Retrieval of High-Resolution Vegetation Optical Depth from Sentinel-1 Data over a Grassland Region in the Heihe River Basin
2022
Zhou, Zhilan | Fan, Lei | de Lannoy, Gabrielle | Liu, Xiangzhuo | Peng, Jian | Bai, Xiaojing | Frappart, Frédéric | Baghdadi, Nicolas | Xing, Zanpin | Li, Xiaojun | Ma, Mingguo | Li, Xin | Che, Tao | Geng, Liying | Wigneron, Jean-Pierre | Southwest University [Chongqing] | Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven) | Interactions Sol Plante Atmosphère (UMR ISPA) ; Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Leipzig University | Helmholtz Zentrum für Umweltforschung = Helmholtz Centre for Environmental Research (UFZ) | Nanjing University (NJU) | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | University of Chinese Academy of Sciences [Beijing] (UCAS) | Chinese Academy of Sciences [Beijing] (CAS) | This work was supported by research grants from the National Natural Science Foundation of China (Grant No. 42171339), the Fundamental Research Funds for the Central Universities (SWU020016).
International audience
Show more [+] Less [-]English. Vegetation optical depth (VOD), as a microwave-based estimate of vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. However, current global operational VOD products have a coarse spatial resolution (~25 km), which limits their use for agriculture management and vegetation dynamics monitoring at regional scales (1–5 km). This study aims to retrieve high-resolution VOD from the C-band Sentinel-1 backscatter data over a grassland of the Heihe River Basin in northwestern China. The proposed approach used an analytical solution of a simplified Water Cloud Model (WCM), constrained by given soil moisture estimates, to invert VOD over grassland with 1 km spatial resolution during the 2018–2020 period. Our results showed that the VOD estimates exhibited large spatial variability and strong seasonal variations. Furthermore, the dynamics of VOD estimates agreed well with optical vegetation indices, i.e., the mean temporal correlations with normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) were 0.76, 0.75, and 0.75, respectively, suggesting that the VOD retrievals could precisely capture the dynamics of grassland.
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