Soil Moisture Retrieval From AMSR-E Data in Xinjiang (China): Models and Validation

• Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Main Authors: Zhang, Xianfeng, Zhao, Jiepeng, Sun, Quan, Wang, Xuyang, Guo, Yulong, Li, Jonathan
• Other Authors: Zhang, XF (reprint author), Peking Univ, Inst Remote Sensing, Beijing 100871, Peoples R China., Peking Univ, Inst Remote Sensing, Beijing 100871, Peoples R China., Peking Univ, GIS, Beijing 100871, Peoples R China., Univ Waterloo, Dept Geog & Environm Management, Waterloo, ON N2L 3G1, Canada.
• Format: Journal/Newspaper
• Language: English
• Published: ieee journal of selected topics in applied earth observations and remote sensing 2011
• Subjects: AMSR-E; arid area; inversion; precipitation; soil moisture; E LAND OBSERVATIONS; THERMAL INERTIA; MICROWAVE EMISSION; REMOTE ESTIMATION; INDEX; VEGETATION; SPACE; National Snow and Ice Data Center
• Online Access: https://hdl.handle.net/20.500.11897/155810; https://doi.org/10.1109/JSTARS.2010.2076336

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. http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000288678800015&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 Engineering, Electrical & Electronic Geography, Physical Remote Sensing Imaging Science & Photographic Technology SCI(E) EI 14 ARTICLE 1 117-127 4