Process Study for Developing Algorithms to Quantitatively Estimate Hydrological Parameters Based on ALOS Data -A Case Study of Soil Moisture Estimation with Existing Algorithm-

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 land hydrological parameters i.e. soil moisture and snow parameters using ALOS data. As first step of this study, we applied existing algorithm to PALSAR data to estimate surface soil moisture. 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 and continuously maintaining. We derived surface soil moisture maps with 100m spatial resolutions and validated seasonal variation using ground truth data.