Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method

doi:10.1016/j.jhydrol.2017.08.013

NWIPB OpenIR

	Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method
	Yao, Yunjun 1; Liang, Shunlin 1; Li, Xianglan 2; Zhang, Yuhu 3; Chen, Jiquan 4; Jia, Kun 1; Zhang, Xiaotong 1; Fisher, Joshua B.5; Wang, Xuanyu 1; Zhang, Lilin 1; Xu, Jia 1; Shao, Changliang 4; Posse, Gabriela 6; Li, Yingnian7 ; Magliulo, Vincenzo 8; Varlagin, Andrej 9; Moors, Eddy J.10; Boike, Julia 11; Macfarlane, Craig 12; Kato, Tomomichi 13; Buchmann, Nina 14; Billesbach, D. P.15,16; Beringer, Jason 17; Wolf, Sebastian 14; Papuga, Shirley A.18; Wohlfahrt, Georg 19; Montagnani, Leonardo 20; Ardo, Jonas 21; Paul-Limoges, Eugenie 14; Emmel, Carmen 14; Hortnagl, Lukas 14; Sachs, Torsten 22; Gruening, Carsten 23; Gioli, Beniamino 24; Lopez-Ballesteros, Ana 25; Steinbrecher, Rainer 26; Gielen, Bert 27
	2017-10-01
发表期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
卷号	553 页码:508-526
文章类型	Article
摘要	Estimation of high-resolution terrestrial evapotranspiration (ET) from Landsat data is important in many climatic, hydrologic, and agricultural applications, as it can help bridging the gap between existing coarse-resolution ET products and point-based field measurements. However, there is large uncertainty among existing ET products from Landsat that limit their application. This study presents a simple Taylor skill fusion (STS) method that merges five Landsat-based ET products and directly measured ET from eddy covariance (EC) to improve the global estimation of terrestrial ET. The STS method uses a weighted average of the individual ET products and weights are determined by their Taylor skill scores (S). The validation with site-scale measurements at 206 EC flux towers showed large differences and uncertainties among the five ET products. The merged ET product exhibited the best performance with a decrease in the averaged root-mean-square error (RMSE) by 2-5 W/m(2) when compared to the individual products. To evaluate the reliability of the STS method at the regional scale, the weights of the STS method for these five ET products were determined using EC ground-measurements. An example of regional ET mapping demonstrates that the STS-merged ET can effectively integrate the individual Landsat ET products. Our proposed method provides an improved high-resolution ET product for identifying agricultural crop water consumption and providing a diagnostic assessment for global land surface models. (C) 2017 Elsevier B.V. All rights reserved.
关键词	Terrestrial Evapotranspiration Eddy Covariance Fusion Method Landsat Data High-resolution Products
WOS标题词	Science & Technology ; Technology ; Physical Sciences
DOI	10.1016/j.jhydrol.2017.08.013
关键词[WOS]	LATENT-HEAT FLUX ; LEAF-AREA INDEX ; REMOTE-SENSING OBSERVATIONS ; BALANCE CLOSURE PROBLEM ; ISLSCP-II DATA ; EDDY-COVARIANCE ; SURFACE-ENERGY ; SATELLITE-OBSERVATIONS ; TIBETAN PLATEAU ; CARBON-DIOXIDE
收录类别	SCI
语种	英语
项目资助者	AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program)(DE-FG02-04ER63917 ; CFCAS ; NSERC ; BIOCAP ; Environment Canada ; NRCan ; GreenGrass ; KoFlux ; LBA ; NECC ; OzFlux ; Swiss FluxNet ; TCOS-Siberia ; USCCC ; CarboEuropeIP ; FAO-GTOS-TCO ; iLEAPS ; Max Planck Institute for Biogeochemistry ; National Science Foundation ; University of Tuscia ; University Laval ; US Department of Energy ; Natural Science Fund of China(41671331) ; National Key Research and Development Program of China(2016YFA0600102) ; National Science Foundation Division of Earth Sciences Award(1255013) ; NASA Science Utilization of the Soil Moisture Active Passive Mission (SUSMAP) program ; DE-FG02-04ER63911)
WOS研究方向	Engineering ; Geology ; Water Resources
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS记录号	WOS:000412612700040
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：40[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://210.75.249.4/handle/363003/9819
专题	中国科学院西北高原生物研究所
通讯作者	Yao, Yunjun
作者单位	1.Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China 2.Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China 3.Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China 4.Michigan State Univ, CGCEO Geog, E Lansing, MI 48823 USA 5.CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 USA 6.Natl Inst Agr Technol CIRN INTA, Climate & Water Inst, Res Ctr Nat Resources, Hurlingham, Argentina 7.Chinese Acad Sci, Northwest Inst Plateau Biol, Xining 810001, Qinghai, Peoples R China 8.CNR, Inst Mediterranean Forest & Agr Syst, Via Patacca 85, I-80040 Naples, Italy 9.Russian Acad Sci, AN Severtsov Inst Ecol & Evolut, Moscow 119071, Russia 10.Wageningen Univ & Res, Wageningen Environm Res, Wageningen, Netherlands 11.Alfred Wegener Inst Polar & Marine Res, Telegrafenberg A43, D-14473 Potsdam, Germany 12.CSIRO Land & Water, Floreat, WA 6014, Australia 13.Hokkaido Univ, Res Fac Agr, Sapporo, Hokkaido 0608589, Japan 14.ETH, Dept Environm Syst Sci, Zurich, Switzerland 15.Univ Nebraska, Dept Biol Syst Engn, Lincoln, NE 68583 USA 16.Univ Nebraska, Sch Nat Resources, Lincoln, NE 68583 USA 17.Univ Western Australia, Sch Agr & Environm, Crawley, WA 6020, Australia 18.Univ Arizona, Sch Nat Resources & Environm, Tucson, AZ 85721 USA 19.Univ Innsbruck, Inst Ecol, A-6020 Innsbruck, Austria 20.Free Univ Bolzano, Fac Sci & Technol, Piazza Univ 5, Bolzano, Italy 21.Lund Univ, Phys Geog & Ecosyst Sci, Solvegatan 12, SE-22362 Lund, Sweden 22.GFZ German Res Ctr Geosci, Sect Remote Sensing, D-14473 Potsdam, Germany 23.European Commiss, Joint Res Ctr, Ispra, Italy 24.CNR, Inst Biometeorol, Via Caproni 8, I-50145 Florence, Italy 25.Univ Granada, Fac Sci, Dept Ecol, E-18071 Granada, Spain 26.KIT, Inst Meteorol & Climate Res IMK IFU, D-82467 Garmisch Partenkirchen, Germany 27.Univ Antwerp, Dept Biol, Ctr Excellence PLECO, Univ Pl 1, B-2610 Antwerp, Belgium
推荐引用方式 GB/T 7714	Yao, Yunjun,Liang, Shunlin,Li, Xianglan,et al. Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method[J]. JOURNAL OF HYDROLOGY,2017,553:508-526.
APA	Yao, Yunjun.,Liang, Shunlin.,Li, Xianglan.,Zhang, Yuhu.,Chen, Jiquan.,...&Gielen, Bert.(2017).Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method.JOURNAL OF HYDROLOGY,553,508-526.
MLA	Yao, Yunjun,et al."Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method".JOURNAL OF HYDROLOGY 553(2017):508-526.