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 Retrieval of High-Resolution Vegetation Optical Depth from Sentinel-1 Data over a Grassland Region in the Heihe River Basin Zhilan Zhou 2662258-0 Lei Fan baaf7a09d1f0c9324e7dac4b912ab6e5 swu.edu.cn 801650 1376655-801650 https://orcid.org/0000-0002-1834-5088 Gabrielle de Lannoy 772280 859330-772280 https://orcid.org/0000-0002-6743-7122 Xiangzhuo Liu 1196439 2035749-1196439 https://orcid.org/0000-0002-1690-7083 Jian Peng 1196440 880703-1196440 https://orcid.org/0000-0002-4071-0512 Xiaojing Bai 2259348-0 Frédéric Frappart 21a5f52595ede3c6b111a70449a85bee inrae.fr ffrappart 1338014 54587-1338014 https://orcid.org/0000-0002-4661-8274 https://www.idref.fr/10905024X Nicolas Baghdadi 434ca16932a07d698c6a47db37155932 inrae.fr nicolas-baghdadi 736141 49716-736141 https://orcid.org/0000-0002-9461-4120 https://www.idref.fr/147699614 Zanpin Xing 2325982-0 Xiaojun Li 48df4ec06c949dfd4db2c6a773a954f8 inra.fr 1078253 1408799-1078253 https://orcid.org/0000-0002-3831-4852 Mingguo Ma 1196442 1376830-1196442 https://orcid.org/0000-0002-3783-8363 Xin Li 1196443 236333-1196443 https://orcid.org/0000-0003-2999-9818 Tao Che 1196444 2290328-1196444 https://orcid.org/0000-0001-6848-7271 Liying Geng 2662259-0 Jean-Pierre Wigneron d843c1e53d5407018c94fe8c3e07e821 inrae.fr jean-pierre-wigneron 736098 105960-736098 https://orcid.org/0000-0001-5345-3618 https://scholar.google.fr/citations?user=Hfo-3FcAAAAJ&hl=fr https://www.idref.fr/090178858 Isabelle NAULT 257a01240d864fba4fd9d7a650162fd2 inrae.fr 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). 2022-11-29 14:59:13 2026-05-12 10:18:05 2022-11-29 15:16:34 2022-11 2022-11-29 inra_etatDocument_local_VE inra_publicVise_local_SC contributor Isabelle NAULT 257a01240d864fba4fd9d7a650162fd2 inrae.fr CCSD hal-03877849 https://hal.inrae.fr/hal-03877849 zhou:hal-03877849 <i>Remote Sensing</i>, 2022, 14 (21), pp.5468. <a target="_blank" href="https://dx.doi.org/10.3390/rs14215468">&#x27E8;10.3390/rs14215468&#x27E9;</a> Remote Sensing, 2022, 14 (21), pp.5468. &#x27E8;10.3390/rs14215468&#x27E9; CC BY 4.0 - Attribution CIRAD - Centre de coopération internationale en recherche agronomique pour le développement Sciences De l'Environnement AgroParisTech CNRS - Centre national de la recherche scientifique GIP Bretagne Environnement TETIS Archive ouverte en agrobiosciences Institut National de Recherche en Agriculture, Alimentation et Environnement INRAE Occitanie Montpellier UMR ISPA : Interactions Sol Plante Atmosphère TEST3-HALCNRS collection test Département MathNum Réseau "Systèmes Agricoles et Eau" Ecologie et biodiversité collection test 5 Institut écologie et environnement du CNRS Test MathNum International No Yes Retrieval of High-Resolution Vegetation Optical Depth from Sentinel-1 Data over a Grassland Region in the Heihe River Basin Zhilan Zhou 2662258-0 Lei Fan baaf7a09d1f0c9324e7dac4b912ab6e5 swu.edu.cn 801650 1376655-801650 https://orcid.org/0000-0002-1834-5088 Gabrielle de Lannoy 772280 859330-772280 https://orcid.org/0000-0002-6743-7122 Xiangzhuo Liu 1196439 2035749-1196439 https://orcid.org/0000-0002-1690-7083 Jian Peng 1196440 880703-1196440 https://orcid.org/0000-0002-4071-0512 Xiaojing Bai 2259348-0 Frédéric Frappart 21a5f52595ede3c6b111a70449a85bee inrae.fr ffrappart 1338014 54587-1338014 https://orcid.org/0000-0002-4661-8274 https://www.idref.fr/10905024X Nicolas Baghdadi 434ca16932a07d698c6a47db37155932 inrae.fr nicolas-baghdadi 736141 49716-736141 https://orcid.org/0000-0002-9461-4120 https://www.idref.fr/147699614 Zanpin Xing 2325982-0 Xiaojun Li 48df4ec06c949dfd4db2c6a773a954f8 inra.fr 1078253 1408799-1078253 https://orcid.org/0000-0002-3831-4852 Mingguo Ma 1196442 1376830-1196442 https://orcid.org/0000-0002-3783-8363 Xin Li 1196443 236333-1196443 https://orcid.org/0000-0003-2999-9818 Tao Che 1196444 2290328-1196444 https://orcid.org/0000-0001-6848-7271 Liying Geng 2662259-0 Jean-Pierre Wigneron d843c1e53d5407018c94fe8c3e07e821 inrae.fr jean-pierre-wigneron 736098 105960-736098 https://orcid.org/0000-0001-5345-3618 https://scholar.google.fr/citations?user=Hfo-3FcAAAAJ&hl=fr https://www.idref.fr/090178858 101254 2072-4292 Remote Sensing MDPI 14 21 5468 2022-11 10.3390/rs14215468 000883556600001 English C-band grassland Sentinel-1 vegetation optical depth (VOD) Environmental Sciences Journal articles Journal articles Journal articles

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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