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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Smos Validation of Soil Moisture and Ocen Salinity (Smos) Soil Moisture over Watershed Networks in the U.S.

Authors
item Jackson, Thomas
item Bindlish, Rajat -
item Cosh, Michael
item Zhao, Tanjie -
item Starks, Patrick
item Bosch, David
item Moran, Mary
item Seyfried, Mark
item Kerr, Yann -
item Leroux, Delphine -
item Goodrich, David

Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 20, 2011
Publication Date: May 1, 2012
Citation: Jackson, T.J., Bindlish, R., Cosh, M.H., Zhao, T., Starks, P.J., Bosch, D.D., Moran, M.S., Seyfried, M.S., Kerr, Y., Leroux, D., Goodrich, D.C. 2012. SMOS validation of soil moisture and ocen salinity (SMOS) soil moisture over watershed networks in the U.S. IEEE Transactions on Geoscience and Remote Sensing. 50:1530-1543.

Interpretive Summary: Products from the first global satellite dedicated to soil moisture mapping, the Soil Moisture and Ocean Salinity (SMOS) satellite were validated over a range of climate and vegetation conditions in the U.S. This satellite utilizes a new an innovative antenna technology that provides good spatial resolution at the long wavelength it employs. A thorough validation of this new satellite and its technology was critical to insure product quality that will in turn support the widespread utilization of the data. 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 retrievals using an alternative satellite system. The root mean square error of the SMOS soil moisture estimates is 0.043 m3/m3 for the watershed networks. 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 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. Validated products from this satellite will be used improve the crop yield models employed by USDA, result in more accurate weather and climate forecasts, and guide the development of the next generation of satellites under development by NASA.

Technical Abstract: 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.

Last Modified: 10/22/2014
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