Inversion of surface soil moisture based on ASAR-GM data: Combining water cloud and change detection models

Surface soil moisture (SSM) is a significant component in the modeling of climatological, hydrological and ecological systems. In this study, an approach is presented and validated for combining the water cloud and Change Detection models to invert surface soil moisture using ENVISAT ASAR Global Monitor mode images (ASAR-GM data). A total of 25 images were selected in HH polarization. We have used parameters of the water cloud model best suited for eliminating the influence of vegetation cover on ASAR-GM and MOD15A2 LAI data in the Jiangsu Province. In our algorithm, Leaf Area Index (LAI) was used to substitute Vegetation Water Content (VWC). Relative soil moisture was calculated using the Change Detection method which eliminates the influence of surface roughness. Due to the relatively high rainfall and efficient irrigation system in this area as observed from in-situ measurement data, the lowest relative soil moisture (degree of duration) is about 30%. Therefore, the formula for the Change Detection method should apply a gain of 0.7, and an offset of 0.3, and thereafter converting the results to volumetric soil moisture (m
v
) in accordance with local soil parameters. Finally, the retrieved volumetric soil moisture was validated with in-situ data, determination coefficient (R2) is 0.348 and a root mean square error (RMSE) in 0.087 cm3/cm3 was recorded, based on the method involving the combination of water cloud and Change Detection models. Compared with the conventional Change Detection method, the R2 of 0.104 was increased and the RMSE of 0.014 cm3/cm3 was reduced, indicating the robustness of our proposed soil moisture retrieval algorithm.