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Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method

hal.structure.identifierState Key Laboratory of Earth Surface Processes and Resource Ecology [ESPRE]
dc.contributor.authorLIU, Jin
hal.structure.identifierState Key Laboratory of Earth Surface Processes and Resource Ecology [ESPRE]
dc.contributor.authorCHAI, Linna
hal.structure.identifierHydrology and Remote Sensing Laboratory
dc.contributor.authorDONG, Jianzhi
hal.structure.identifierInstitute of Tibetan Plateau Research P.O. Box 2871, 18 Shuang Qing Road, Beijing 100085
dc.contributor.authorZHENG, Donghai
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorWIGNERON, J.-P.
hal.structure.identifierState Key Laboratory of Earth Surface Processes and Resource Ecology [ESPRE]
dc.contributor.authorLIU, Shaomin
hal.structure.identifierUniversity of Electronic Science and Technology of China [Chengdu] [UESTC]
dc.contributor.authorZHOU, Ji
hal.structure.identifierState Key Laboratory of Earth Surface Processes and Resource Ecology [ESPRE]
dc.contributor.authorXU, Tongren
hal.structure.identifierState Key Laboratory of Earth Surface Processes and Resource Ecology [ESPRE]
dc.contributor.authorYANG, Shiqi
hal.structure.identifierCurtin University
dc.contributor.authorSONG, Yongze
hal.structure.identifier4. Institute of Bio- and Geosciences Agrosphere (IBG-3), Research Center Jülich, Germany
dc.contributor.authorQU, Yuquan
hal.structure.identifierState Key Laboratory of Earth Surface Processes and Resource Ecology [ESPRE]
dc.contributor.authorLU, Zheng
dc.date.accessioned2024-04-08T11:52:17Z
dc.date.available2024-04-08T11:52:17Z
dc.date.issued2021-03
dc.identifier.issn0034-4257
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195475
dc.description.abstractEnSoil moisture (SM) is a fundamental environmental variable for characterizing climate, land surface and atmosphere. In recent years, several SM products have been developed based on remote sensing (RS), land surface model (LSM) or land data assimilation system (LDAS). However, little knowledge is available in understanding spatial patterns of the uncertainty of different SM products and potential regional drivers over the Qinghai-Tibet Plateau (QTP), a complex environment for accurate SM estimation. This paper investigates the sensitivity of the SM uncertainties based on the three-cornered hat (TCH) method and a generalized additive model (GAM) in terms of underlying surface characteristics (sand fraction, soil organic matter, vegetation, land surface temperature, and topography) and near-ground meteorology (precipitation and air temperature) in the third pole environment over the 2015–2018 period. Eleven SM products are involved in this work, including Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity INRA-CESBIO (SMOS-IC), Japan Aerospace Exploration Agency (JAXA), Land Surface Parameter Model (LPRM), Climate Change Initiative - Active/Combined (CCI_A/CCI_C), Global Land Data Assimilation System (GLDAS), European Centre for Medium-Range Weather Forecasts Interim reanalysis (ERA-Interim), Global Land Evaporation Amsterdam Model product a/b (GLEAM_a/GLEAM_b), and Random Forest Soil Moisture (RFSM). Results show that most of the SM products perform well across QTP, while SMOS-IC is strongly affected by radio-frequency interference in this region, JAXA has a relatively higher noise level over QTP, and LPRM has larger relative uncertainties (RUs) in the southeast of QTP. Nonlinear regression analysis demonstrates that variations of RUs in SMOS-IC and JAXA are driven by topography, while LPRM's are mainly controlled by vegetation. In addition, two groups of opposite (positive/negative) effects from topography and vegetation, topography and precipitation, and precipitation and land surface temperature affect the spatial variations of RUs in CCI_A, RFSM, and ERA-Interim, respectively. Meanwhile, more complex relationships are found between multiple surface factors and RUs of different products. In general, the underlying surface factors explain on average 39.41% and 28.34% of the variations in RS and LSM/LDAS SM RUs, respectively. Comparatively, the near-ground meteorology factors have a slightly larger effect on LSM/LDAS products than that on RS products.
dc.language.isoen
dc.publisherElsevier
dc.subject.enSoil moisture
dc.subject.enRemote sensing
dc.subject.enLand surface model
dc.subject.enLand data assimilation system
dc.subject.enUncertainty quantification
dc.subject.enRelative contribution
dc.title.enUncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method
dc.typeArticle de revue
dc.identifier.doi10.1016/j.rse.2020.112225
dc.subject.halSciences de l'environnement
bordeaux.journalRemote Sensing of Environment
bordeaux.page1-20
bordeaux.volume255
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-03165899
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03165899v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing%20of%20Environment&rft.date=2021-03&rft.volume=255&rft.spage=1-20&rft.epage=1-20&rft.eissn=0034-4257&rft.issn=0034-4257&rft.au=LIU,%20Jin&CHAI,%20Linna&DONG,%20Jianzhi&ZHENG,%20Donghai&WIGNERON,%20J.-P.&rft.genre=article


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