Identification of Robust Hybrid Inversion Models on the Crop Fraction of Absorbed Photosynthetically Active Radiation Using PROSAIL Model Simulated and Field Multispectral Data

Abstract

The fraction of absorbed photosynthetically active radiation (FPAR), which represents the capability of vegetation-absorbed solar radiation to accumulate organic matter, is a crucial indicator of photosynthesis and vegetation growth status. Although a simplified semi-empirical FPAR estimation model was easily obtained using vegetation indices (VIs), the sensitivity and robustness of VIs and the optimal inversion method need to be further evaluated and developed for canola FPAR retrieval. The objective of this study was to identify the robust hybrid inversion model for estimating the winter canola FPAR. A field experiment with different sow dates and densities was conducted over two growing seasons to obtain canola FPARs. Moreover, 29 VIs, two machine learning algorithms and the PROSAIL model were incorporated to establish the FPAR inversion model. The results indicate that the OSAVI, WDRVI and mSR had better capability for revealing the variations of the FPAR. Three parameters of leaf area index (LAI), solar zenith angle (SZA) and average leaf inclination angle (ALA) accounted for over 95% of the total variance in the FPARs and OSAVI exhibited a greater resistance to changes in the leaf and canopy parameters of interest. The hybrid inversion model with an artificial neural network (ANN-VIs) performed the best for both datasets. The optimal hybrid inversion model of ANN-OSAVI achieved the highest performance for canola FPAR retrieval, with R2 and RMSE values of 0.65 and 0.051, respectively. Finally, the work highlights the usefulness of the radiation transfer model (RTM) in quantifying the crop canopy FPAR and demonstrates the potential of hybrid model methods for retrieving the canola FPAR at each growth stage.

Plant phenotyping relevance

キャノーラの植物キャノピー形質であるFPARを、マルチスペクトルデータ、PROSAIL、植生指数、機械学習により推定する手法を開発・比較しており、形質取得・推定法が研究の中心である。

Code and data availability

The supplied blocks describe field multispectral UAV imagery, FPAR measurements, PROSAIL simulations, and ANN/SVR modeling, but contain no public dataset deposit, code repository, or availability statement for the paper's phenotyping data or analysis code.

No evidence-backed public reproduction asset is currently recorded.