Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery.

Abstract

Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop's phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learning regression algorithm, i.e., Gaussian processes regression, and an active learning technique to estimate LAI, CCC and VWC of irrigated winter wheat. The established hybrid models of the three traits were validated against in-situ data of a wheat campaign in the Bonaerense valley, South of the Buenos Aires Province, Argentina, in the year 2020. We obtained good to highly accurate validation results with LAI: R 2 = 0.92, RMSE = 0.43 m 2 m -2 , CCC: R 2 = 0.80, RMSE = 0.27 g m -2 and VWC: R 2 = 0.75, RMSE = 416 g m -2 . The retrieval models were also applied to a series of S2 images, producing time series along the seasonal cycle, which reflected the effects of fertilizer and irrigation on crop growth. The associated uncertainties along with the obtained maps underlined the robustness of the hybrid retrieval workflow. We conclude that processing S2 imagery with optimised hybrid models allows accurate space-based crop traits mapping over large irrigated areas and thus can support agricultural management decisions.

Plant phenotyping relevance

Sentinel-2画像からLAI、CCC、VWCを推定する検索ワークフローを開発し、実測値で検証しており、植物形質取得法が研究の中心である。

Code and data availability

The article describes a hybrid PROSAIL-PRO/GPR workflow built in ARTMO, with in-situ wheat trait data from the BVCR 2020 campaign and Sentinel-2 imagery. No author-deposited public dataset, field campaign data, code, or trained models are reported; the only URLs (scihub.copernicus.eu, SNAP, artmotoolbox.com) are third-

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