|Zhan, Xiwu - NOAA NESDIS|
Submitted to: Geoscience and Remote Sensing Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: February 23, 2007
Publication Date: July 20, 2007
Repository URL: http://handle.nal.usda.gov/10113/58457
Citation: Crow, W.T., Zhan, X. 2007. Continental-scale evaluation of remotely sensed soil moisture products. Geoscience and Remote Sensing Letters. 4(3):451-455. 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, 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 (i.e. the entire continental United States). 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 new data assimilation-based approach for the continental-scale evaluation of remotely sensed surface soil moisture retrievals is applied to four separate soil moisture products over the contiguous United States (CONUS). The approach is based on quantifying the ability of a given soil moisture product to correct for known rainfall errors when sequentially assimilated into a simple water balance model. Analysis results provide new insight into the continental-scale performance of surface soil moisture retrieval approaches based on passive microwave, active microwave and thermal remote sensing measurements.