干旱区生态环境知识资源中心

Accurate soil moisture information is required for studying the global water and energy cycles as well as the carbon cycle. The AMSR-E sensor onboard NASA’s Aqua satellite offers a new means to accurately retrieve soil moisture information at a regional and global scale. However, the characterization of the factors such as precipitation, vegetation, cloud, ground roughness, and ice-snow packs is sensitive to the retrieval of the soil moisture content from the remotely sensed data.

This paper examines the models that are used to generate soil moisture products from US National Snow and Ice Data Center (NSIDC), and to adapt the models to improve the accuracy of soil moisture retrieval in Xinjiang, northwest China. The ground truth data collected by the WET and WatchDog instruments in Xinjiang were used to derive the empirical parameters for the regressive model that are suited to the conditions in Xinjiang. To improve the accuracy of inversion, the impact of precipitation’s lag-effect on the surface soil moisture has been addressed using the parameters monthly bases, daily variation and the lag-effect impact of precipitation in the improved model. The improved model is then used to retrieve the soil moisture information from the AMSR-E data. A comparative study between the result from the proposed model and the NSIDC products of May to September 2009 were performed with the AMSR-E data. Validation with ground truth and the comparison indicate that the improved model performs better and produces more accurate soil moisture maps than the NSIDC products in the study area.