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hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
hal.structure.identifierUniversity of Chinese Academy of Sciences [Beijing] [UCAS]
dc.contributor.authorBAI, Yu
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorZHAO, Tianjie
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorJIA, Li
hal.structure.identifierUSDA-ARS : Agricultural Research Service
dc.contributor.authorCOSH, Michael
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorSHI, Jiancheng
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorPENG, Zhiqing
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.accessioned2024-04-08T11:46:31Z
dc.date.available2024-04-08T11:46:31Z
dc.date.issued2022-08-13
dc.identifier.issn0034-4257
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195236
dc.description.abstractEnPassive microwave remote sensing of soil moisture is an underdetermined problem, as observed microwave emission from the landscape is affected by a variety of unknown surface parameters. Increasing observation information is an effective means to make retrievals more robust. In this study, a multi-temporal and multi-angular (MTMA) approach is proposed using SMOS (Soil Moisture and Ocean Salinity) satellite L-band data for retrieving vegetation optical depth (VODp, p indicates the polarization with H for horizontal and V for ver-tical)), effective scattering albedo (omega(eff)(p)), soil surface roughness (Z(p)(s)), and soil moisture (SMp). The advantage of the MTMA approach is that it does not need auxiliary data as inputs or constraints. SMOS polarization-dependent VOD are produced and compared at a global scale for the first time, and it is found that the polarization dif-ference of vegetation effects should not be ignored in the SM retrieval algorithm. The MTMA VOD retrievals are found to have a reasonable global spatial distribution, which is generally consistent with the VOD retrievals obtained from the SMOS Level 3 (SMOS-L3) and SMOS-IC Version 2 (V2) (referred to as SMOS-IC), except for showing relatively lower values over densely vegetated areas compared with the other two SMOS products. The spatial distribution of retrieved omega(eff)(p) generally shows a dependence on both VOD and land cover types. In addition, the values of MTMA-omega(eff)(V) are higher than that of MTMA-omega(eff)(H), indicating stronger microwave scattering of V-pol in the vegetation layer than that of H-pol. The retrieved surface roughness parameter (Z(p)(s)) ranges from 0.04 to 0.22 cm, and its spatial distribution is partially different from the existing roughness products/auxiliary data from SMOS and SMAP. The retrieved MTMA SM shows generally high correlations with in-situ measurements (11 dense observation networks) with overall correlation coefficients of > 0.75. The overall ubRMSE of MTMA-SMH and MTMA-SMV are < 0.055 m(3)/m(3) and lower than that of SMOS-IC and SMOS-L3 products. SMOS-IC generally presents higher correlation coefficients compared to MTMA in most sites outside China; in China, RFI filtering is crucial and makes it very difficult when comparing algorithms based on different brightness temperature products. The number of effective retrievals of MTMA-SMH and MTMA-SMV ranges from 1409 to 1640 and 1104 to 1603 respectively, which is more than that from SMOS-IC (from 236 to 1358) over the selected 11 networks. Therefore, it is concluded that by incorporating multi-temporal SMOS data, the proposed method of MTMA can be used to systematically retrieve SM, VOD and additional surface parameters (effective scattering albedo and surface roughness) with comparable or better performance of SM than that of SMOS-IC and SMOS-L3. Moreover, this paper for the first time produced a polarization-dependent SMOS VOD product at a global scale.
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by-nc/
dc.subject.enSMOS
dc.subject.enSoil moisture
dc.subject.enVegetation optical depth
dc.subject.enSurface roughness
dc.subject.enMulti-temporal
dc.subject.enMulti-angular
dc.title.enA multi-temporal and multi-angular approach for systematically retrieving soil moisture and vegetation optical depth from SMOS data
dc.typeArticle de revue
dc.identifier.doi10.1016/j.rse.2022.113190
dc.subject.halSciences de l'environnement
bordeaux.journalRemote Sensing of Environment
bordeaux.page1-24
bordeaux.volume280
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-03775471
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03775471v1
bordeaux.COinSctx_ver=Z39.88-2004&amp;rft_val_fmt=info:ofi/fmt:kev:mtx:journal&amp;rft.jtitle=Remote%20Sensing%20of%20Environment&amp;rft.date=2022-08-13&amp;rft.volume=280&amp;rft.spage=1-24&amp;rft.epage=1-24&amp;rft.eissn=0034-4257&amp;rft.issn=0034-4257&amp;rft.au=BAI,%20Yu&amp;ZHAO,%20Tianjie&amp;JIA,%20Li&amp;COSH,%20Michael&amp;SHI,%20Jiancheng&amp;rft.genre=article


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