Hydrological Evaluation of Satellite Soil Moisture Data in Two Basins of Different Climate and Vegetation Density Conditions

Accurate soil moisture information can be assimilated into an operational hydrological model to greatly enhance its flood and drought forecasting performances. Although satellite soil moisture observations are useful information, their validations are generally hindered by the large spatial difference with the point-based measurements, hence they cannot be directly applied in hydrological modelling. This study adopts a widely applied operational hydrological model Xinanjiang (XAJ) as a hydrological validation tool because it is spatially more scale-matched. Two widely used microwave sensors (Soil Moisture and Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E)) are evaluated, over two basins (French Broad and Pontiac, respectively) with different climate types and vegetation covers. All soil moisture datasets evaluated in this study are procured for the period of January 2010 to October 2011, a period during which both SMOS and AMSR-E products are available for the study areas. The results demonstrate SMOS outperforms AMSR-E in the Pontiac basin (cropland); while both products perform poorly in the French Broad basin (forest). The Moderate-Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) thresholds of 0.81 and 0.64 (for cropland and forest basins, respectively) are very effective in dividing soil moisture datasets into ‘denser’ and ‘thinner’ vegetation periods. As a result, in the cropland, the statistical performance is further improved for both SMOS and AMSR-E (i.e., improved to Nash-Sutcliffe Efficiency (NSE) = 0.74, Root Mean Square Error (RMSE) = 0.0059 m and NSE = 0.58, RMSE = 0.0066 m for SMOS and AMER-E, respectively). The overall assessment suggests that SMOS is of reasonable quality in estimating basin-scale soil moisture at moderate-vegetated areas, and NDVI is a useful indicator for further improving the performance.