An improved method for estimating soil moisture over cropland using SAR and optical data

The paper aims to construct simple soil moisture(SM) retrieval model using Sentinel-1 synthetic aperture radar (SAR) data. The water cloud model (WCM) removed the contribution of vegetation to the radar backscattering coefficient, and the backscattering coefficient of soil was estimated. Based on the established SM retrieval model without soil roughness parameters, the SM in farmland and forest land was retrieved using radar VV-VH dual-polarization data. We considered the interference of uneven surfaces on the radar signal, added the radar local incidence angle parameter to improve the model, and constructed a semi-empirical SM retrieval model. The accuracy of the results showed Root Mean Square Error (RMSE) of 0.04 and the Pearson correlation coefficient (r) of 0.80. The SM retrieval model for removing soil roughness parameters can estimate soil moisture with reasonable accuracy. The influence of topographic factors (elevation, slope and aspect) on the retrieval results of the model was analyzed. It was found that the area with the steep slope and blocked radar signal is not conducive to estimate SM. The SM retrieval method constructed in this paper provides many advantages for some research and practical applications, and its application in other SAR data remains to be further studied.