Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms

doi:10.1016/j.agrformet.2017.04.011

	Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms
	Yao, Yunjun 1; Liang, Shunlin 1; Li, Xianglan 2; Chen, Jiquan 3; Liu, Shaomin 4; Jia, Kun 1; Zhang, Xiaotong 1; Xiao, Zhiqiang 1; Fisher, Joshua B.5; Mu, Qiaozhen 6
刊名	AGRICULTURAL AND FOREST METEOROLOGY
	2017-08-15
卷号	242 页码:55-74
关键词	Terrestrial evapotranspiration Machine learning methods Bayesian model averaging method Plant functional type
ISSN号	0168-1923
DOI	10.1016/j.agrformet.2017.04.011
通讯作者	Yao, Yunjun(boyyunjun@163.com)
英文摘要	Terrestrial evapotranspiration (ET) for each plant functional type (PFT) is a key variable for linking the energy, water and carbon cycles of the atmosphere, hydrosphere and biosphere. Process-based algorithms have been widely used to estimate global terrestrial ET, yet each ET individual algorithm has exhibited large uncertainties. In this study, the support vector machine (SVM) method was introduced to improve global terrestrial ET estimation by integrating three process-based ET algorithms: MOD16, PT-JPL and SEMI-PM. At 200 FLUXNET flux tower sites, we evaluated the performance of the SVM method and others, including the Bayesian model averaging (BMA) method and the general regression neural networks (GRNNs) method together with three process-based ET algorithms. We found that the SVM method was superior to all other methods we evaluated. The validation results showed that compared with the individual algorithms, the SVM method driven by tower-specific (Modern Era Retrospective Analysis for Research and Applications, MERRA) meteorological data reduced the root mean square error (RMSE) by approximately 0.20 (0.15) mm/day for most forest sites and 0.30 (0.20) mm/day for most crop and grass sites and improved the squared correlation coefficient (R-2) by approximately 0.10 (0.08) (95% confidence) for most flux tower sites. The water balance of basins and the global terrestrial ET calculation analysis also demonstrated that the regional and global estimates of the SVM-merged ET were reliable. The SVM method provides a powerful tool for improving global ET estimation to characterize the long-term spatiotemporal variations of the global terrestrial water budget.
资助项目	CFCAS ; NSERC ; BIOCAP ; Environment Canada ; NRCan ; CarboEuropelP ; FAO-GTOS-TCO ; iLEAPS ; Max Planck Institute for Biogeochemistry ; National Science Foundation ; University of Tuscia ; Universite Laval ; US Department of Energy ; Natural Science Fund of China[41671331] ; National Key Research and Development Program of China[2016YFA0600102] ; National Key Research and Development Program of China[2016YFB0501404]
WOS关键词	SURFACE-ENERGY BALANCE ; REMOTELY-SENSED DATA ; EDDY-COVARIANCE ; RIVER-BASIN ; HEAT-FLUX ; REGRESSION ALGORITHMS ; CLOSURE PROBLEM ; CARBON-DIOXIDE ; BOREAL FOREST ; MODIS
WOS研究方向	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000403988500006
资助机构	CFCAS ; NSERC ; BIOCAP ; Environment Canada ; NRCan ; CarboEuropelP ; FAO-GTOS-TCO ; iLEAPS ; Max Planck Institute for Biogeochemistry ; National Science Foundation ; University of Tuscia ; Universite Laval ; US Department of Energy ; Natural Science Fund of China ; National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/63161]
专题	中国科学院地理科学与资源研究所
通讯作者	Yao, Yunjun
作者单位	1.Beijing Normal Univ, State Key Lab Remote Sensing Sci, Inst Remote Sensing Sci & Engn, Fac Geog Sci, Beijing 100875, Peoples R China 2.Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China 3.Michigan State Univ, CGCEO Geog, E Lansing, MI 48823 USA 4.Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Sch Nat Resources, Fac Geog Sci, Beijing 100875, Peoples R China 5.CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 USA 6.Univ Montana, Numer Terradynam Simulat Grp, Dept Ecosyst & Conservat Sci, Missoula, MT 59812 USA 7.Princeton Univ, Dept Civil & Environm Engn, Princeton, NJ 08544 USA 8.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 9.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 10.Tech Univ Dresden, Inst Hydrol & Meteorol, Chair Meteorol, D-01062 Dresden, Germany
推荐引用方式 GB/T 7714	Yao, Yunjun,Liang, Shunlin,Li, Xianglan,et al. Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms[J]. AGRICULTURAL AND FOREST METEOROLOGY,2017,242:55-74.
APA	Yao, Yunjun.,Liang, Shunlin.,Li, Xianglan.,Chen, Jiquan.,Liu, Shaomin.,...&Roupsard, Olivier.(2017).Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms.AGRICULTURAL AND FOREST METEOROLOGY,242,55-74.
MLA	Yao, Yunjun,et al."Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms".AGRICULTURAL AND FOREST METEOROLOGY 242(2017):55-74.