We develop a vegetation scattering model to eliminate the effect of vegetation and surface roughness on the radar signal. The canopy water content is an important variable associated with the scattering effect of vegetation, and it can be calculated using the leaf area index, which is retrieved from PROSAILH optical model based on Landsat-8 images. The scattering model introduced the direct scattering contribution of underlying ground into the water cloud model. The experimental correlation length was replaced by a fitting parameter from C-band RADSARSAT-2 radar data to calculate the scattering contribution of underlying ground. Results demonstrate that the vegetation scattering model has a good performance in soil moisture retrieval with R2 of 0.805 and root-mean-square error of 0.039 m3 · m − 3. The applicability and capability of the scattering model will provide the operational potential of C-band radar data for soil moisture retrieval in an agricultural region.
Five surface backscattering models, including Oh, integral equation model (IEM), advanced integral equation model (AIEM), Dubois, and Shi models are selected to evaluate and reproduce synthetic aperture radar backscatter coefficients based on radar configuration and ground measurements at L- and C-bands. Regardless of bands or polarizations, the Oh model can attain a better performance among the five models with a root mean square error (RMSE) of about 2 dB, with the only exception being the AIEM and Shi models in VV polarization at the C-band. The Dubois model overestimates the radar signal and an underestimation is produced using the Shi model. The estimation accuracy of AIEM is significantly higher than that of IEM. Meanwhile, the performance of the scattering models in 0 to 7.6 cm is better than that in 0 to 20 cm. The frequency distribution of soil moisture over the field site approximates the normal distribution. Nevertheless, the estimated accuracy is not satisfactory for the inversion of AIEM. A site-specific calibration parameter is used at the C-band and improves the backscatter prediction for the AIEM. After calibration, the mean differences between the AIEM and RADARSAT-2 are nearly −1 dB with RMSEs of about 1 dB in the HH and VV polarizations. This work indicates that effective calibration factors can significantly improve the estimation accuracy and precisely implement soil moisture retrieval.