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dc.rights.licenseopenen_US
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorLUBAC, Bertrand
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorBURVINGT, Olivier
dc.contributor.authorNICOLAE-LERMA, Alexandre
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorSENECHAL, Nadia
IDREF: 077248430
dc.date.accessioned2023-06-16T12:01:39Z
dc.date.available2023-06-16T12:01:39Z
dc.date.issued2022-05-12
dc.identifier.issn2072-4292en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/182706
dc.description.abstractEnObjectives of this study are to evaluate the performance of different satellite-derived bathymetry (SDB) empirical models developed for multispectral satellite mission applications and to propose an uncertainty model based on inferential statistics. The study site is the Arcachon Bay inlet (France). A dataset composed of 450,837 echosounder data points and 89 Sentinel-2 A/B and Landsat-8 images acquired from 2013 to 2020, is generated to test and validate SDB and uncertainty models for various contrasting optical conditions. Results show that water column optical properties are characterized by a high spatio-temporal variability controlled by hydrodynamics and seasonal conditions. The best performance and highest robustness are found for the cluster-based approach using a green band log-linear regression model. A total of 80 satellite images can be exploited to calibrate SDB models, providing average values of root mean square error and maximum bathymetry of 0.53 m and 7.3 m, respectively. The uncertainty model, developed to extrapolate information beyond the calibration dataset, is based on a multi-scene approach. The sensitivity of the model to the optical variability not explained by the calibration dataset is demonstrated but represents a risk of error of less than 5%. Finally, the uncertainty model applied to a diachronic analysis definitively demonstrates the interest in SDB maps for a better understanding of morphodynamic evolutions of large-scale and complex coastal systems.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subject.ensatellite-derived bathymetry
dc.subject.enuncertainty
dc.subject.encoastal
dc.subject.enmorphodynamics
dc.subject.enmultispectral
dc.subject.enempirical model
dc.subject.encluster-based approach
dc.subject.enSentinel-2
dc.subject.enLandsat-8
dc.title.enPerformance and Uncertainty of Satellite-Derived Bathymetry Empirical Approaches in an Energetic Coastal Environment
dc.typeArticle de revueen_US
dc.identifier.doi10.3390/rs14102350en_US
dc.subject.halSciences de l'environnementen_US
bordeaux.journalRemote Sensingen_US
bordeaux.volume14en_US
bordeaux.hal.laboratoriesEPOC : Environnements et Paléoenvironnements Océaniques et Continentaux - UMR 5805en_US
bordeaux.issue10en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionCNRSen_US
bordeaux.teamMETHYSen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.exportfalse
dc.rights.ccCC BYen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing&rft.date=2022-05-12&rft.volume=14&rft.issue=10&rft.eissn=2072-4292&rft.issn=2072-4292&rft.au=LUBAC,%20Bertrand&BURVINGT,%20Olivier&NICOLAE-LERMA,%20Alexandre&SENECHAL,%20Nadia&rft.genre=article


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