An observation-driven optimization method for continuous estimation of evaporative fraction over large heterogeneous areas

doi:10.1016/j.rse.2020.111887

	An observation-driven optimization method for continuous estimation of evaporative fraction over large heterogeneous areas
	Zhu, Wenbin 1; Jia, Shaofeng 1; Lall, Upmanu 2,3; Cheng, Yu 3; Gentine, Pierre 3
刊名	REMOTE SENSING OF ENVIRONMENT
	2020-09-15
卷号	247 页码:17
关键词	Evaporative fraction Land surface temperature Vegetation index Optimization method Satellite remote sensing
ISSN号	0034-4257
DOI	10.1016/j.rse.2020.111887
通讯作者	Zhu, Wenbin(zhuwb@igsnrr.ac.cn)
英文摘要	Ground-based evaporative fraction (EF) observations have been used widely for validation purposes in previous remote sensing-based EF models. Few studies have investigated whether such measurements can be utilized for calibration use. In this paper, an observation-driven optimization method is proposed to quantify EF over a large heterogeneous area within the surface temperature-vegetation index framework. It is designed at both daily scale and seasonal scale with MODIS products and in-situ EF observations over the Southern Great Plains in the US. The goal is to search for the optimal dry edge within the allowable range that minimizes the difference between the estimated and observed EF of a given site. Results show that the accuracy produced using only one site for calibration has reached a level comparable to those produced by traditional triangle methods. Compared with the daily-scale optimization method, the seasonal-scale optimization method has not only demonstrated its superiority in accuracy but also held distinctive advantages over the traditional triangle methods. Specifically, the dry edge produced by our optimization method holds true under both clear sky and partially cloudy conditions. This has not only bypassed the repetitive work of previous triangle methods but also made it possible to conduct a continuous monitoring of EF. Besides, the optimization method is characterized by its simplicity in algorithm, stability in accuracy and extensibility in parameterization, which makes it a suitable tool for providing a quick and reasonable estimation of EF over large heterogeneous areas from a limited number of in-situ EF observations.
资助项目	Key Science and Technology Project of Qinghai Province[2019-SF-A4] ; National Natural Science Foundation of China[41701485] ; Basic Research Program of Qinghai Province[2020-ZJ-715] ; Youth Innovation Promotion Association of Chinese Academy of Sciences[2020056]
WOS关键词	SURFACE-ENERGY BALANCE ; SOUTHERN GREAT-PLAINS ; SOIL-MOISTURE ; HEAT-FLUX ; TRIANGLE METHOD ; AIR-TEMPERATURE ; EVAPOTRANSPIRATION ESTIMATION ; REGIONAL EVAPOTRANSPIRATION ; OBTAINABLE VARIABLES ; MODIS
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000549189200006
资助机构	Key Science and Technology Project of Qinghai Province ; National Natural Science Foundation of China ; Basic Research Program of Qinghai Province ; Youth Innovation Promotion Association of Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/158420]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhu, Wenbin
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China 2.Columbia Univ, Columbia Water Ctr, New York, NY USA 3.Columbia Univ, Dept Earth & Environm Engn, New York, NY USA
推荐引用方式 GB/T 7714	Zhu, Wenbin,Jia, Shaofeng,Lall, Upmanu,et al. An observation-driven optimization method for continuous estimation of evaporative fraction over large heterogeneous areas[J]. REMOTE SENSING OF ENVIRONMENT,2020,247:17.
APA	Zhu, Wenbin,Jia, Shaofeng,Lall, Upmanu,Cheng, Yu,&Gentine, Pierre.(2020).An observation-driven optimization method for continuous estimation of evaporative fraction over large heterogeneous areas.REMOTE SENSING OF ENVIRONMENT,247,17.
MLA	Zhu, Wenbin,et al."An observation-driven optimization method for continuous estimation of evaporative fraction over large heterogeneous areas".REMOTE SENSING OF ENVIRONMENT 247(2020):17.