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A deep neural network based SMAP soil moisture product

hal.structure.identifierUniversity of Minnesota [Crookston] [UMC]
hal.structure.identifierUniversity of Illinois at Urbana-Champaign [Urbana] [UIUC]
dc.contributor.authorGAO, Lun
hal.structure.identifierUniversity of Minnesota [Crookston] [UMC]
dc.contributor.authorGAO, Qiang
hal.structure.identifierSouth Dakota State University [SDSTATE]
dc.contributor.authorZHANG, Hankui
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorLI, Xiaojun
hal.structure.identifierJet Propulsion Laboratory [JPL]
dc.contributor.authorCHAUBELL, Mario Julian
hal.structure.identifierUniversity of Minnesota [Crookston] [UMC]
dc.contributor.authorEBTEHAJ, Ardeshir
hal.structure.identifierDepartment of Aerospace Engineering and Mechanics [Minneapolis] [AEM]
dc.contributor.authorSHEN, Lian
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorJ.-P., Wigneron
dc.date.accessioned2024-04-08T11:47:07Z
dc.date.available2024-04-08T11:47:07Z
dc.date.issued2022-08
dc.identifier.issn0034-4257
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195268
dc.description.abstractEnIn this paper, it is demonstrated that while satellite soil moisture (SM) retrievals often have minimum biases, reanalysis data can capture more temporal variability of SM, especially for non-cropland areas - when validated against in situ measurements. Accordingly, this paper presents a deep neural network (DNN) that utilizes the merits of a suite of existing satellite and reanalysis products to produce a new SM product with minimum (maximum) bias (correlation) - using NASA's Soil Moisture Active Passive (SMAP) data and ERA5 reanalysis. The benchmark of the network is a bias-adjusted SM with maximum correlation with in situ data over each land cover type. The mean of the benchmark data is adjusted to the product that exhibits a minimum bias over each land-cover type. Consistent with the laws of L-band microwave propagation in soil and canopy, the input variables of DNN include polarized SMAP brightness temperatures, incidence angle, vegetation scattering albedo, surface roughness parameter, surface water fraction, effective soil temperatures, bulk density, clay fraction, and vegetation optical depth from the normalized difference vegetation index (NDVI) climatology. The DNN is trained and validated using two years (04/2015-03/2017) of global data and deployed for assessment of its performance from 04/2017 to 03/2021. The testing results against in situ measurements demonstrate that the DNN outputs typically exhibit improved error quality metrics over most land-cover types and climate regimes and can properly capture SM temporal dynamics, beyond each SMAP product across regional to continental scales.
dc.language.isoen
dc.publisherElsevier
dc.subject.enSoil moisture
dc.subject.enL-band radiometry
dc.subject.enSMAP
dc.subject.enDeep neural networks
dc.title.enA deep neural network based SMAP soil moisture product
dc.typeArticle de revue
dc.identifier.doi10.1016/j.rse.2022.113059
dc.subject.halSciences de l'environnement
bordeaux.journalRemote Sensing of Environment
bordeaux.page1-15
bordeaux.volume277
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-03696623
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03696623v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing%20of%20Environment&rft.date=2022-08&rft.volume=277&rft.spage=1-15&rft.epage=1-15&rft.eissn=0034-4257&rft.issn=0034-4257&rft.au=GAO,%20Lun&GAO,%20Qiang&ZHANG,%20Hankui&LI,%20Xiaojun&CHAUBELL,%20Mario%20Julian&rft.genre=article


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