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Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China

hal.structure.identifierHohai University
dc.contributor.authorZHENG, Jingyao
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorZHAO, Tianjie
hal.structure.identifierHohai University
dc.contributor.authorLÜ, Haishen
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorSHI, Jiancheng
hal.structure.identifierUSDA-ARS : Agricultural Research Service
dc.contributor.authorCOSH, Michael
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorJI, Dabin
hal.structure.identifierBeijing Normal University [BNU]
dc.contributor.authorJIANG, Lingmei
hal.structure.identifierMinistry of Water Resources of the People's Republic of China
dc.contributor.authorCUI, Qian
hal.structure.identifierTsinghua University [Beijing] [THU]
dc.contributor.authorLU, Hui
hal.structure.identifierTsinghua University [Beijing] [THU]
dc.contributor.authorYANG, Kun
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorJ.-P., Wigneron
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorLI, Xiaojun
hal.structure.identifierUniversiti Teknologi Malaysia [UTM]
dc.contributor.authorKANG, Chuen
hal.structure.identifierHohai University
dc.contributor.authorZHU, Yonghua
hal.structure.identifierNanjing University [NJU]
dc.contributor.authorHU, Lu
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorPENG, Zhiqing
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorZENG, Yelong
hal.structure.identifierHohai University
dc.contributor.authorWANG, Xiaoyi
dc.date.accessioned2024-04-08T11:43:52Z
dc.date.available2024-04-08T11:43:52Z
dc.date.issued2022-01-13
dc.identifier.issn0034-4257
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195126
dc.description.abstractEnA new soil moisture and soil temperature wireless sensor network (the SMN-SDR) consisting of 34 sites was established within the Shandian River Basin in 2018, located in a semi-arid area of northern China. In this study, in situ measurements of the SMN-SDR were used to evaluate 24 different soil moisture datasets grouped according to three categories: (1) single-sensor satellite-based products, (2) multi-sensor merged products, and (3) model-based products. Triple collocation analysis (TCA) was applied to all possible triplets to verify the reliability and robustness of the results. Impacts of different factors on the accuracy of soil moisture products were also investigated, including local acquisition time, physical surface temperature, and vegetation optical depth (VOD). The results reveal that the latest Climate Change Initiative (CCI)-combined product (v06.1, merging extra low-frequency passive microwave data) had the best agreement with in situ measurements from the SMN-SDR, with the lowest ubRMSE ( 0.04 m(3)/m(3)) and highest R (> 0.6). Among all single-sensor retrieved soil moisture products, the Soil Moisture Active Passive (SMAP) products performed best in terms of R (> 0.6) and ubRMSE (close to 0.04 m(3)/m(3)), with the SMAP-MDCA (Modified Dual Channel Algorithm) being slightly better than the baseline SCA-V (Single Channel Algorithm-Vertical polarization). Importantly, the newly developed SMAP-IB product, which does not use auxiliary data, delivered the best bias statistics and higher VOD values compared with the drier SMAP retrievals, suggesting that the low VOD values (underestimated vegetation effects) may be the major factor causing the dry bias of SMAP products in this study area. It was also found that TCA may systematically overestimate the correlation and underestimate the ubRMSE of soil moisture products as compared with ground-based metrics. TCA-based metrics may vary considerably when using different triplets, due to the TCA assumptions being violated even with the most conservative triplets (in this case an active product, a passive product, and a model-based product). Redundant TCA-based metrics from multiple inde-pendent triplets could be averaged to increase the accuracy of final TCA estimates. This study is the first to use in situ measurements from the SMN-SDR to conduct a comprehensive evaluation of commonly used, multi-source soil moisture products. These results are expected to further promote the improvement of satellite-and model-based soil moisture products.
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/
dc.subject.enValidation
dc.subject.enIn situ network
dc.subject.enSatellite-based soil moisture
dc.subject.enModel -based soil moisture
dc.subject.enTriple collocation
dc.subject.enClimate change initiative
dc.subject.enCopernicus climate change service
dc.title.enAssessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China
dc.typeArticle de revue
dc.identifier.doi10.1016/j.rse.2022.112891
dc.subject.halSciences de l'environnement
bordeaux.journalRemote Sensing of Environment
bordeaux.page112891
bordeaux.volume271
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-04114588
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04114588v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing%20of%20Environment&rft.date=2022-01-13&rft.volume=271&rft.spage=112891&rft.epage=112891&rft.eissn=0034-4257&rft.issn=0034-4257&rft.au=ZHENG,%20Jingyao&ZHAO,%20Tianjie&L%C3%9C,%20Haishen&SHI,%20Jiancheng&COSH,%20Michael&rft.genre=article


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