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A Downscaling Method for Improving the Spatial Resolution of Passive Microwave Derived Soil Moisture Product Based on MODIS Data

Soil moisture is an important parameter to be monitored in agricultural activities. Estimation on soil moisture is as important as understanding the process of hydrology and ecology. Nowadays, how to acquire extensive soil moisture information has became the key to this problem due to insufficient performance of water models. Remote sensing technology is able to obtain essential soil moisture information on a large scale quickly and efficiently. In order to integrate high coverage of microwave data with higher spatial resolution of optical/infrared satellite data, a downscaling algorithm is evaluated for the improvement of soil moisture at temporal and spatial resolution using AMSR-E/AMSR-2 (The Advanced Microwave Scanning Radiometer - EOS/2) and MODIS (The Moderate Resolution Imaging Spectroradiometer). The turbulent energy fluxes which link passive microwave derived soil moisture with several factors (eg: NDVI (Normalized Difference Vegetation Index), LST(Land Surface Temperature), albedo) at high resolution through evapotranspiration. Based on “universal triangle method”, NDVI, albedo, and LST from MODIS are used to downscale AMSR-E/AMSR-2 daily soil moisture from 25km to 1km in this study. With linear relationships between microwave soil moisture and several parameters (NDVI, LST, albedo), specific downscaling parameters of satellite images for same date which represents unique relations between soil moisture and energy flux was calculated through multivariate linear model between soil moisture at coarse resolution and several parameters(NDVI, LST, albedo). Downscaled soil moisture was obtained by calculation with downscaling parameters and NDVI, LST and albedo at higher spatial resolution. Because of discrepancy of different spatio-temporal scale between satellite date and in situ point data used in validation, we used a algorithm to rescale downscaled result to remove systematic differences. The scatter plot and linear fit of 10 years downscaled soil moisture and measured soil moisture in 10 cm depth from different agro-meteorological station in Inner Mongolia is plotted and analyzed. The performance of this algorithm is assessed by comparing the obtained daily downscaling results at 1 km with in situ soil moisture point measurement (10 cm depth) from agro-meteorological station in Inner Mongolia. The result showed that scatter plot and linear fit of 10 years downscaled soil moisture and station observation of 10 cm depth soil moisture with its bias and RMSE of −0.094 and 0.092. Results showed that downscaled soil moisture at 1km enable to represent value and variability of soil moisture content at a large scale and perform better than satellite data from single source because of higher coverage and spatial-temporal resolution.