气候变化领域集成服务门户(CCPortal): Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method

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DOI	10.1016/j.rse.2020.112225
	Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method
	Liu J.; Chai L.; Dong J.; Zheng D.; Wigneron J.-P.; Liu S.; Zhou J.; Xu T.; Yang S.; Song Y.; Qu Y.; Lu Z.
发表日期	2021
ISSN	00344257
卷号	255
英文摘要	Soil 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. © 2020 Elsevier Inc.
英文关键词	Land data assimilation system; Land surface model; Relative contribution; Remote sensing; Soil moisture; Uncertainty quantification
语种	英语
scopus关键词	Atmospheric temperature; Climate change; Climate models; Decision trees; Integrated circuits; Poles; Precipitation (meteorology); Radio interference; Remote sensing; Soil moisture; Space research; Surface measurement; Surface properties; Topography; Uncertainty analysis; Vegetation; Weather forecasting; European centre for medium-range weather forecasts; Generalized additive model; Japan Aerospace Exploration Agency; Land data assimilation systems; Non-linear regression analysis; Radio frequency interference; Soil moisture active passive (SMAP); Soil moisture ocean salinities; Land surface temperature; data assimilation; land cover; land surface; regression analysis; remote sensing; soil moisture; surface temperature; topography; uncertainty analysis; Amsterdam [North Holland]; China; Japan; Netherlands; North Holland; Qinghai-Xizang Plateau
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178952
作者单位	State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China; Hydrology and Remote Sensing Laboratory, USDA-ARS, Beltsville, MD, United States; National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China; INRAE, UMR1391 ISPA, Villenave d'Ornon, F-33140, France; School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu, China; School of Design and the Built Environment, Curtin University, Perth, Australia; Forschungszentrum Jülich, Institute of Bio- and Geosciences: Agrosphere (IBG-3), Jülich, Germany
推荐引用方式 GB/T 7714	Liu J.,Chai L.,Dong J.,et al. Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method[J],2021,255.
APA	Liu J..,Chai L..,Dong J..,Zheng D..,Wigneron J.-P..,...&Lu Z..(2021).Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method.Remote Sensing of Environment,255.
MLA	Liu J.,et al."Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method".Remote Sensing of Environment 255(2021).