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

Yao, Yunjun; Liang, Shunlin; Li, Xianglan; Zhang, Yuhu; Chen, Jiquan; Jia, Kun; Zhang, Xiaotong; Fisher, Joshua B.; Wang, Xuanyu and Zhang, Lilin, et al. (2017) In Journal of Hydrology 553. p.508-526
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... (More)

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.

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@article{7005d4b2-a988-4cd1-8176-e4c0f0ad9a7b,
  abstract     = {<p>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<sup>2</sup> 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.</p>},
  author       = {Yao, Yunjun and Liang, Shunlin and Li, Xianglan and Zhang, Yuhu and Chen, Jiquan and Jia, Kun and Zhang, Xiaotong and Fisher, Joshua B. and Wang, Xuanyu and Zhang, Lilin and Xu, Jia and Shao, Changliang and Posse, Gabriela and Li, Yingnian and Magliulo, Vincenzo and Varlagin, Andrej and Moors, Eddy J. and Boike, Julia and Macfarlane, Craig and Kato, Tomomichi and Buchmann, Nina and Billesbach, D. P. and Beringer, Jason and Wolf, Sebastian and Papuga, Shirley A. and Wohlfahrt, Georg and Montagnani, Leonardo and Ardö, Jonas and Paul-Limoges, Eugénie and Emmel, Carmen and Hörtnagl, Lukas and Sachs, Torsten and Gruening, Carsten and Gioli, Beniamino and López-Ballesteros, Ana and Steinbrecher, Rainer and Gielen, Bert},
  issn         = {0022-1694},
  keyword      = {Eddy covariance,Fusion method,High-resolution products,Landsat data,Terrestrial evapotranspiration},
  language     = {eng},
  month        = {10},
  pages        = {508--526},
  publisher    = {Elsevier},
  series       = {Journal of Hydrology},
  title        = {Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method},
  url          = {http://dx.doi.org/10.1016/j.jhydrol.2017.08.013},
  volume       = {553},
  year         = {2017},
}