Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery
| Autor: | Gabriel Caballero, Alejandro Pezzola, Cristina Winschel, Alejandra Casella, Paolo Sanchez Angonova, Juan Pablo Rivera-Caicedo, Katja Berger, Jochem Verrelst, Jesus Delegido |
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| Rok vydání: | 2022 |
| Předmět: |
Leaf Area Index
Vegetation Water and Chlorophyll Content Active Learning Contenido de Agua y Clorofila de la Vegetación Dimencionality Reduction Índice de Superficie Foliar Aprendizaje Activo Reducción de Dimensionalidad Kriging Imágenes Hybrid Retrieval Workflow Flujo de Trabajo de Recuperación Híbrido General Earth and Planetary Sciences Imagery leaf area index vegetation water and chlorophyll content Gaussian processes regression hybrid retrieval workflow dimensionality reduction active learning Krigeage |
| Zdroj: | Remote Sensing 14 (18) : 4531. (September 2022) INTA Digital (INTA) Instituto Nacional de Tecnología Agropecuaria instacron:INTA Remote Sensing; Volume 14; Issue 18; Pages: 4531 |
| ISSN: | 2072-4292 |
| Popis: | 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: R2 = 0.92, RMSE = 0.43 m2 m−2, CCC: R2 = 0.80, RMSE = 0.27 g m−2 and VWC: R2 = 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. Fil: Caballero, Gabriel. Technological University of Uruguay (UTEC). Agri-Environmental Engineering; Uruguay. University of Valencia. Image Processing Laboratory (IPL); España Fil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina Fil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina Fil: Casella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Sanchez Angonova, Paolo Andres. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina Fil: Rivera Caicedo, Juan Pablo. CONACYT-UAN. Secretary of Research and Graduate Studies; México Fil: Berger, Katja. University of Valencia. Image Processing Laboratory (IPL); España. Mantle Labs GmbH; Austria Fil: Verrelst, Jochem. University of Valencia. Image Processing Laboratory (IPL); España Fil: Delegido, Jesús. Universidad de Valencia. Image Processing Laboratory (IPL); España |
| Databáze: | OpenAIRE |
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