SATELLITE SOIL MOISTURE DOWNSCALING USING RAINFALL RUNOFF MODEL

Most of the types of slope landslides are highly related by soil moisture. Observation of soil moisture by remote sensing approach can provide data with a wide coverage and fine temporal resolution. However, the soil condition of the localized landslide in a fine resolution cannot be detected by the satellite. This research aims to simulate the soil moisture using hydrological model in landslide-prone sub-catchment and to use the simulated runoff as a proxy for downscaling the satellite soil moisture. The study is conducted in upper Brantas River Basin Indonesia, which is vulnerable for flood and landslide. The soil moisture is derived from The Soil Moisture and Ocean Salinity (SMOS) from ESA's product with 15 km resolution which has been corrected from the biases using ground sensors and soil testing. Physically distributed hydrological model is applied with 1 km spatial resolution in a daily scale. The soil water content retrieved from the model with the same resolution can be reproduced dynamically. The downscaling follows Linear Regression, with the regression parameters derived from simulated soil moisture using Linear Least Square method. The verification by comparing the model output with coarse resolution SMOS product shows the plausible results with 0.93 correlation coefficient. The proposed method integrated with soil stability model will be useful for further application in landslide early warning system.