Seasonal mapping of irrigated winter wheat traits in Argentina with a hybrid retrieval workflow using sentinel-2 imagery

• Autores: Caballero, Gabriel; Pezzola, Nestor Alejandro; Winschel, Cristina Ines; Casella, Alejandra An; Sanchez Angonova, Paolo Andres; Rivera Caicedo, Juan Pablo; Berger, Katja; Verrelst, Jochem; Delegido, Jesús
• Año de publicación: 2022
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Remote Sensing 14 (18) : 4531. (September 2022)
• Materia: Leaf Area Index; Kriging; Imagery; Índice de Superficie Foliar; Krigeage; Imágenes; Vegetation Water and Chlorophyll Content; Hybrid Retrieval Workflow; Dimencionality Reduction; Active Learning; Contenido de Agua y Clorofila de la Vegetación; Flujo de Trabajo de Recuperación Híbrido; Reducción de Dimensionalidad; Aprendizaje Activo
• Institución: Instituto Nacional de Tecnología Agropecuaria

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