气候变化领域集成服务门户(CCPortal): Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing; machine learning and land surface modeling

CCPortal

DOI	10.5194/hess-24-1485-2020
	Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing; machine learning and land surface modeling
	Pan S.; Pan N.; Tian H.; Friedlingstein P.; Sitch S.; Shi H.; Arora V.K.; Haverd V.; Jain A.K.; Kato E.; Lienert S.; Lombardozzi D.; Nabel J.E.M.S.; Ottlé C.; Poulter B.; Zaehle S.; Running S.W.
发表日期	2020
ISSN	1027-5606
起始页码	1485
结束页码	1509
卷号	24 期号:3
英文摘要	Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed the basic theory and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensingbased physical models, machine-learning algorithms and land surface models (LSMs). We then utilized 4 remotesensing-based physical models, 2 machine-learning algorithms and 14 LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the ensemble means of annual global terrestrial ET estimated by these three categories of approaches agreed well, with values ranging from 589.6 mm yr-1 (6.56 × 104 km3 yr-1) to 617.1 mm yr-1 (6.87 × 104 km3 yr-1). For the period from 1982 to 2011, both the ensembles of remote-sensing-based physical models and machine-learning algorithms suggested increasing trends in global terrestrial ET (0.62 mm yr-2 with a significance level of p < 0.05 and 0.38 mm yr-2 with a significance level of p < 0.05, respectively). In contrast, the ensemble mean of the LSMs showed no statistically significant change (0.23 mm yr-2, p > 0.05), although many of the individual LSMs reproduced an increasing trend. Nevertheless, all 20 models used in this study showed that anthropogenic Earth greening had a positive role in increasing terrestrial ET. The concurrent small interannual variability, i.e., relative stability, found in all estimates of global terrestrial ET, suggests that a potential planetary boundary exists in regulating global terrestrial ET, with the value of this boundary being around 600 mm yr-1. Uncertainties among approaches were identified in specific regions, particularly in the Amazon Basin and arid/semiarid regions. Improvements in parameterizing water stress and canopy dynamics, the utilization of new available satellite retrievals and deep-learning methods, and model-data fusion will advance our predictive understanding of global terrestrial ET. © Author(s) 2020.
语种	英语
scopus关键词	Data fusion; Deep learning; Evapotranspiration; Learning systems; Remote sensing; Surface measurement; Interannual variability; Land surface modeling; Land surface models; Relative stabilities; Significance levels; Spatial and temporal variation; State-of-the-art approach; Terrestrial evapotranspiration; Learning algorithms; algorithm; anthropogenic effect; evapotranspiration; machine learning; remote sensing; spatiotemporal analysis; terrestrial environment; water stress; Amazon Basin
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159459
作者单位	Pan, S., International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36832, United States; Pan, N., International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36832, United States, State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; Tian, H., International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36832, United States; Friedlingstein, P., College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF, United Kingdom; Sitch, S., College of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4RJ, United Kingdom; Shi, H., International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn Universi...
推荐引用方式 GB/T 7714	Pan S.,Pan N.,Tian H.,et al. Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing; machine learning and land surface modeling[J],2020,24(3).
APA	Pan S..,Pan N..,Tian H..,Friedlingstein P..,Sitch S..,...&Running S.W..(2020).Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing; machine learning and land surface modeling.Hydrology and Earth System Sciences,24(3).
MLA	Pan S.,et al."Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing; machine learning and land surface modeling".Hydrology and Earth System Sciences 24.3(2020).