globalchange  > 气候减缓与适应
DOI: 10.1029/2018JD028447
Scopus记录号: 2-s2.0-85052810341
论文题名:
Evaluating Different Machine Learning Methods for Upscaling Evapotranspiration from Flux Towers to the Regional Scale
作者: Xu T.; Guo Z.; Liu S.; He X.; Meng Y.; Xu Z.; Xia Y.; Xiao J.; Zhang Y.; Ma Y.; Song L.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2018
卷: 123, 期:16
起始页码: 8674
结束页码: 8690
语种: 英语
英文关键词: eddy covariance ; Evapotranspiration ; Large aperture scintillometer ; Machine learning methods ; Upscaling
英文摘要: Evapotranspiration (ET) is a vital variable for land-atmosphere interactions that links surface energy balance, water, and carbon cycles. The in situ techniques can measure ET accurately but the observations have limited spatial and temporal coverage. Modeling approaches have been used to estimate ET at broad spatial and temporal scales, while accurately simulating ET at regional scales remains a major challenge. In this study, we upscale ET from eddy covariance flux tower sites to the regional scale with machine learning algorithms. Five machine learning algorithms are employed for ET upscaling including artificial neural network, Cubist, deep belief network, random forest, and support vector machine. The machine learning methods are trained and tested at 36 flux towers sites (65 site years) across the Heihe River Basin and are then applied to estimate ET for each grid cell (1 km × 1 km) within the watershed and for each day over the period 2012–2016. The artificial neural network, Cubist, random forest, and support vector machine algorithms have almost identical performance in estimating ET and have slightly lower root-mean-square error than deep belief network at the site scale. The random forest algorithm has slightly lower relative uncertainty at the regional scale than other methods based on three-cornered hat method. Additionally, the machine learning methods perform better over densely vegetated conditions than barren land or sparsely vegetated conditions. The regional ET generated from the machine learning approaches captured the spatial and temporal patterns of ET at the regional scale. ©2018. American Geophysical Union. All Rights Reserved.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/113223
Appears in Collections:气候减缓与适应

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作者单位: State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China; I. M. Systems Group at Environmental Modeling Center (EMC), National Centers for Environmental Prediction (NCEP), College Park, MD, United States; Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, United States; Department of Geography, Handan College, Handan, China; School of Geographical Sciences, Southwest University, Chongqing, China

Recommended Citation:
Xu T.,Guo Z.,Liu S.,et al. Evaluating Different Machine Learning Methods for Upscaling Evapotranspiration from Flux Towers to the Regional Scale[J]. Journal of Geophysical Research: Atmospheres,2018-01-01,123(16)
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