Vegetation effects on soil moisture estimation from ERS-2 SAR images

The aim of this study was to map soil moisture from ERS-2 SAR images by minimizing the effect of vegetation on the backscatter coefficient. Detailed analysis was carried out to identify the prominent crop descriptor (i.e. crop height, h; leaf area index, LAI; and plant water content, PWC), and to minimize its effect on soil moisture estimation. A semi-empirical water cloud model was used to eliminate the vegetation effects on the backscatter coefficient. Our results showed that the water cloud model based on LAI as the canopy descriptor was able to estimate the crop-covered backscatter coefficient more accurately than the models based on either of the other two crop descriptors. Once the crop-covered backscatter coefficient was determined, a nonlinear least square method (LSM) was implemented to estimate the volumetric soil moisture. A significantly high correlation (R-2 approximate to 0.94) between the estimated soil moisture and the corresponding observed soil moisture for barren land, as well as crop-covered surfaces, was obtained. Subsequently, individual soil moisture maps were generated from the three ERS-2 SAR images to depict the spatial distribution of soil moisture during the three seasons.