000904464 001__ 904464
000904464 005__ 20220224125204.0
000904464 0247_ $$2doi$$a10.1016/j.rse.2020.112225
000904464 0247_ $$2ISSN$$a0034-4257
000904464 0247_ $$2ISSN$$a1879-0704
000904464 0247_ $$2WOS$$aWOS:000619233200001
000904464 037__ $$aFZJ-2021-06034
000904464 082__ $$a550
000904464 1001_ $$0P:(DE-Juel1)180513$$aLiu, Jin$$b0
000904464 245__ $$aUncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method
000904464 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2021
000904464 3367_ $$2DRIVER$$aarticle
000904464 3367_ $$2DataCite$$aOutput Types/Journal article
000904464 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1642756556_26828
000904464 3367_ $$2BibTeX$$aARTICLE
000904464 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000904464 3367_ $$00$$2EndNote$$aJournal Article
000904464 500__ $$aEin Postprint steht leider nicht zur Verfügung
000904464 520__ $$aSoil 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.
000904464 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
000904464 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000904464 7001_ $$0P:(DE-HGF)0$$aChai, Linna$$b1
000904464 7001_ $$0P:(DE-HGF)0$$aDong, Jianzhi$$b2
000904464 7001_ $$0P:(DE-HGF)0$$aZheng, Donghai$$b3
000904464 7001_ $$0P:(DE-HGF)0$$aWigneron, J.-P.$$b4
000904464 7001_ $$0P:(DE-HGF)0$$aLiu, Shaomin$$b5
000904464 7001_ $$0P:(DE-HGF)0$$aZhou, Ji$$b6
000904464 7001_ $$0P:(DE-HGF)0$$aXu, Tongren$$b7
000904464 7001_ $$0P:(DE-HGF)0$$aYang, Shiqi$$b8
000904464 7001_ $$0P:(DE-HGF)0$$aSong, Yongze$$b9
000904464 7001_ $$0P:(DE-Juel1)180577$$aQu, Yuquan$$b10$$ufzj
000904464 7001_ $$0P:(DE-HGF)0$$aLu, Zheng$$b11
000904464 773__ $$0PERI:(DE-600)1498713-2$$a10.1016/j.rse.2020.112225$$gVol. 255, p. 112225 -$$p112225 -$$tRemote sensing of environment$$v255$$x0034-4257$$y2021
000904464 909CO $$ooai:juser.fz-juelich.de:904464$$pVDB
000904464 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180577$$aForschungszentrum Jülich$$b10$$kFZJ
000904464 9131_ $$0G:(DE-HGF)POF4-217$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2173$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vFür eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten$$x0
000904464 9141_ $$y2021
000904464 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2021-02-03$$wger
000904464 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bREMOTE SENS ENVIRON : 2019$$d2021-02-03
000904464 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2021-02-03
000904464 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2021-02-03
000904464 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2021-02-03
000904464 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2021-02-03
000904464 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2021-02-03
000904464 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-02-03
000904464 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2021-02-03
000904464 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2021-02-03
000904464 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2021-02-03
000904464 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-02-03
000904464 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2021-02-03
000904464 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bREMOTE SENS ENVIRON : 2019$$d2021-02-03
000904464 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0
000904464 980__ $$ajournal
000904464 980__ $$aVDB
000904464 980__ $$aI:(DE-Juel1)IBG-3-20101118
000904464 980__ $$aUNRESTRICTED