A Robust Coinversion Model for Soil Moisture Retrieval From Multisensor Data

Optical remote sensing has been widely used to estimate soil moisture. However, modeling soil moisture dynamics across a large area based on remotely sensed optical data still poses a problem because of its spatial discontinuity due to cloud contamination. This study proposes a multisensor strategy for better mapping surface soil moisture on a daily basis at a regional scale. The basic idea is to decompose the surface soil moisture at any location into two terms, namely, baseline value in an observed period and daily variation, and to estimate for each term differently. For a certain day of interest, the corresponding 16-day composite of Moderate Resolution Imaging Spectroradiometer (MODIS) data is used to estimate the soil moisture baseline values across space, and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) data are employed to estimate the daily variations. The proposed model was applied to produce daily surface soil moisture maps at a 1-km resolution for the fairly large study area of Xinjiang, China, regardless of the local weather conditions. It was found that the integrated use of MODIS and AMSR-E data was able to achieve significantly higher accuracy in surface soil moisture estimation (with a root-mean-square error of 3.99% in May and 4.43% in August, 2009) than the approaches based on either data alone could. The proposed model is expected to perform well for mapping surface soil moisture in other arid areas after the required parameters are calibrated with the local field data.