干旱区生态环境知识资源中心(Arid): Estimating surface heat and water vapor fluxes by combining two-source energy balance model and back-propagation neural network

Arid

DOI	10.1016/j.scitotenv.2020.138724
	Estimating surface heat and water vapor fluxes by combining two-source energy balance model and back-propagation neural network
	Feng, Jiaojiao; Wang, Weizhen; Xu, Feinan; Sun, Saiyu
通讯作者	Wang, WZ
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2020
卷号	729
英文摘要	The accurate quantification of surface heat and water vapor fluxes is significantly essential for understanding water balance dynamics. In this study, 15-m spatial resolution turbulent fluxes (H and LE) in the Zhangye oasis situated the middle reaches of the Heihe River Basin (HRB) were estimated by the remote sensing-based twosource energy balance model (TSEB). The TSEB model uses temperature including land surface temperature (LST) and air temperature (Ta) as the main input variable to compute turbulent fluxes but their spatial resolution is rather limited. To overcome this shortcoming, the 15-m spatial resolution LST and Ta were obtained by using the back-propagation neural network (BPNN). The results indicated that the BPNNwas able to obtain finer spatial resolution and LST and Ta; the root mean square error (RMSE) values of LST and Ta are 1.99 K and 0.50 K, respectively. The remotely sensed H and LE predicted by TSEBmodel utilizing the LST and Ta modeled by BPNN. The results showed that H and LE agreed well with the flux observations from multi-set eddy covariance (EC) systems installed at a number of sites and covering all representative land cover types; particularly for the latent heat flux, its estimates produced mean absolute percent errors (MAPE) of 8.76% formaize, 20.17% for vegetable, 29.06% for residential area, and 16.12% for orchard. This study obtained surface heat and water vapor fluxes at finer spatial resolution than the other flux estimates from the remote sensing models that have been used in the Zhangye oasis. The results produced by combining the TSEB model and BPNN can provide more information for drafting reliable sustainable water resource management schemes and improving the irrigation water use efficiency in arid and semi-arid regions. (C) 2020 Elsevier B.V. All rights reserved.
英文关键词	Sensible and latent heat fluxes Finer spatial resolution TSEB model BPNN Heihe River Basin
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000537441700017
WOS关键词	MAPPING DAILY EVAPOTRANSPIRATION ; SOIL RESISTANCE FORMULATIONS ; HEIHE RIVER-BASIN ; VEGETATION INDEX ; EDDY-COVARIANCE ; UPSCALING EVAPOTRANSPIRATION ; MEDITERRANEAN DRYLANDS ; MULTISOURCE DATA ; TEMPERATURE DATA ; MIDDLE REACHES
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324605
作者单位	[Feng, Jiaojiao; Wang, Weizhen; Xu, Feinan; Sun, Saiyu] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Peoples R China; [Feng, Jiaojiao; Xu, Feinan; Sun, Saiyu] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Wang, Weizhen] Chinese Acad Sci, Key Lab Land Surface Proc & Climate Change Cold &, Lanzhou 730000, Peoples R China
推荐引用方式 GB/T 7714	Feng, Jiaojiao,Wang, Weizhen,Xu, Feinan,et al. Estimating surface heat and water vapor fluxes by combining two-source energy balance model and back-propagation neural network[J],2020,729.
APA	Feng, Jiaojiao,Wang, Weizhen,Xu, Feinan,&Sun, Saiyu.(2020).Estimating surface heat and water vapor fluxes by combining two-source energy balance model and back-propagation neural network.SCIENCE OF THE TOTAL ENVIRONMENT,729.
MLA	Feng, Jiaojiao,et al."Estimating surface heat and water vapor fluxes by combining two-source energy balance model and back-propagation neural network".SCIENCE OF THE TOTAL ENVIRONMENT 729(2020).