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

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Bibtex

@article{7005d4b2a9884cd18176e4c0f0ad9a7b,
title = "Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method",
abstract = "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/m2 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.",
keywords = "Eddy covariance, Fusion method, High-resolution products, Landsat data, Terrestrial evapotranspiration",
author = "Yunjun Yao and Shunlin Liang and Xianglan Li and Yuhu Zhang and Jiquan Chen and Kun Jia and Xiaotong Zhang and Fisher, {Joshua B.} and Xuanyu Wang and Lilin Zhang and Jia Xu and Changliang Shao and Gabriela Posse and Yingnian Li and Vincenzo Magliulo and Andrej Varlagin and Moors, {Eddy J.} and Julia Boike and Craig Macfarlane and Tomomichi Kato and Nina Buchmann and Billesbach, {D. P.} and Jason Beringer and Sebastian Wolf and Papuga, {Shirley A.} and Georg Wohlfahrt and Leonardo Montagnani and Jonas Ard{\"o} and Eug{\'e}nie Paul-Limoges and Carmen Emmel and Lukas H{\"o}rtnagl and Torsten Sachs and Carsten Gruening and Beniamino Gioli and Ana L{\'o}pez-Ballesteros and Rainer Steinbrecher and Bert Gielen",
year = "2017",
month = "10",
day = "1",
doi = "10.1016/j.jhydrol.2017.08.013",
language = "English",
volume = "553",
pages = "508--526",
journal = "Journal of Hydrology",
issn = "0022-1694",
publisher = "Elsevier",

}