TY - JOUR
T1 - Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method
AU - Yao, Yunjun
AU - Liang, Shunlin
AU - Li, Xianglan
AU - Zhang, Yuhu
AU - Chen, Jiquan
AU - Jia, Kun
AU - Zhang, Xiaotong
AU - Fisher, Joshua B.
AU - Wang, Xuanyu
AU - Zhang, Lilin
AU - Xu, Jia
AU - Shao, Changliang
AU - Posse, Gabriela
AU - Li, Yingnian
AU - Magliulo, Vincenzo
AU - Varlagin, Andrej
AU - Moors, Eddy J.
AU - Boike, Julia
AU - Macfarlane, Craig
AU - Kato, Tomomichi
AU - Buchmann, Nina
AU - Billesbach, D. P.
AU - Beringer, Jason
AU - Wolf, Sebastian
AU - Papuga, Shirley A.
AU - Wohlfahrt, Georg
AU - Montagnani, Leonardo
AU - Ardö, Jonas
AU - Paul-Limoges, Eugénie
AU - Emmel, Carmen
AU - Hörtnagl, Lukas
AU - Sachs, Torsten
AU - Gruening, Carsten
AU - Gioli, Beniamino
AU - López-Ballesteros, Ana
AU - Steinbrecher, Rainer
AU - Gielen, Bert
N1 - Funding Information:
This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, Swiss FluxNet, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval, Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California-Berkeley and the University of Virginia. Other ground-measured data were obtained from the GAME AAN ( http://aan.suiri.tsukuba.ac.jp/), the Arid/Semi-arid experimental observation synergy and integration of northern China ( http://observation.tea.ac.cn/ ), and the water experiments of Environmental and Ecological Science Data Center for West China ( http://westdc.westgis.ac.cn/water ). This work was partially supported by the Natural Science Fund of China (No. 41671331), the National Key Research and Development Program of China (No.2016YFA0600102) and the National Science Foundation Division of Earth Sciences Award #1255013 (S.A.P.). JBF contributed to this manuscript from the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration; he was supported, in part, by the NASA Science Utilization of the Soil Moisture Active-Passive Mission (SUSMAP) program. Appendix A
Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/10
Y1 - 2017/10
N2 - 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.
AB - 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.
KW - Eddy covariance
KW - Fusion method
KW - High-resolution products
KW - Landsat data
KW - Terrestrial evapotranspiration
UR - http://www.scopus.com/inward/record.url?scp=85028504311&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028504311&partnerID=8YFLogxK
U2 - 10.1016/j.jhydrol.2017.08.013
DO - 10.1016/j.jhydrol.2017.08.013
M3 - Article
AN - SCOPUS:85028504311
VL - 553
SP - 508
EP - 526
JO - Journal of Hydrology
JF - Journal of Hydrology
SN - 0022-1694
ER -