气候变化领域集成服务门户(CCPortal): An observation-driven optimization method for continuous estimation of evaporative fraction over large heterogeneous areas

CCPortal

DOI	10.1016/j.rse.2020.111887
	An observation-driven optimization method for continuous estimation of evaporative fraction over large heterogeneous areas
	Zhu W.; Jia S.; Lall U.; Cheng Y.; Gentine P.
发表日期	2020
ISSN	00344257
卷号	247
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. © 2020 Elsevier Inc.
英文关键词	Evaporative fraction; Land surface temperature; Optimization method; Satellite remote sensing; Vegetation index
语种	英语
scopus关键词	Calibration; Cloudy conditions; Continuous monitoring; Evaporative fraction; Optimization method; Repetitive works; Southern great plains; Surface temperatures; Vegetation index; Remote sensing; arid region; clear sky; cloud cover; evaporation; MODIS; optimization; parameterization; vegetation index; Great Plains
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179252
作者单位	Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Columbia Water Center, Columbia University, New York, United States; Department of Earth and Environmental Engineering, Columbia University, New York, United States
推荐引用方式 GB/T 7714	Zhu W.,Jia S.,Lall U.,et al. An observation-driven optimization method for continuous estimation of evaporative fraction over large heterogeneous areas[J],2020,247.
APA	Zhu W.,Jia S.,Lall U.,Cheng Y.,&Gentine P..(2020).An observation-driven optimization method for continuous estimation of evaporative fraction over large heterogeneous areas.Remote Sensing of Environment,247.
MLA	Zhu W.,et al."An observation-driven optimization method for continuous estimation of evaporative fraction over large heterogeneous areas".Remote Sensing of Environment 247(2020).