Comprehensive Evaluation of the GF-3 Series SAR Satellites for Soil Moisture and Surface Roughness Retrieval over Bare Soils

Accurate quantification of soil moisture (mv) is of great scientific significance for regional hydrological modeling, meteorological forecasting, and drought and flood disaster monitoring. Although C-band SAR aboard the GF-3 satellite constellation supports large-scale retrieval, existing studies are mostly confined to local validation under simple surface conditions. Its retrieval performance across varied surface roughness (s), mv, soil texture, and topography, as well as the synergistic retrieval ability of the satellite constellation, has not been fully investigated. Therefore, this study systematically evaluated four mv retrieval strategies using quality-controlled satellite-ground synchronous observation data from 11 arid-to-humid experimental areas (378 plots) in China: Oh94 model inversion (Strategy I), calibrated Oh94 model inversion (Strategy II), calibrated Oh94 model inversion with prior constraints on mv and s (Strategy III), and random forest inversion (Strategy IV). Subsequently, the measured satellite backscattering coefficients (σobs0) were compared with model simulations (σsim0), yielding initial biases of 2.08 dB, 0.78 dB, and −0.29 dB for VV, HH, and HV polarizations, respectively, and these biases were significantly reduced to −0.01 dB, 0.00 dB, and −0.06 dB after systematic deviation correction (SDC). Overall, the root-mean-square errors (RMSE) of mv retrieval for Strategies I–IV were 0.092, 0.078, 0.058, and 0.046 cm3·cm−3, respectively, while those for s retrieval were 0.620, 0.578, 0.610, and 0.403 cm. Strategy IV achieved the highest mv retrieval accuracy owing to the robust nonlinear predictive capacity of machine learning. Nevertheless, Strategy III exhibited superior transferability in spatially independent validation, with an RMSE of 0.054 cm3·cm−3, outperforming Strategy IV (0.065 cm3·cm−3). This demonstrates that Strategy III possesses a stronger generalization ability than purely data-driven models under domain shifts. By incorporating prior constraints, Strategy III effectively mitigated radiometric inconsistencies within the satellite constellation, and mv retrieval biases among GF-3, GF-3B, and GF-3C converged stably within 0.021 cm3·cm−3, with RMSE ranging from 0.046 to 0.079 cm3·cm−3. This study validates the feasibility of synergistic mv retrieval over bare surfaces using the GF-3 SAR constellation, providing critical technical support for large-area operational mapping.