Abstract
The estimation of continuous soil moisture is critical for hydrological and agricultural study. The Land Data Assimilation System developed at the University of Tokyo (LDAS-UT) can estimate high temporal and spatial resolution. LDAS-UT was applied to the Pursat station which located near the Tonle Sap lake in Cambodia. The result showed that the low frequency of brightness temperature at the Pursat station was affected by the Tonle Sap lake area. To overcome this issue, we focused on the heterogeneity in the footprint, especially the existence of water bodies. We developed a water surface detection method to estimate water coverage within the low frequency data, and applied brightness temperature correction to remove effect of water body. Then, the corrected data was used with LDAS-UT to estimate soil moisture. The estimated soil moisture was in good agreement in dry season, and it can estimate low soil moisture value and increase accompany with precipitation during wet season.
