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Validation of Soil Moisture and Ocean Salinity (SMOS) Soil Moisture Over Watershed Networks in the U.S.

Jackson, Thomas J., Bindlish, Rajat, Cosh, Michael H., Zhao, Tianjie, Starks, Patrick J., Bosch, David M., Seyfried, Mark, Moran, Mary Susan, Goodrich, David C., Kerr, Yann H., Leroux, Delphine
IEEE Transactions on Geoscience & Remote Sensing 2012 v.50 no.5 pp. 1530
algorithms, canopy, information sources, microwave radiation, new technology, oceans, product quality, radiometers, rain, remote sensing, salinity, satellites, sensors (equipment), soil water, vegetation, watersheds, United States
Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will in turn support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first operational passive L-band system. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based upon another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The ascending pass overall root mean square error of the SMOS soil moisture estimates is 0.043 m3/m3 for the watershed networks. There are bias issues at some sites that need to be addressed as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information on the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated and it is expected that the SMOS estimates will improve.