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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #335031
Title: Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets

Author
item AL-YARRI, A. - National Institute For Agricultural Research (INIAP)
item WIGNERON, J. - National Institute For Agricultural Research (INIAP)
item KERR, Y. - University Of Toulouse
item RODRIGUEZ-FERNANDEZ, N. - University Of Toulouse
item O'NEILL, PEGGY - Goddard Space Flight Center
item Jackson, Thomas

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/30/2017
Publication Date: 8/1/2017
Publication URL: http://handle.nal.usda.gov/10113/5643926
Citation: Al-Yaari, A., Wigneron, J., Kerr, Y., Rodriguez-Fernandez, N., O'Neill, P., Jackson, T.J. 2017. Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets. Remote Sensing of Environment. 193:257-273.

Interpretive Summary: The benefit of combining data from two current soil moisture satellites was demonstrated using a new integrating retrieval algorithm. This was quantified by comparing standard retrieval results from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive missions and the new approach to in situ observations. Results show the close relationship between the two sets of brightness temperature observations. The integration of data sets from the two satellites will contribute to building a long term soil moisture record that will help understand the impacts of seasonal and decadal patterns on hydrology and agriculture.

Technical Abstract: Two satellites are currently monitoring surface soil moisture (SM) from L-band observations: SMOS (Soil Moisture and Ocean Salinity), a European Space Agency (ESA) satellite that was launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve surface soil moisture from SMAP data to create a global dataset of surface soil moisture, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using SMOS soil moisture and horizontal and vertical brightness temperatures (TB), over the 2013 - 2014 period. Next, this model was applied to SMAP TB data from 04/2015 to 02/2016. The retrieved surface soil moisture from SMAP (referred here to as SMAP-Reg) was compared to: (i) the operational SMAP L3 surface soil moisture (SMAP_SCA), retrieved using the baseline single channel retrieval algorithm (SCA) and (2) the operational SMOS L3 surface soil moisture, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from more than 400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https://ismn.geo.tuwien.ac.at/) in North of America (PBO_H2O, SCAN, SNOTEL, and USCRN) and in Europe (REMEDHUS, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE), Bias, Unbiased RMSE, and correlation coefficient (R). Results of the comparison of these various products with in situ observations show that the performance of both SMAP products i.e. SMAP_SCA and SMAP_Reg is similar and marginally better than the performance of SMOS L3 products particularly over the PBO_H2O, SCAN, and USCRN sites. We found that the correlation between all the three datasets and in situ measurements is best (R > 0.80) over RSMN site and worst (R < 0.50) over SNOTEL sites. The bias values showed that all products are generally dry, except over RSMN (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful for further integration between SMAP and SMOS datasets.