A semi-empirical inversion model for assessing surface soil moisture using AMSR-E brightness temperatures

SummaryIn 2004–2005, 2007 and 2009, three major drought disasters occurred in Guangdong Province of southern China, which caused serious economic losses. Hence, it has recently become an important research subject in China to monitor surface soil moisture (SSM) and the drought disaster quickly and accurately. SSM is an effective indicator for characterizing the degree of drought. First, using the brightness temperatures (Tb) of the Advanced Microwave Scanning Radiometer on the EOS Aqua Satellite (AMSR-E), a modified surface roughness index was developed to map the land surface roughness. Then by combining microwave polarization difference indices (MPDI)-based vegetation cover classification and the modified surface roughness index, a simple semi-empirical model of SSM was derived from the passive microwave radiative transfer equation using AMSR-E C-band Tb and observed surface soil temperature (Ts). The model was inverted to calculate SSM. The results showed the ability to discriminate over a broad range of SSM (7–73%) with an accuracy of 2.11% in bare ground and flat areas (R2 = 0.87), 2.89% in sparse vegetation and flat surface areas (R2 = 0.85), about 6–9% in dense vegetation areas and rough surface areas (0.80 ⩽ R2 ⩽ 0.83). The simulation results were also validated using in situ SSM data (R2 = 0.87, RMSE = 6.36%). Time series mapping of SSM from AMSR-E imageries further demonstrated that the presented method was effective to detect the initiation, duration and recovery of the drought events.

Highlight
► The land surface was classified into five types based on different vegetation and roughness condition.
► Improving a roughness index using three channels of AMSR-E Tb.
► Developing a SSM model integrating Tb, MPDI and observed temperature data.
► Inversed results discriminated over a broad range of SSM (7–73%, RMSE: 6.36%).
► The errors for dense vegetation and rough surfaces were smaller than former studies.
► The method was effective to detect the initiation, duration and recovery of drought events.