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Global retrieval of soil moisture using neural networks trained with synthetic radiometric data

hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorRODRIGUEZ‐FERNANDEZ, Nemesio
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorRICHAUME, Philippe
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorKERR, Yann H.
hal.structure.identifierObservatoire de Paris
dc.contributor.authorAIRES, Filipe
hal.structure.identifierObservatoire de Paris
dc.contributor.authorPRIGENT, Catherine
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorWIGNERON, Jean-Pierre
dc.date.accessioned2024-04-08T12:00:46Z
dc.date.available2024-04-08T12:00:46Z
dc.date.issued2017
dc.date.conference2017-07-23
dc.identifier.isbn978-1-5090-4951-6
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/196097
dc.description.abstractEnThis paper discusses a methodology to construct a synthetic dataset using realistic geophysical data and the L-MEB model to compute synthetic brightness temperatures (Tb's) and to train a Neural Network (NN) for global retrievals of soil moisture (SM). The trained NNs are applied to real Tb's measured by the Soil Moisture and Ocean Salinity (SMOS) satellite (L-MEB NN). The objective is twofold. First, to compare and provide feedback to the operational algorithm. Second, to evaluate this approach in the context of pre-launch algorithm development. The performance of the L-MEB NN dataset was evaluated by comparing with time series of in situ measurements in North America. The correlation, standard deviation and bias of NN SM and in situ SM are similar to those obtained with the SMOS L3 SM product and with ECMWF models. The L-MEB NN dataset was also compared globally to the SMOS Level 3 SM and SM from ECMWF models. The L-MEB NN dataset is in general wetter than SMOS L3 SM, closer to ECMWF models. Some possible reasons are briefly discussed.
dc.language.isoen
dc.publisherIEEE Remote Sensing Society
dc.publisher.location(united states)
dc.source.titleIEEE International Geoscience and Remote Sensing Symposium Proceedings
dc.title.enGlobal retrieval of soil moisture using neural networks trained with synthetic radiometric data
dc.typeCommunication dans un congrès
dc.identifier.doi10.1109/IGARSS.2017.8127273
dc.subject.halSciences du Vivant [q-bio]
dc.subject.halSciences de l'environnement
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.conference.titleIEEE International Geoscience and Remote Sensing Symposium (IGARSS)
bordeaux.countryUS
bordeaux.title.proceedingIEEE International Geoscience and Remote Sensing Symposium Proceedings
bordeaux.conference.cityFort Worth
bordeaux.peerReviewedoui
hal.identifierhal-02736324
hal.version1
hal.invitednon
hal.conference.end2017-07-28
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02736324v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=IEEE%20International%20Geoscience%20and%20Remote%20Sensing%20Symposium%20Proceedings&rft.date=2017&rft.au=RODRIGUEZ%E2%80%90FERNANDEZ,%20Nemesio&RICHAUME,%20Philippe&KERR,%20Yann%20H.&AIRES,%20Filipe&PRIGENT,%20Catherine&rft.isbn=978-1-5090-4951-6&rft.genre=unknown


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