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Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations

hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorAL-YAARI, A.
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorWIGNERON, J.P.
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorKERR, Yann H.
hal.structure.identifierUniversity of Amsterdam [Amsterdam] = Universiteit van Amsterdam [UvA]
dc.contributor.authorDE JEU, R.
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorRODRIGUEZ‐FERNANDEZ, Nemesio
hal.structure.identifierUniversity of Amsterdam [Amsterdam] = Universiteit van Amsterdam [UvA]
dc.contributor.authorVAN DER SCHALIE, R.
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
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.identifierFaculty of Earth and Life Sciences [Amsterdam] [FALW]
dc.contributor.authorDOLMAN, A.
hal.structure.identifierMilieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols [METIS]
hal.structure.identifierInstitut Pierre-Simon-Laplace [IPSL]
dc.contributor.authorDUCHARNE, Agnès
dc.date.accessioned2024-04-08T12:12:03Z
dc.date.available2024-04-08T12:12:03Z
dc.date.issued2016
dc.identifier.issn0034-4257
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/196731
dc.description.abstractEnWithin the framework of the efforts of the European Space Agency (ESA) to develop the most consistent and complete record of surface soil moisture (SSM), this study investigated a statistical approach to retrieve a global and long-term SSM dataset from space-borne observations. More specifically, this study investigated the ability of physically based statistical regressions to retrieve SSM from two passive microwave remote sensing observations: the Advanced Microwave Scanning Radiometer (AMSR-E; 2003–Sept. 2011) and the Soil Moisture and Ocean Salinity (SMOS) satellite. Regression coefficients were calibrated using AMSR-E horizontal and vertical brightness temperature (TB) observations and SMOS level 3 SSM (SMOSL3; as a training dataset). This calibration process was carried out over the June 2010–Sept. 2011 period, over which both SMOS and AMSR-E observations coincide. Based on these calibrated coefficients, a global SSM product (referred here to as AMSR-reg) was computed from the AMSR-E TB observations during the 2003–2011 period. The regression quality was assessed by evaluating the AMSR-reg SSM product against the SMOSL3 SSM product over the period of calibration, in terms of correlation (R) and Root Mean Square Error (RMSE). A good agreement (mean global R = 0.60 and mean global RMSE = 0.057 m3/m3), was obtained between the AMSR-reg and SMOSL3 SSM products particularly over Australia, central USA, central Asia, and the Sahel. In a second step, the AMSR-reg SSM retrievals and commonly used AMSR-E SSM retrievals derived from the Land Parameter Retrieval Model (AMSR-LPRM), were evaluated against two kinds of SSM references (i) the global MERRA-Land SSM simulations and (ii) in situ measurements over 2003–2009. The results demonstrated that both AMSR-reg and AMSR-LPRM (better when considering global simulations) successfully captured the temporal dynamics of the references used having comparable correlation values. AMSR-reg was more consistent with MERRA-land than AMSR-LPRM in terms of unbiased RMSE (ubRMSE) with a global average of ubRMSE of 0.055 m3/m3 for AMSR-reg and 0.084 m3/m3 for AMSR-LPRM. In conclusion, the statistical regression, which is tested here for the first time using long-term spaceborne TB datasets, appears to be a promising approach for retrieving SSM from passive microwave remote sensing TB observations.
dc.language.isoen
dc.publisherElsevier
dc.subjectSMOS
dc.subjectsoil moisture
dc.subject.enAMSR-E
dc.subject.enstatistical regression
dc.title.enTesting regression equations to derive long-term global soil moisture datasets from passive microwave observations
dc.typeArticle de revue
dc.identifier.doi10.1016/j.rse.2015.11.022
dc.subject.halPlanète et Univers [physics]/Sciences de la Terre/Hydrologie
dc.subject.halPlanète et Univers [physics]/Interfaces continentales, environnement
bordeaux.journalRemote Sensing of Environment
bordeaux.page453–464
bordeaux.volume180
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-01312307
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01312307v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing%20of%20Environment&rft.date=2016&rft.volume=180&rft.spage=453%E2%80%93464&rft.epage=453%E2%80%93464&rft.eissn=0034-4257&rft.issn=0034-4257&rft.au=AL-YAARI,%20A.&WIGNERON,%20J.P.&KERR,%20Yann%20H.&DE%20JEU,%20R.&RODRIGUEZ%E2%80%90FERNANDEZ,%20Nemesio&rft.genre=article


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