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hal.structure.identifierCentre National d'Études Spatiales [Toulouse] [CNES]
hal.structure.identifierEstellus
hal.structure.identifierLaboratoire d'Etude du Rayonnement et de la Matière en Astrophysique et Atmosphères = Laboratory for Studies of Radiation and Matter in Astrophysics and Atmospheres [LERMA]
dc.contributor.authorSORIOT, Clément
hal.structure.identifierInstitut des Géosciences de l’Environnement [IGE]
dc.contributor.authorPICARD, Ghislain
hal.structure.identifierLERMA Cergy [LERMA]
dc.contributor.authorPRIGENT, Catherine
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorFRAPPART, Frédéric
hal.structure.identifierTakuvik Joint International Laboratory ULAVAL-CNRS
hal.structure.identifierDépartement de biologie, chimie et géographie & Centre d’études nordiques [Canada]
dc.contributor.authorDOMINE, Florent
dc.date.accessioned2024-04-08T11:47:04Z
dc.date.available2024-04-08T11:47:04Z
dc.date.issued2022-09
dc.identifier.issn0034-4257
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195265
dc.description.abstractEnSatellite microwave observations from 1.4 to 36 GHz already showed sensitivity to several geophysical parameters of sea ice such as Sea Ice Concentration (SIC), Sea Ice Thickness (SIT) or snow depth. The main goal of this article is to provide a realistic and comprehensive characterization of the sea ice and its snow cover that explains the microwave observations during a whole year using a radiative transfer model. For this purpose, we construct a unique dataset of passive microwave observations, to mimic the future Copernicus Imaging Microwave Radiometer (CIMR), along with the active microwave scatterometer data (ASCAT). CIMR database is used to classify sea ice microwave signatures in their spectral dimension with a machine learning technique while ASCAT data are used to help interpret the results of the classification. Classification results are then interpreted with a state-of-art sea ice and Snow Microwave Radiative Transfer model (SMRT) for all highlighted signatures and all seasons. Results make it possible to identify the specific behaviors from the observation co-variabilities for SIC, SIT, and snow structure. Our analysis underlined the role of the depth hoar over multi-year ice, for the interpretation of scattering signals in winter. Scattering signals that appear in late summer are explained by the presence of superimposed ice. This characterization will benefit from future advances in SMRT development, as well as the improved observations of future satellite missions.
dc.language.isoen
dc.publisherElsevier
dc.subject.enSea ice
dc.subject.enSnow
dc.subject.enCIMR
dc.subject.enRadiometer
dc.subject.enASCAT
dc.subject.enScatterometer
dc.subject.enSMRT
dc.subject.enClassification
dc.title.enYear-round sea ice and snow characterization from combined passive and active microwave observations and radiative transfer modeling
dc.typeArticle de revue
dc.identifier.doi10.1016/j.rse.2022.113061
dc.subject.halSciences de l'environnement
bordeaux.journalRemote Sensing of Environment
bordeaux.page1-15
bordeaux.volume278
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-03699154
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03699154v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing%20of%20Environment&rft.date=2022-09&rft.volume=278&rft.spage=1-15&rft.epage=1-15&rft.eissn=0034-4257&rft.issn=0034-4257&rft.au=SORIOT,%20Cl%C3%A9ment&PICARD,%20Ghislain&PRIGENT,%20Catherine&FRAPPART,%20Fr%C3%A9d%C3%A9ric&DOMINE,%20Florent&rft.genre=article


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