Insights from field phenotyping improve satellite remote sensing based in-season estimation of winter wheat growth and phenology

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。

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