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Reappraisal of SMAP inversion algorithms for soil moisture and vegetation optical depth

hal.structure.identifierDepartment of Civil, Environmental and Geo-Engineering [Minneapolis]
dc.contributor.authorGAO, Lun
hal.structure.identifierDepartment of Civil, Environmental and Geo-Engineering [Minneapolis]
dc.contributor.authorEBTEHAJ, Ardeshir
hal.structure.identifierJet Propulsion Laboratory [JPL]
dc.contributor.authorCHAUBELL, Mario Julian
hal.structure.identifierCalifornia Department of Water Resources
dc.contributor.authorSADEGHI, Morteza
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorLI, Xiaojun
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorJ.-P., Wigneron
dc.date.accessioned2024-04-08T11:50:30Z
dc.date.available2024-04-08T11:50:30Z
dc.date.issued2021-10
dc.identifier.issn0034-4257
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195387
dc.description.abstractEnNASA's Soil Moisture Active Passive (SMAP) satellite mission has been providing high-quality global estimates of soil moisture (SM) and vegetation optical depth (VOD) using L-band radiometry since 2015. To date, a variety of retrieval algorithms as well as surface roughness and scattering albedo have been developed. However, a comprehensive evaluation of different algorithms with the new surface parameters across diverse biomes, climates, and terrain slopes is lacking. To narrow down this knowledge gap, here we examine the performance of various existing algorithms, including V-pol Single Channel Algorithms (SCA-V), H-pol Single Channel Algorithms (SCA-H), classic DCA, extended DCA (E-DCA), regularized DCA (RDCA), land parameter retrieval model (LPRM), multi-temporal DCA (MT-DCA), constrained multi-channel algorithm (CMCA), and spatially constrained multi-channel algorithm (S-CMCA). The SM estimates are evaluated against in-situ measurements from the International Soil Moisture Network (ISMN) while VOD estimates are compared with the two-band enhanced vegetation index (EVI2), tree height, and aboveground biomass. This study has led to several important findings: (1) The overall bias, root mean square error (RMSE), and unbiased RMSE (ubRMSE) of SM estimates from different algorithms generally increase with vegetation density while their temporal correlations with in-situ measurements decrease as the terrain slope increases. (2) The divergence between different SM estimates is relatively larger over forested areas than non-forested areas. (3) In terms of temporal correlation with in-situ measurements, the SCA-V and RDCA outperform other algorithms over most land cover types and climates. (4) SCA-H typically underestimates SM more compared to other algorithms across sparsely vegetated areas and most climates. (5) The ubRMSE values demonstrate that all algorithms have close performance when EVI2 is less than 0.3; however, the performance of classic DCA decays notably when EVI2 exceeds 0.3. (6) VOD retrievals from RDCA exhibit improved spatial correlations with EVI2, tree height, and aboveground biomass across the globe compared to other algorithms. Overall, RDCA exhibits a good compromise between the high performance of SM and VOD.
dc.language.isoen
dc.publisherElsevier
dc.subject.enSoil moisture
dc.subject.enVegetation optical depth
dc.subject.enL-band radiometry
dc.subject.enSMAP
dc.title.enReappraisal of SMAP inversion algorithms for soil moisture and vegetation optical depth
dc.typeArticle de revue
dc.identifier.doi10.1016/j.rse.2021.112627
dc.subject.halSciences de l'environnement
bordeaux.journalRemote Sensing of Environment
bordeaux.page1-17
bordeaux.volume264
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-03339001
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03339001v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing%20of%20Environment&rft.date=2021-10&rft.volume=264&rft.spage=1-17&rft.epage=1-17&rft.eissn=0034-4257&rft.issn=0034-4257&rft.au=GAO,%20Lun&EBTEHAJ,%20Ardeshir&CHAUBELL,%20Mario%20Julian&SADEGHI,%20Morteza&LI,%20Xiaojun&rft.genre=article


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