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Soil Moisture Retrievals From the WindSat Spaceborne Polarimetric Microwave Radiometer

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3 Author(s)
Robert M. Parinussa ; Hydrology and Remote Sensing Laboratory, USDA Agricultural Research Service, Beltsville, MD, USA ; Thomas R. H. Holmes ; Richard A. M. de Jeu

An existing methodology to derive surface soil moisture from passive microwave satellite observations is applied to the WindSat multifrequency polarimetric microwave radiometer. The methodology is a radiative-transfer-based model that has successfully been applied to a series of (historical) satellite sensors, including the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Brightness temperature observations from the WindSat and AMSR-E radiometers were compared, and the WindSat observations were adjusted to overcome small sensor differences (e.g., frequency, bandwidth, incidence angle, and original sensor calibration procedure). The method to relate Ka-band brightness temperature observations to land surface temperature was adapted to the overpass times of WindSat. Statistical analysis with both satellite-observed and in situ soil moistures indicates that the quality of the newly derived WindSat soil moisture product is similar to that obtained with AMSR-E after the adjustment of the WindSat brightness temperature observations. The average correlation coefficients (R) between satellite soil moisture and in situ observations are similar for the two satellites with average values of R = 0.60 for WindSat and R = 0.62 for AMSR-E as calculated from 33 sites. On a global scale, the average correlation coefficient between the two satellite soil moisture products is high with a value of R = 0.83. The results of this study demonstrate that soil moisture from WindSat is consistent with existing soil moisture products derived from AMSR-E using the land parameter retrieval model. Therefore, the soil moisture retrievals from these two satellites could easily be combined to increase the temporal resolution of satellite-derived soil moisture observations.

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:50 ,  Issue: 7 )