Estimation of surface soil moisture and roughness from multi-angular ASAR imagery in the Watershed Allied Telemetry Experimental Research (WATER).

Radar remote sensing has demonstrated its applicability to the retrieval of basin-scale soil moisture. The mechanism of radar backscattering from soils is complicated and strongly influenced by surface roughness. Furthermore, retrieval of soil moisture using AIEM-like models is a classic example of the underdetermined problem due to a lack of credible known soil roughness distributions at a regional scale. Characterization of this roughness is therefore crucial for an accurate derivation of soil moisture based on backscattering models. This study aims to directly obtain surface roughness information along with soil moisture from multi-angular ASAR images. The method first used a semi-empirical relationship that connects the roughness slope (Zs) and the difference in backscattering coefficient (Δσ) from ASAR data in different incidence angles, in combination with an optimal calibration form consisting of two roughness parameters (the standard deviation of surface height and the correlation length), to estimate the roughness parameters. The deduced surface roughness was then used in the AIEM model for the retrieval of soil moisture. An evaluation of the proposed method was performed in a grassland site in the middle stream of the Heihe River Basin, where the Watershed Allied Telemetry Experimental Research (WATER) was taken place. It has demonstrated that the method is feasible to achieve reliable estimation of soil water content. The key challenge to surface soil moisture retrieval is the presence of vegetation cover which significantly impacts the estimates of surface roughness and soil moisture. [ABSTRACT FROM AUTHOR]