Soil moisture retrievals using ALOS2-ScanSAR and MODIS synergy over Tibetan Plateau

This paper investigates the soil moisture retrievals over Tibetan Plateau using multi-temporal ALOS2-PALSAR2 data acquired in ScanSAR mode, in contrary to the commonly used StripMap SAR imaging mode. Considering the dual-polarimetry with limited observables, the surface roughness parameters such as the RMS height and auto-correlation length are first estimated by optimizing the semi-empirical Oh2004 model applied to the ALOS2 data acquired under bare soil condition. The vegetation water content corresponding to each ALOS2 acquisition time is derived from an empirical model applied to the temporally interpolated MODIS NDVI data. Then, the obtained roughness and vegetation optical depth are substituted into the Water Cloud Model which we modified by adding a double-bounce component according to the L-band scattering processes to retrieve the effective scattering albedo and soil moisture. The results show that the optimized surface RMS height and the associated slope are negatively related to a dual-angular radar index ΔHH, indicating the feasibility to optimize the relatively stable surface roughness parameters before retrieving the soil moisture dynamics. The obtained vegetation optical depth which is cross-validated against a normalized cross-polarized radar descriptor indicates a significant increase from May to August, in response to the vegetation phenological growth. Furthermore, the time-variable scattering albedo is less than 0.08, and slightly increases with the vegetation development. By accounting for the double-bounce component, the retrieved soil moisture better agrees with the ground measurements with R = 0.89 and RMSE = 0.058 m3/m3, but exhibits an overestimation issue for the soil moisture higher than 0.35 m3/m3 due to the saturation of SAR signal and the relatively strong vegetation cover. A positive correspondence (R = 0.81) between the retrieved soil moisture and the interpolated NDVI was found, verifying their close coupling in the soil-vegetation system. This study deepens the insights in the potential integration between the optical and L-band microwave observations to retrieve soil moisture over vegetated area. •ALOS2 data acquired at ScanSAR mode are investigated to retrieve soil moisture.•Microwave and optical data are coupled in the retrieval process.•Water Cloud Model is calibrated by adding a double-bounce scattering component.•Three-component is better than two-component decomposition for soil moisture retrieval.