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Timely access to soil moisture distribution is critical for agricultural production. As an in-orbit L-band synthetic aperture radar (SAR), SAOCOM offers high penetration and full polarization, making it suitable for agricultural soil moisture estimation. In this study, based on the single-temporal coupled water cloud model (WCM) and Oh model, we first modified the WCM (MWCM) to incorporate bare soil effects on backscattering using SAR data, enhancing the scattering representation during crop growth. Additionally, the Oh model was revised to enable retrieval of both the surface layer (0–5 cm) and underlying layer (5–10 cm) soil moisture. SAOCOM data from 19 June 2022, and 23 June 2023 in Bei’an City, China, along with Sentinel-2 imagery from the same dates, were used to validate the coupled MWCM-Oh model individually. The enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and leaf area index (LAI), together with the radar vegetation index (RVI) served as vegetation descriptions. Results showed that surface soil moisture estimates were more accurate than those for the underlying layer. LAI performed best for surface moisture (RMSE = 0.045), closely followed by RVI (RMSE = 0.053). For underlying layer soil moisture, RVI provided the most accurate retrieval (RMSE = 0.038), while LAI, EVI, and NDVI tended to overestimate. Overall, LAI and RVI effectively capture surface soil moisture, and RVI is particularly suitable for underlying layers, enabling more comprehensive monitoring.
Dong et al. (Thu,) studied this question.