Unverified paper record
Insights from field phenotyping improve satellite remote sensing based in-season estimation of winter wheat growth and phenology
Remote Sensing of Environment · 1 Dec 2023 · 10.1016/j.rse.2023.113860
Abstract
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.
Plant phenotyping relevance
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.
abstractValidation against a large data set of on-farm trials in Switzerland collected in 2019 and 2022 revealed high accuracy of our approach
abstractSentinel-2 derived GLAI and CCC explained between 77 to 84% and between 79 to 84% of the variability in in-situ measurements, respectively.
Code and data availability
公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。
No evidence-backed public reproduction asset is currently recorded.
This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.