENIR-SWIR=0.0551) for both broadleaved and conifer species. Inversion estimates of EWT and LMA for broadleaved species agreed well with direct measurements (CVEWT=18.8%, CVLMA=24.5%), while estimates for conifer species were less accurate (CVEWT=53.2%, CVLMA=63.3%). To examine the influence of spectral resolution on parameter uncertainty, we simulated leaf reflectance as observed by ten common remote sensing platforms with varying spectral configurations and performed a Bayesian inversion on the resulting spectra. We found that full-range hyperspectral platforms were able to retrieve all parameters accurately and precisely, while the parameter estimates of multispectral platforms were much less precise and prone to bias at high and low values. We also observed that variations in the width and location of spectral bands influenced the shape of the covariance structure of parameter estimates. Our Bayesian spectral inversion provides a powerful and versatile framework for future RTM development and single- and multi-instrumental remote sensing of vegetation.
Why it matches plant phenotyping methods葉の反射スペクトルからEWTやLMAなどの植物形質を推定するBayesian RTM逆解析法を開発し、実測値および異なるセンサー構成で検証しており、形質取得手法が研究の中心である。
abstractHere, we introduce a Bayesian algorithm for the spectral inversion of the PROSPECT 5 leaf RTM
Covering 40% of the terrestrial surface, dryland ecosystems characteristically have distinct vegetation structures that are strongly linked to their function. Existing survey approaches cannot provide sufficiently fine-resolution data at landscape-level extents to quantify this structure appropriately. Using a small, unpiloted aerial system (UAS) to acquire aerial photographs and processing theses using structure-from-motion (SfM) photogrammetry, three-dimensional models were produced describing the vegetation structure of semi-arid ecosystems at seven sites across a grass–to shrub transition zone. This approach yielded ultra-fine (<1cm2) spatial resolution canopy height models over landscape-levels (10ha), which resolved individual grass tussocks just a few cm3 in volume. Canopy height cumulative distributions for each site illustrated ecologically-significant differences in ecosystem structure. Strong coefficients of determination (r2 from 0.64 to 0.95) supported prediction of above-ground biomass from canopy volume. Canopy volumes, above-ground biomass and carbon stocks were shown to be sensitive to spatial changes in the structure of vegetation communities. The grain of data produced and sensitivity of this approach is invaluable to capture even subtle differences in the structure (and therefore function) of these heterogeneous ecosystems subject to rapid environmental change. The results demonstrate how products from inexpensive UAS coupled with SfM photogrammetry can produce ultra-fine grain biophysical data products, which have the potential to revolutionise scientific understanding of ecology in ecosystems with either spatially or temporally discontinuous canopy cover.
Why it matches plant phenotyping methodsUAS画像とSfMフォトグラメトリを用いて植生の3次元構造、樹冠高、体積、バイオマスを定量化する手法が研究の中心であり、植物構造形質の取得・推定を技術的に評価している。
abstractUsing a small, unpiloted aerial system (UAS) to acquire aerial photographs and processing theses using structure-from-motion (SfM) photogrammetry, three-dimensional models were produced describing the vegetation structure of semi-arid ecosystems at seven sites across a grass–to shrub transition zone.
Land surface phenology (LSP) and vegetation growth of the circumpolar north are changing in response to more pronounced warming in the region. We here introduce the first phenology index (PI) based vegetation dynamics product, comprising start (SOS), end (EOS), length of growing season (LOS), and growing season integrated annual normalized difference vegetation index (NDVI), specifically designed for the entire circumpolar north (>45°N) using SPOT VGT data starting from 1999. PI combines the merits of NDVI and normalized difference infrared index (NDII) by taking the difference of squared greenness (from NDVI) and wetness (from NDII) to remove the soil and snow cover dynamics from key vegetation LSP cycles. The results show that the circumpolar vegetation dynamics and their spatial distributions are realistically detected. Further validation based on North American and European deciduous broadleaf, evergreen needleleaf and mixed forests, and wetland flux tower sites shows good agreements between the LSP dates from the circumpolar vegetation dynamics and ground phenology estimates from CO2 flux measurements. The validation also proves that the circumpolar vegetation dynamics product is an improvement over the operational global MODIS Combined Land Cover Dynamics (MCD12Q2) product for the circumpolar region. The results are further compared with the interannual variability of sea ice extent and leading teleconnection patterns in the region. The circumpolar averaged results show that, the growing season integrated annual NDVI is significantly increasing (0.68%year−1, p=0.006) and well correlated with the growing season sea ice extent trend (p=0.007). The circumpolar vegetation dynamics is more related to Polar/Eurasia pattern (i.e., indicator of circumpolar vortex) than to Scandinavian Pattern (SCA) and North Atlantic Oscillation (NAO). In view of the considerable scientific and policy importance of the circumpolar region, particularly the arctic ecosystems, the presented circumpolar vegetation dynamics product will greatly contribute to study changes in plant growth, phenology, photosynthetic capacity, and associated feedbacks under climate change.
Why it matches plant phenotyping methods衛星データから植物の生育季節（SOS、EOS、LOS）と生育指標を抽出する新しい植生動態プロダクトを開発し、地上観測および既存プロダクトと検証・比較しており、植物フェノタイピング手法が中心である。
abstractWe here introduce the first phenology index (PI) based vegetation dynamics product, comprising start (SOS), end (EOS), length of growing season (LOS), and growing season integrated annual normalized difference vegetation index (NDVI), specifically designed for the entire circumpolar north (>45°N) using SPOT VGT data starting from 1999.
The April 2010 Deepwater Horizon (DWH) oil spill was the largest coastal spill in U.S. history. Monitoring subsequent change in marsh plant community distributions is critical to assess ecosystem impacts and to establish future coastal management priorities. Strategically deployed airborne imaging spectrometers, like the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), offer the spectral and spatial resolution needed to differentiate plant species. However, obtaining satisfactory and consistent classification accuracies over time is a major challenge, particularly in dynamic intertidal landscapes.Here, we develop and evaluate an image classification system for a time series of AVIRIS data for mapping dominant species in a heavily oiled salt marsh ecosystem. Using field-referenced image endmembers and canonical discriminant analysis (CDA), we classified 21 AVIRIS images acquired during the fall of 2010, 2011 and 2012. Classification results were evaluated using ground surveys that were conducted contemporaneously to AVIRIS collection dates. We analyzed changes in dominant species cover from 2010 to 2012 for oiled and non-oiled shorelines.CDA discriminated dominant species with a high level of accuracy (overall accuracy=82%, kappa=0.78) and consistency over three imaging dates (overall2010=82%, overall2011=82%, overall2012=88%). Marshes dominated by Spartina alterniflora were the most spatially abundant in shoreline zones (≤28m from shore) for all three dates (2010=79%, 2011=61%, 2012=63%), followed by Juncus roemerianus (2010=11%, 2011=19%, 2012=17%) and Distichlis spicata (2010=4%, 2011=10%, 2012=7%).Marshes that were heavily contaminated with oil exhibited variable responses from 2010 to 2012. Marsh vegetation classes converted to a subtidal, open water class along oiled and non-oiled shorelines that were similarly situated in the landscape. However, marsh loss along oil-contaminated shorelines doubled that of non-oiled shorelines. Only S. alterniflora dominated marshes were extensively degraded, losing 15% (354,604m2) cover in oiled shoreline zones, suggesting that S. alterniflora marshes may be more vulnerable to shoreline erosion following hydrocarbon stress, due to their landscape position.
Why it matches plant phenotyping methods航空画像分光と判別分析による植物種分布・被覆の抽出システムを開発・評価し、地上調査で精度検証しているため、植物状態の測定法が中心である。
abstractwe develop and evaluate an image classification system for a time series of AVIRIS data for mapping dominant species in a heavily oiled salt marsh ecosystem.
A variety of evidence suggests that the boreal forests of Canada are responding to climate change. Specifically, several studies have inferred that widespread browning trends detected in time series of the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) reflect the response of boreal forests to longer growing seasons, increased summer drought stress, and higher frequency of fires. Data from the Thematic Mapper (TM5) and Enhanced Thematic Mapper Plus (ETM+) sensors onboard Landsat 5 and 7, respectively, span essentially the same time period as the AVHRR record, but provide data with substantially higher radiometric and spatial fidelity, and by extension, a much improved basis for evaluating decadal-scale trends in spectral vegetation indices such as the NDVI. However, detection of trends, which are often subtle, requires careful attention to ensure that artifacts associated with the quality and stability of inter- and intra-sensor calibration do not lead to spurious conclusions in results from time series analyses. In this paper, we use time series of TM5 and ETM+ images for fifteen sites distributed across the Canadian boreal forest zone to explore if and how sensor geometry and inter- and intra-sensor calibration affect trends in spectral vegetation indices derived from multi-decadal Landsat time series. To do this, we created annual cloud-free composites for each Landsat spectral band based on peak summer NDVI at each site from 1984 to 2011 using all available TM5 and ETM+ data. To distinguish trends arising from long term climate change from those related to disturbance, we isolated areas within each site that were undisturbed during the Landsat record, and used these locations to analyze sources of variance in time series of red reflectance, near-infrared (NIR) reflectance, the NDVI, and the Enhanced Vegetation Index (EVI). Our results highlight the challenges involved in distinguishing trends in surface properties from data artifacts caused by undetected atmospheric effects, changes in sensor view angles, and subtle radiometric differences between the TM5 and ETM+ sensors. In particular, differences in sensor view geometry across adjacent overlapping Landsat scenes cause vegetated pixels in the eastern portion of Landsat scenes to have higher reflectances in the red and NIR bands (by 5 and 6 percent, respectively) than pixels in the western portion of scenes. While this effect does not significantly change NDVI values, it does affect EVI values. We also found modest, but potentially significant, differences between the red band reflectance of each sensor, with TM5 data having 14 percent higher red reflectance on average for vegetated pixels, which can introduce spurious trends in time series that combine TM5 and ETM+ data. More generally, the results from this work demonstrate that while the 30+ year Landsat archive provides unprecedented opportunities for studying changes to the Earth's terrestrial biosphere over the last three decades, care must be taken when inferring trends in these data without considering how sources of variance unrelated to surface processes affect the integrity of Landsat time series.
Why it matches plant phenotyping methodsLandsatセンサーの幾何・校正バイアスが植生指数（NDVI・EVI）による森林植生状態の推定に与える影響を検証しており、植物状態のリモートセンシング計測法の技術的評価が中心である。
abstractOur results highlight the challenges involved in distinguishing trends in surface properties from data artifacts caused by undetected atmospheric effects, changes in sensor view angles, and subtle radiometric differences between the TM5 and ETM+ sensors.
Plant phenology plays a significant role in regulating carbon sequestration period of terrestrial ecosystems. Remote sensing of land surface phenology (LSP), i.e., the start and the end of the growing season (SOS and EOS, respectively) in evergreen needleleaf forests is particularly challenging due to their limited seasonal variability in canopy greenness. Using 107 site-years of CO2 flux data at 14 evergreen needleleaf forest sites in North America, we developed a new model to estimate SOS and EOS based entirely on the Moderate Resolution Imaging Spectroradiometer (MODIS) data. We found that the commonly used vegetation indices (VI), including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), were not able to detect SOS and EOS in these forests. The MODIS land surface temperature (LST) showed better performance in the estimation of SOS than did a single VI. Interestingly, the variability of LST (i.e., the coefficient of variation, CV_LST) was more useful than LST itself in detecting changes in forest LSP. Therefore, a new model using the product of VI and CV_LST was developed and it significantly improved the representation of LSP with mean errors of 11.7 and 5.6days for SOS and EOS, respectively. Further validation at five sites in the Long Term Ecological Research network (LTER) using camera data also indicated the applicability of the new approach. These results suggest that temperature variability plays a previously overlooked role in phenological modeling, and a combination of canopy greenness and temperature could be a useful way to enhance the estimation of evergreen needleleaf forest phenology of future ecosystem models.
Why it matches plant phenotyping methodsMODIS反射率・地表面温度から森林の生育季節開始・終了を推定する新モデルを開発し、複数サイトおよびカメラデータで検証しており、植物フェノロジーの取得手法が中心である。
abstractwe developed a new model to estimate SOS and EOS based entirely on the Moderate Resolution Imaging Spectroradiometer (MODIS) data.
Real-time, nondestructive monitoring of crop nitrogen (N) status is important for precise N management in winter wheat production. Nadir viewing passive multispectral sensors have limited utility for measuring the N status of winter wheat in middle and bottom layers, and multi-angular remote sensors may instead improve detection of whole canopy physiological and biochemical parameters. Our objective was to improve the predictive accuracy and angular stability of leaf nitrogen concentration (LNC) measurement by constructing a novel Angular Insensitivity Vegetation Index (AIVI). We quantified the relationship between LNC and ground-based multi-angular hyperspectral reflectance in winter wheat (Triticum aestivum L.) across different growth stages, plant types, N rates, planting density, ecological sites and years. The optimum vegetation indices (VIs) obtained from 17 traditional indices reported in the literature were tested for their stability in estimating LNC at 13 view zenith angles (VZAs) in the solar principal plane (SPP). Overall the back-scatter direction gave improved index performance, relative to the nadir and forward-scattering direction. Red-edge VIs (e.g., mND705, GND [750,550], NDRE, RI-1dB) were highly correlated with LNC. However, the relationships strongly depended on experimental conditions, and these VIs tended to saturate at the highest LNC (4.5%). To further overcome the influence of different experimental conditions and VZAs on VIs, we developed a novel index, Angular Insensitivity Vegetation Index (AIVI), based on red-edge, blue and green bands. Our new model showed the highest association with LNC (R2=0.73–0.87) compared to traditional VIs. Investigating AIVI predictive accuracy in measuring LNC across view zenith angles (VZAs) revealed that performance was the highest at −20° and was relatively homogenous between −10° and −40°. This provided a united, predictive model across this wide-angle range, which enhances the possibility of N monitoring by using portable monitors. Testing of the models with independent data gave R2 of 0.84 at −20°, and 0.83 across the range of −10° to −40°, respectively. These results suggest that the novel AIVI is more effective for monitoring LNC than previously reported VIs for predicting accuracy, monitoring model stability and view angle independency. More generally, our model indicates the importance of accounting for angular effects when analyzing VIs under different experimental conditions.
Why it matches plant phenotyping methods冬小麦の葉窒素濃度という植物形質を、多角 hyperspectral センシングと新規植生指数で推定する手法を開発・独立データで検証しており、フェノタイピング手法が中心である。
abstractOur objective was to improve the predictive accuracy and angular stability of leaf nitrogen concentration (LNC) measurement by constructing a novel Angular Insensitivity Vegetation Index (AIVI).
Shifts in ecosystem phenology play an important role in the definition of inter-annual variability of net ecosystem carbon uptake. A good estimate at the global scale of ecosystem phenology, mainly that of photosynthesis or gross primary productivity (GPP), may be provided by vegetation indices derived from MODIS satellite image data.However, the relationship between the start date of a growing (or greening) season (SGS) when derived from different vegetation indices (VI's), and the starting day of carbon uptake is not well elucidated. Additionally, the validation of existing phenology data with in-situ measurements is largely missing. We have investigated the possibility to use different VI's to predict the starting day of the growing season for 28 FLUXNET sites as well as MODIS data. This analysis included main plant functional types (PFT's).Of all VI's taken into account in this paper, the NDVI (Normalized Difference Vegetation Index) shows the highest correlation coefficient for the relationship between the starting day of the growing season as observed with MODIS and in-situ observations. However, MODIS observations elicit a 20–21days earlier SGS date compared to in-situ observations. The prediction for the NEE start of the growing season diverges when using different VI's, and seems to depend on the amplitude for carbon and VI and on PFT. The optimal VI for estimation of a SGS date was PFT-specific — for example the WRDVI for cropland, but the MODIS NDVI performed best when applied as an estimator for Net Ecosystem Exchange and when considering all PFT's pooled.
Why it matches plant phenotyping methodsMODIS植生指数を用いた生育期開始日の推定を、FLUXNET現地観測と比較・検証しており、植物群落のフェノロジー測定法の評価が中心である。
abstractWe have investigated the possibility to use different VI's to predict the starting day of the growing season for 28 FLUXNET sites as well as MODIS data.
Most methods for assessing the loading of particles on plant leaf surfaces involve a cumbersome manual step, and hence are slow to employ. Furthermore, they yield results that are summative, representing total number of particles or total volume or weight of particles in standardized size fractions. Here, we present a novel approach that cannot only accurately quantify the number of particles, but also their size and shape. In addition, the method we present replaces the manual measurement of the particles on leaf surfaces with an automated step. We applied the well-developed object-based image analysis technique to scanning electronic microscope (SEM) micrographs of tree leaf, and tested this approach for replicate SEM micrographs of a common urban tree species. We demonstrated that: 1) this new method automatically identifies the number of particles, as well as their size and shape, in contrast to the commonly used microscopic inspection approach that can only measure the number of particles; and 2) this method achieved similar overall accuracy to that of microscopic inspection (92.17% versus 95.53%), but microscopic inspection takes fourteen times longer. It is expected that the difference in efficiency would be more significant with the increase of micrograph numbers, because micrographs can be batch processed with object-based classification. With the greatly increased efficiency and the ability of the proposed method to capture new variables about particle shape and complexity, this method can facilitate comparative research on the adsorption capacities of different plant species, and potentially identifying the source apportionment of particulate matters based on their morphological characteristics, which may provide insights for species selection for pollutant reduction.
Why it matches plant phenotyping methods葉面上の粒子数・サイズ・形状という植物表面状態を、SEM画像と物体ベース画像解析で自動抽出する手法の開発・精度比較が研究の中心であり、植物フェノタイピング手法として適格です。
abstractHere, we present a novel approach that cannot only accurately quantify the number of particles, but also their size and shape.