Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles

• Autores: Caballero, Gabriel; Pezzola, Nestor Alejandro; Winschel, Cristina Ines; Casella, Alejandra An; Sanchez Angonova, Paolo Andres; Orden, Luciano; Berger, Katja; Verrelst, Jochem; Delegido, Jesús
• Año de publicación: 2022
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Remote Sensing 14 (22) : 5867. (November 2022)
• Materia: Índice de Superficie Foliar; Trigo; Invierno; Imágenes por Satélites; Riego; Leaf Area Index; Wheat; Winter; Satellite Imagery; Irrigation; Sentinel-1
• Institución: Instituto Nacional de Tecnología Agropecuaria

Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition’s geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with R2CV = 0.67 and RMSECV = 0.88 m2 m−2. The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloud-prone agri-environments.
EEA Hilario Ascasubi
Fil: Caballero, Gabriel. Technological University of Uruguay (UTEC). Agri-Environmental Engineering; Uruguay
Fil: Caballero, Gabriel. 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: Orden, Luciano. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Orden, Luciano. Universidad Miguel Hernández. Centro de Investigación e Innovación Agroalimentaria y Agroambiental. GIAAMA Reseach Group; España
Fil: Berger, Katja. University of Valencia. Image Processing Laboratory (IPL); España
Fil: Berger, Katja. 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