Dong Fan, Tianjie Zhao, Xiaoguang Jiang, Almudena García-García, Toni Schmidt, Luis Samaniego, Sabine Attinger, Hua Wu, Yazhen Jiang, Jiancheng Shi, Lei Fan, Bo-Hui Tang, Wolfgang Wagner, Wouter Dorigo, Alexander Gruber, Francesco Mattia, Anna Balenzano, Luca Brocca, Thomas Jagdhuber, Jean-Pierre Wigneron, Carsten Montzka, Jian Peng
- High spatial resolution of satellite-based soil moisture (SM) data are essential for hydrological, meteorological, ecological, and agricultural studies. Especially, for watershed hydrological simulation and crop water stress analysis, 1-km resolution SM data have attracted considerable attention. In this study, a dual-polarization algorithm (DPA) for SM estimation is proposed to produce a global-scale, 1-km resolution SM dataset (S1-DPA) using the Sentinel-1 synthetic aperture radar (SAR) data. Specifically, a forward model was constructed to simulate the backscatter observed by the Sentinel-1 dual-polarization SAR, and SM retrieval was achieved by minimizing the simulation error for different soil and vegetation states. The produced S1-DPA data products cover the global land surface for the period 2016–2022 and include both ascending and descending data with an observation frequency of 3–6 days for Europe and 6–12 days for the other regions. The validation results show that the S1-DPAHigh spatial resolution of satellite-based soil moisture (SM) data are essential for hydrological, meteorological, ecological, and agricultural studies. Especially, for watershed hydrological simulation and crop water stress analysis, 1-km resolution SM data have attracted considerable attention. In this study, a dual-polarization algorithm (DPA) for SM estimation is proposed to produce a global-scale, 1-km resolution SM dataset (S1-DPA) using the Sentinel-1 synthetic aperture radar (SAR) data. Specifically, a forward model was constructed to simulate the backscatter observed by the Sentinel-1 dual-polarization SAR, and SM retrieval was achieved by minimizing the simulation error for different soil and vegetation states. The produced S1-DPA data products cover the global land surface for the period 2016–2022 and include both ascending and descending data with an observation frequency of 3–6 days for Europe and 6–12 days for the other regions. The validation results show that the S1-DPA reproduces the spatio-temporal variation characteristics of the ground-observed SM, with an unbiased root mean squared difference (ubRMSD) of 0.077 m3/m3. The generated 1-km SM product will facilitate the application of high-resolution SM data in the field of hydrology, meteorology and ecology.…
