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

REMOTE SENSING OF ENVIRONMENT

卷:247

Article

Zhu, Wenbin¹  Jia, Shaofeng¹  Lall, Upmanu^2,3  Cheng, Yu³  Gentine, Pierre³ 

[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

关键词: Evaporative fraction;    Land surface temperature;    Vegetation index;    Optimization method;    Satellite remote sensing;   

DOI  :  10.1016/j.rse.2020.111887

来源: Elsevier

【 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.

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