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The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E

hal.structure.identifierFaculty of Earth and Life Sciences
dc.contributor.authorVAN DER SCHALIE, Robin
hal.structure.identifierSpace Technology Center
dc.contributor.authorDE JEU, Richard A. M.
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorKERR, Yann H.
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.authorRODRIGUEZ‐FERNANDEZ, Nemesio
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorAL YAARI, Amen
hal.structure.identifierSpace Technology Center
dc.contributor.authorPARINUSSA, Robert Mathijs
hal.structure.identifierAgence Spatiale Européenne = European Space Agency [ESA]
dc.contributor.authorMECKLENBURG, Susanne
hal.structure.identifierEuropean Space Research and Technology Centre [ESTEC]
dc.contributor.authorDRUSCH, Matthias
dc.date.accessioned2024-04-08T12:10:14Z
dc.date.available2024-04-08T12:10:14Z
dc.date.issued2017
dc.identifier.issn0034-4257
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/196617
dc.description.abstractEnThis paper evaluates a methodology to integrate surface soil moisture retrievals from SMOS and AMSR-E into a single, consistent dataset retrieved by the Land Parameter Retrieval Model (LPRM). In a first step, the SMOS LPRM soil moisture retrievals were used as the baseline for optimizing the internal parameterization (i.e. surface roughness and single scattering albedo) of the AMSR-E LPRM retrievals. Secondly, to overcome the uniqueness of these datasets a linear scaling approach was applied resulting in a consistent soil moisture dataset. The new parameter set from the first step is similar for the two (low) frequencies of AMSR-E (i.e. C- and X-band) further improving their inter-comparability for both soil moisture and vegetation optical depth. Soil moisture retrievals from these AMSR-E frequencies were globally merged based on the availability of brightness temperatures that are free from RFI contamination (resulting in AMSR-E LPRMN). This new product was evaluated against both the SMOS LPRM product in the overlapping period (July 2010 to October 2011), as well as the standard, publicly available AMSR-E LPRM dataset (AMSR-E LPRMV3) for an almost 9 year period (January 2003 to October 2011). For the overlapping period, the AMSR-E and SMOS LPRM products show high temporal correlation coefficients (0.60 < R < 0.90) and low root mean square errors (rmse < 0.04 m3 m− 3) for NDVI values up to 0.60. Their agreement tends to drop over the well-known challenging areas such as the arctic region and tropical rainforest. A detailed evaluation over in situ sites from 5 in situ networks worldwide showed that AMSR-E LPRMN often outperforms SMOS LPRM in sparsely vegetated areas, with generally higher correlation coefficients in areas with NDVI < 0.3, and in general a lower unbiased rmse (ubrmse). In line with theoretical expectations, SMOS LPRM outperforms the AMSR-E LPRM product over the more densely vegetated areas. The newly developed AMSR-E LPRMN product was also compared against AMSR-E LPRMV3, revealing a significant increase (from 0.48 to 0.55) in temporal correlation coefficient over 16 in situ networks. This finding was confirmed through a large scale (50°N–50°S) precipitation based verification technique, the so-called Rvalue, which shows a superior performance of the newly developed AMSR-E LPRMN product. Additionally, the linear scaling of AMSR-E LPRMN to the SMOS LPRM leads to further reducing the ubrmse from 0.09 to 0.06 m3 m− 3 and the average bias from 0.14 to 0.00 m3 m− 3 over these stations. The AMSR-E LPRMN was furthermore compared against the top layer of two re-analysis models (i.e. from the Modern-Era Retrospective analysis for Research and Applications-Land and ERA-Interim/Land models) generally demonstrating increased correlation coefficients and reduced ubrmse with the exception of the challenging areas. As a result, this study shows the significant potential of SMOS LPRM to be a successful integrator to build a long term soil moisture record based on multiple passive microwave sensors.
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by-sa/
dc.subjecttélédétection
dc.subjectradiomètre
dc.subjectacquisition de données
dc.subjectcapteur smos
dc.subjectndvi
dc.subjectréseau de mesures
dc.subjecthumidité du sol
dc.subject.enremote sensing
dc.subject.enradiometer
dc.subject.ensoil moisture and ocean salinity
dc.title.enThe merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E
dc.typeArticle de revue
dc.identifier.doi10.1016/j.rse.2016.11.026
dc.subject.halSciences du Vivant [q-bio]
bordeaux.journalRemote Sensing of Environment
bordeaux.page180-193
bordeaux.volume189
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-01595237
hal.version1
hal.popularnon
hal.audienceNon spécifiée
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01595237v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing%20of%20Environment&rft.date=2017&rft.volume=189&rft.spage=180-193&rft.epage=180-193&rft.eissn=0034-4257&rft.issn=0034-4257&rft.au=VAN%20DER%20SCHALIE,%20Robin&DE%20JEU,%20Richard%20A.%20M.&KERR,%20Yann%20H.&WIGNERON,%20Jean-Pierre&RODRIGUEZ%E2%80%90FERNANDEZ,%20Nemesio&rft.genre=article


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