Accurate measurement of soil moisture along with precipitation is essential to improve our understanding of the exchange of water and energy between the atmosphere and land surface and to study the hydrological cycle and energy budget of the earth. However, despite recent improvements in soil moisture estimation, discrepancies still remain between in situ measured and remotely sensed soil moisture due to the many sources of sampling and measurement error. Insufficient ground truth data considerably limit the quantitative validation of retrieved soil moisture over the globe.

In this study, we compared retrieved soil moisture data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) with that of the North American Regional Reanalysis (NARR) which is based on the NCEP/Noah land surface model. The main focus was to investigate consistency at various time and space scales between the retrieved and the model data. Regional scale study domains of wet and dry regions were selected and basic statistics of the two data sets were intercompared. The relationship between precipitation and soil moisture for both data sets was also studied using time-series correlations and Empirical Orthogonal Function (EOF) analysis. Temporal and spatial patterns related with the interaction of precipitation and soil moisture, between the model and the retrieved data sets, were examined. Potential implications for how satellite-based soil moisture observations can contribute to improved large-scale soil moisture estimation through data assimilation using a land surface model were investigated.