All-Sky Soil Moisture Estimation over Agriculture Areas from the Full Polarimetric SAR GF-3 Data

This study aims to estimate the soil moisture (SM) in all-sky agriculture areas using fully polarimetric synthetic aperture radar (SAR) Gaofen-3 (GF-3) data. The radar vegetation index (RVI) is obtained using the radar SAR data, which overcomes the difficulty that the optical data cannot construct the vegetation index in cloud-covered areas. The RVI is introduced into the water cloud model (WCM) to remove the contribution of vegetation to the total radar backscatter sigma(0) and obtain the soil backscattering coefficients sigma(0)(soil) with HH and VV polarization. Subsequently, sigma(0)(soil) and radar frequency data are introduced into the Chen model, and a semi-empirical model of SM estimation is established. The main findings of this study are as follows: (1) Compared with the sigma(0), the sigma(0 )(soil)obtained by the WCM has a stronger correlation with the SM. (2) In the cloud covered area, the accuracy of the estimated SM by synergistically using the WCM and the Chen model is ideal. An RMSE of 0.05 and a correlation coefficient (r) of 0.69 are achieved. In this study, the SM estimation method is not affected by clouds, and it shows many advantages for sustainable development, monitoring soil drought degree, and other related research.