A Novel Method for Quantifying Value in Spaceborne Soil Moisture Retrievals

Abstract

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. A synthetic twin experiment demonstrates that the correlation coefficient between antecedent precipitation errors and filter increments provides an effective proxy for the accuracy of the soil moisture retrievals themselves. Given the inherent difficulty of directly validating remotely sensed soil moisture products using ground-based 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. Results suggest that the relative superiority of various retrieval strategies varies geographically.