Soil moisture is important for fields not only hydrology but also meteorology. It plays important roles in the interactions between the land surface and the atmosphere, as well as in the partitioning of precipitation into runoff and ground water storage. In spite of its importance, soil moisture is not generally used for weather forecasting and water resources management because it is difficult to measure on a routine basis over large areas. The objective of this study is to develop algorithms to estimate spatial and temporal distributions of soil moisture using satellite remote sensing techniques. The Advanced Land Observing Satellite (ALOS, nicknamed "Daichi") was successfully launched on January 24, 2006 from Tanegashima Space Center, Japan, and it is continuously working very well. ALOS has an L-band Synthetic Aperture Radar (SAR) called PALSAR. In this study, we applied existing algorithm to PALSAR data and generated surface soil moisture maps with 100 m spatial resolution. The test sites are located in the Mongolian Plateau, where is spatially homogeneous with basically flat terrain features, and three Automatic Weather Stations (AWSs) and twelve Automatic Stations for Soil Hydrology (ASSH) are installed.
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