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

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Yao Yunjun, Liang Shunlin, Li Xianglan, Zhang Yuhu, Chen Jiquan, Jia Kun, Zhang Xiaotong, Fisher Joshua B., Wang Xuanyu, Zhang Lilin, Xu Jia, Shao Changliang, Posse Gabriela, Li Yingnian, Magliulo Vincenzo, Varlagin Andrej, Moors Eddy J., Boike Julia, Macfarlane Craig, Kato Tomomichi, Buchmann Nina, Billesbach D.P., Beringer Jason, Wolf Sebastian, et al. ,
Project ICOS-CH Phase 2
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Original article (peer-reviewed)

Journal Journal of Hydrology
Volume (Issue) 553
Page(s) 508 - 526
Title of proceedings Journal of Hydrology
DOI 10.1016/j.jhydrol.2017.08.013


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.