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hal.structure.identifierHenan University
dc.contributor.authorWANG, Zengyan
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorCHE, Tao
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorZHAO, Tianjie
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorDAI, Liyun
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorLI, Xiaojun
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorWIGNERON, Jean-Pierre
dc.date.accessioned2024-04-08T11:50:19Z
dc.date.available2024-04-08T11:50:19Z
dc.date.issued2021
dc.identifier.issn1939-1404
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195381
dc.description.abstractEnLong-term surface soil moisture (SM) data are increasingly needed in water budget and energy balance analysis of watersheds. The performance of nine remotely sensed SM products from Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) missions are evaluated based on observations collected from distributed observation networks in the Heihe River Basin (HRB) of China during 2013 to 2017. Results show that the SMAP Level 3 dual channel algorithm SM retrievals reflect the seasonal SM variations well with high temporal correlations of similar to 0.7 and high accuracy within 0.04 m(3)/m(3) in terms of unbiased root mean squared error (ubRMSE) over the grassland in the HRB. The SMOS level 3 SM retrievals present increased underestimation and ubRMSE of similar to 0.10 m(3)/m(3) as the vegetation increases. The newly published SMOS Institut National de la Recherche Agronomique-Centre d'Etudes Spatiales de la BIOsphere product in version 2 outperforms the SMOS level 3 product with improved temporal correlation coefficient above 0.4 and lower ubRMSE of similar to 0.05 m(3)/m(3). AMSR2 Land Parameter Retrieval Algorithm SM products show extremely large overestimation over the vegetated regions in HRB, especially the C-band products. Drastically high underestimation biases are observed in the Japan Aerospace Exploration Agency AMSR2 SM product. Parameter uncertainty analyses indicate that the different parameterization schemes of vegetation optical depth inputs could be one of the main reasons resulting in the systematic overestimation/underestimation biases in the AMSR2/SMOS/SMAP SM retrievals. The findings aim to provide insights into studies on algorithms refinements and data fusions of SM products in HRB.
dc.language.isoen
dc.publisherIEEE
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enEvaluation
dc.subject.enHeihe River Basin (HRB)
dc.subject.enJapan Aerospace ExplorationAgency (JAXA)
dc.subject.enLand Parameter Retrieval Algorithm (LPRM)
dc.subject.enSoil Moisture Active Passive (SMAP)
dc.subject.enSoil Moisture and Ocean Salinity (SMOS)-IC
dc.subject.enSoil Moisture (SM)
dc.title.enEvaluation of SMAP, SMOS, and AMSR2 soil moisture products based on distributed ground observation network in cold and arid regions of China
dc.typeArticle de revue
dc.identifier.doi10.1109/jstars.2021.3108432
dc.subject.halSciences de l'environnement
bordeaux.journalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
bordeaux.page8955 - 8970
bordeaux.volume14
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-03353856
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03353856v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=IEEE%20Journal%20of%20Selected%20Topics%20in%20Applied%20Earth%20Observations%20and%20Remote%20Sensing&rft.date=2021&rft.volume=14&rft.spage=8955%20-%208970&rft.epage=8955%20-%208970&rft.eissn=1939-1404&rft.issn=1939-1404&rft.au=WANG,%20Zengyan&CHE,%20Tao&ZHAO,%20Tianjie&DAI,%20Liyun&LI,%20Xiaojun&rft.genre=article


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