Evaluating the Added Value of Spaceborne Soil Moisture Products for Land Surface Modeling Applications

A novel methodology is introduced for quantifying the added value of remotely-sensed soil moisture products for global land surface modeling applications. The approach is based on the assimilation of soil moisture retrievals into a simple surface water balance model driven by satellite-based precipitation products. Filter increments (i.e. discrete additions or subtractions of water suggested by the filter) are then compared to antecedent precipitation errors determined using higher quality rain gauge observations. Given the inherent difficulty of directly validating remotely-sensed soil moisture products using ground-based soil moisture observations, this assimilation-based proxy provides a valuable tool for efforts to improve soil moisture retrieval strategies and quantify the novel information content of remotely sensed soil moisture retrievals for land surface modeling applications. Using real spaceborne data, the approach is demonstrated for four different remotely-sensed soil moisture datasets along two separate transects in the southern United States.