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Uncertainty in soil moisture retrievals: An ensemble approach using SMOS L-band microwave data

hal.structure.identifierDepartment of Earth and Environmental Sciences
dc.contributor.authorQUETS, Jan
hal.structure.identifierDepartment of Earth and Environmental Sciences
dc.contributor.authorDE LANNOY, Gabriëlle J.M.
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorAL-YAARI, Amen
hal.structure.identifierNASA Jet Propulsion Laboratory
dc.contributor.authorCHAN, Steven
hal.structure.identifierUnited States Department of Agriculture [USDA]
dc.contributor.authorCOSH, Michael H.
hal.structure.identifierDepartment of Earth and Environmental Sciences
dc.contributor.authorGRUBER, Alexander
hal.structure.identifierGlobal Modeling and Assimilation Office [GMAO]
dc.contributor.authorREICHLE, Rolf H.
hal.structure.identifierVanderSat B.V.
dc.contributor.authorVAN DER SCHALIE, Robin
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorWIGNERON, Jean-Pierre
dc.date.accessioned2024-04-08T12:05:37Z
dc.date.available2024-04-08T12:05:37Z
dc.date.issued2019
dc.identifier.issn0034-4257
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/196366
dc.description.abstractEnThe uncertainty of surface soil moisture (SM) retrievals from satellite brightness temperature (TB) observations depends primarily on the choice of radiative transfer model (RTM) parameters, prior SM information and TB inputs. This paper studies the sensitivity of several established and experimental SM retrieval products from the Soil Moisture Ocean Salinity (SMOS) mission to these choices at 11 reference sites, located in 7 watersheds across the United States (US). Different RTM parameter sets cause large biases between retrievals. Whereas typical RTM parameter sets are calibrated for SM retrievals, it is shown that a parameter set carefully optimized for TB forward modeling can also be used for retrieving SM. It is also shown that the inclusion of dynamic prior SM estimates in a Bayesian retrieval scheme can strongly improve SM retrievals, regardless of the choice of RTM parameters. The second part of this paper evaluates the ensemble uncertainty metrics for SM retrievals obtained by propagating a wide range of RTM parameters through the RTM, and the relationship with time series metrics obtained by comparing SM retrievals with in situ data. As expected for bounded variables, the total spread in the ensemble SM retrievals is smallest for wet and dry SM values and highest for intermediate SM values. After removal of the strong long-term SM bias associated with the RTM parameter values for individual ensemble members, the remaining anomaly ensemble SM spread shows higher values when SM deviates further from its long-term mean SM. This reveals higher-order biases (e.g. differences in variances) in the retrieval error, which should be considered when characterizing retrieval error. The time-average anomaly ensemble SM spread of 0.037 m(3)/m(3) approximates the actual time series unbiased root-mean-square-difference of 0.042 m(3)/m(3) between ensemble mean retrievals and in situ data across the reference sites.
dc.language.isoen
dc.publisherElsevier
dc.subjectensemble
dc.subject.enretrieval
dc.subject.enradiative transfer model
dc.subject.enparameters
dc.subject.enuncertainty
dc.title.enUncertainty in soil moisture retrievals: An ensemble approach using SMOS L-band microwave data
dc.typeArticle de revue
dc.identifier.doi10.1016/j.rse.2019.05.008
dc.subject.halSciences du Vivant [q-bio]
dc.subject.halSciences de l'environnement
bordeaux.journalRemote Sensing of Environment
bordeaux.page133-147
bordeaux.volume229
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-02622267
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02622267v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing%20of%20Environment&rft.date=2019&rft.volume=229&rft.spage=133-147&rft.epage=133-147&rft.eissn=0034-4257&rft.issn=0034-4257&rft.au=QUETS,%20Jan&DE%20LANNOY,%20Gabri%C3%ABlle%20J.M.&AL-YAARI,%20Amen&CHAN,%20Steven&COSH,%20Michael%20H.&rft.genre=article


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