|Li, L -|
|Gaiser, P -|
|Gao, B -|
|Bevilacqua, R -|
|Njoku, E -|
|Rudiger, C -|
|Calvet, J -|
|Bindlish, R -|
Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 30, 2010
Publication Date: May 1, 2010
Repository URL: http://handle.nal.usda.gov/10113/56289
Citation: Li, L., Gaiser, P.W., Gao, B.C., Bevilacqua, R.M., Jackson, T.J., Njoku, E.G., Rudiger, C., Calvet, J.C., Bindlish, R. 2010. WindSat global soil moisture retrieval and validation. IEEE Transactions on Geoscience and Remote Sensing. 48(5):2224-2241. Interpretive Summary: A physically-based six-channel land algorithm was developed for simultaneously retrieving global soil moisture, vegetation water content and land surface temperature using WindSat multiple channel and polarization data. The WindSat instrument is the first spaceborne polarimetric microwave radiometer. The algorithm was validated for a range of temporal and spatial scales using soil moisture climatology, ground in situ network data, precipitation patterns, and green vegetation fraction data. Despite the diverse set of climatic and biome regions included in the validation studies, the comparison between in situ and retrieval data show consistently good performance for all the sites. At the global scale, the soil moisture and vegetation retrievals vary spatially according to the climate zones. The overall validation results suggest that the WindSat land algorithm is able to separate reasonably well the soil moisture and vegetation effects in the microwave data; and the retrievals can capture land parameter variations in the hydrological processes. It is expected that the validated WindSat products will be utilized in numerical weather prediction and climate models that provide important decision information to agricultural hydrology.
Technical Abstract: A physically based six-channel land algorithm is developed to simultaneously retrieve the global soil moisture, vegetation water content and land surface temperature. The algorithm is based on a maximum-likelihood estimation and uses WindSat passive microwave data at 10, 18.7 and 37 GHz. The global retrievals are validated at multi-spatial and multi-temporal scales against soil moisture climatologies, in situ network data, precipitation patterns, and Advanced Very High Resolution Radiometer (AVHRR) vegetation data. The in situ soil moisture data were acquired from the U.S., France and Mongolia for diverse land/vegetation cover and included extreme wet/dry soil conditions. In general, the volumetric soil moisture retrievals agree very well with the area-averaged in situ data with approximately a standard error of 4%, a 0.4% bias, and a correlation coefficient of 0.89, which meets the performance requirements for most science and operational applications. The retrieved soil moisture and vegetation water content distributions are very consistent with global climatology and mesoscale precipitation patterns. The comparisons between the WindSat vegetation retrievals and the AVHRR Green Vegetation Fraction data also reveal the consistency of these two independent data sets in terms of both spatial and temporal variations.