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Evaluating Soil Moisture Retrievals from ESA's SMOS and NASA's SMAP Brightness Temperature DatasetsTwo satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d'tudes spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite 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 SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Donnes) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40 deg incidence angle, over the 2013 - 2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, 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, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE),Bias, Unbiased RMSE (UnbRMSE), 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 48 similar and marginally better to that of the SMOSL3 product particularly over the PBO_H2O, SCAN, and USCRN sites. However, SMOSL3 SM was closer to the in situ observations over the DAHRA and Oznet sites. We found that the correlation between all three datasets and in situ measurements is best (R 0.80) over the Oznet sites and worst (R 0.58) over the SNOTEL sites for SMAP_SCA and over the DAHRA and SMOSMANIA sites (R 0.51 and R 0.45 for SMAP_Reg and SMOSL3, respectively). The Bias values showed that all products are generally dry, except over RSMN, DAHRA, and Oznet (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful to improve the consistency between SMAP and SMOS datasets.
Document ID
20170005686
Acquisition Source
Goddard Space Flight Center
Document Type
Accepted Manuscript (Version with final changes)
Authors
A Al-Yaari (Institut National de la Recherche Agronomique Villenave d'Ornon, France)
J-P Wigernon (Institut National de la Recherche Agronomique Villenave d'Ornon, France)
Y Kerr (Centre d'Etudes Spatiales de la Biosphere Toulouse, France)
N Rodriguez-Fernandez (Centre d'Etudes Spatiales de la Biosphere Toulouse, France)
P E O'Neill (Goddard Space Flight Center Greenbelt, Maryland, United States)
T J Jackson (Agricultural Research Service Washington D.C., District of Columbia, United States)
G J M De Lannoy ORCID(KU Leuven Leuven, Belgium)
A Al Bitar (Centre d'Etudes Spatiales de la Biosphere Toulouse, France)
A Mialon (Centre d'Etudes Spatiales de la Biosphere Toulouse, France)
P Richaume (Centre d'Etudes Spatiales de la Biosphere Toulouse, France)
J P Walker (Monash University Melbourne, Victoria, Australia)
A Mahmoodi (Centre d'Etudes Spatiales de la Biosphere Toulouse, France)
S Yueh (Jet Propulsion Lab La Cañada Flintridge, California, United States)
Date Acquired
June 21, 2017
Publication Date
March 20, 2017
Publication Information
Publication: Remote Sensing of Environment
Publisher: Elsevier
Volume: 193
Issue Publication Date: May 1, 2017
ISSN: 0034-4257
e-ISSN: 1879-0704
DOI: doi:10.1016/j.rse.2017.03.010
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN42891
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
SMOS
SMAP
statistical regression

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