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hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorAL YAARI, Amen
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorWIGNERON, Jean-Pierre
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorKERR, Yann H.
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorRODRIGUEZ‐FERNANDEZ, Nemesio
hal.structure.identifierNASA Goddard Space Flight Center [GSFC]
dc.contributor.authorO'NEILL, P.E.
hal.structure.identifierUSDA-ARS : Agricultural Research Service
dc.contributor.authorJACKSON, T.J.
hal.structure.identifierDepartment of Earth and Environmental Sciences [Leuven] [EES]
dc.contributor.authorDE LANNOY, G.J.M.
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorAHMAD, Al Bitar
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorMIALON, Arnaud
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorRICHAUME, Philippe
hal.structure.identifierDepartment of Civil Engineering
dc.contributor.authorWALKER, J.P.
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorMAHMOODI, Ali
hal.structure.identifierJet Propulsion Laboratory [JPL]
dc.contributor.authorYUEH, S.
dc.date.accessioned2024-04-08T11:35:04Z
dc.date.available2024-04-08T11:35:04Z
dc.date.issued2017
dc.identifier.issn0034-4257
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/194988
dc.description.abstractEnTwo 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'études 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 Données) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40° 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 > 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 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.
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by-sa/
dc.subjectcycle du carbone
dc.subjecthumidité du sol
dc.subjectcycle de l'eau
dc.subjectsalinité des océans
dc.subjecteau de surface
dc.subjecttélédétection
dc.subject.enSMOS
dc.subject.enSMAP
dc.subject.ensoil moisture
dc.subject.enstatistical regression
dc.subject.encarbon cycle
dc.subject.enremote sensing
dc.title.enEvaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets
dc.typeArticle de revue
dc.identifier.doi10.1016/j.rse.2017.03.010
dc.subject.halSciences de l'environnement/Ingénierie de l'environnement
dc.subject.halPlanète et Univers [physics]/Interfaces continentales, environnement
dc.subject.halSciences du Vivant [q-bio]/Sciences agricoles/Sciences et techniques de l'agriculture
dc.subject.halSciences de l'ingénieur [physics]/Traitement du signal et de l'image
bordeaux.journalRemote Sensing of Environment
bordeaux.page257-273
bordeaux.volume193
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-01605761
hal.version1
hal.popularnon
hal.audienceNon spécifiée
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01605761v1
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