Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer reviewed journal
Publication Acceptance Date: 7/14/2008
Publication Date: N/A
Citation: Interpretive Summary: Estimates of surface soil moisture derived from satellite observations can have value for range of agricultural applications including: drought monitoring, crop yield forecasting, and long-term precipitation forecasts from numerical weather prediction models. Unfortunately, a severe lack of accurate ground-based soil moisture observations hinders the development and testing of surface soil moisture retrieval algorithms. Without such observations it is very difficult to objectively compare two competing retrieval approaches and learn what approach works best. This uncertainty has limited the development of key agricultural applications for this data. Here, we present and apply a novel approach (based on a data assimilation mathematical framework and ancillary rainfall observations) that allows for the objective intercomparison of competing soil moisture retrieval strategies over a much wider spatial and temporal domain than was previously possible. Adoption of the approach will make it much easier to objectively test remotely-sensed soil moisture products and aid in the development of accurate soil moisture retrieval algorithms for agricultural landscapes.
Technical Abstract: A novel methodology is introduced for quantifying the value of remotely-sensed soil moisture products for 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 feedback tool for efforts to optimize soil moisture retrieval strategies and define the geographic extent over which spaceborne soil moisture retrievals contribute value to global land surface modeling. Using real data, the approach is demonstrated for four different remotely-sensed soil moisture datasets over two separate transects in the southern United States. Results suggest that the relative superiority of various retrieval strategies may vary geographically.