Comparative assessment of soil moisture estimation from land surface model and satellite remote sensing based on catchment water balance

Land surface models and satellite remote sensing are among the modern soil moisture retrieval techniques that can be used over large areas. However, the lack of 'ground truth' soil moisture measurements is still an obstacle in the evaluation and validation of soil moisture retrievals. In this study, a new scheme is used to assess soil moisture retrieval from both the NOAH Land surface Model (LSM) coupled with the fifth generation Mesoscale Model MM5 and the Advanced Microwave Scanning Radiometer AMSR-E. The proposed scheme is based on the strong correlation between changes in soil storage from rainfall runoff events and changes in the retrieved soil moisture either from the MM5-NOAH LSM or the AMSR-E. The aim of this study is to compare the application of the proposed scheme between these two different approaches for soil moisture estimation. The MM5-NOAH LSM provides soil moisture estimations at three different layers with depths 0-10 cm (surface layer 1), 10-40 cm (layer 2) and 40-100 cm (layer 3). In this study, the combined soil moisture over the top two layers (first and second) and the combined soil moisture over the first three layers (first, second and third) are used to account for the entire soil column for assessing the estimated soil moisture using changes in the storage from the water balance. The results have shown that the MM5 soil moisture from the combined top two layers has the better performance than either of the individual layers when compared to the catchment water storage. The results also show that the MM5-NOAH LSM soil moisture estimates have a slightly better performance than the AMSR-E surface soil moisture measurement. This preliminary assessment shows the benefits of using hydrological data in the validation of soil moisture retrieval methods.