Publications

Fan, D; Zhao, TJ; Jiang, XG; Garcia-Garcia, A; Schmidt, T; Samaniego, L; Attinger, S; Wu, H; Jiang, YZ; Shi, JC; Fan, L; Tang, BH; Wagner, W; Dorigo, W; Gruber, A; Mattia, F; Balenzano, A; Brocca, L; Jagdhuber, T; Wigneron, JP; Montzka, C; Peng, J (2025). A Sentinel-1 SAR-based global 1-km resolution soil moisture data product: Algorithm and preliminary assessment. REMOTE SENSING OF ENVIRONMENT, 318, 114579.

Abstract
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-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.

DOI:
10.1016/j.rse.2024.114579

ISSN:
1879-0704