气候变化领域集成服务门户(CCPortal): Estimating heat storage in urban areas using multispectral satellite data and machine learning

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DOI	10.1016/j.rse.2020.112125
	Estimating heat storage in urban areas using multispectral satellite data and machine learning
	Hrisko J.; Ramamurthy P.; Gonzalez J.E.
发表日期	2021
ISSN	00344257
卷号	252
英文摘要	A satellite-derived hysteresis model is presented for estimate heat storage in urban areas. Storage heat flux, one of the dominant terms in the urban surface energy budget (USEB), is largely unknown despite its critical relationship to various urban environmental processes. This study introduces a novel technique for quantifying heat storage by relating multispectral satellite radiances and geophysical properties to ground-truth residual heat storage computed with flux instruments. Gradient-boosted regression trees serve as the method of maximizing the relationship between satellite data and flux measurements. Several flux networks are used to train and validate the model over varying land cover types, which strengthens the robustness of the model. The model performs well under variable weather conditions such as cloudy rainy days. In comparison with other studies, the RMSE and MAE values were found to be lower than some ground-to-ground studies, and is one of few satellite-derived methods that computes direct comparison over a range of different land cover types. © 2020 Elsevier Inc.
英文关键词	GBRT; GOES-16; Heat flux; Heat storage; Machine learning; Radiance; Satellite remote sensing; Urban
语种	英语
scopus关键词	Budget control; Heat flux; Heat storage; Hysteresis; Machine learning; Satellites; Trees (mathematics); Boosted regression trees; Environmental process; Flux measurements; Geophysical properties; Hysteresis modeling; Multispectral satellite data; Novel techniques; Storage heat flux; Digital storage; energy budget; energy storage; heat transfer; hysteresis; land cover; machine learning; satellite data; surface energy
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/179083
作者单位	Department of Mechanical Engineering and NOAA-CESSRST Center, City College of New York, New York, NY 10031, United States
推荐引用方式 GB/T 7714	Hrisko J.,Ramamurthy P.,Gonzalez J.E.. Estimating heat storage in urban areas using multispectral satellite data and machine learning[J],2021,252.
APA	Hrisko J.,Ramamurthy P.,&Gonzalez J.E..(2021).Estimating heat storage in urban areas using multispectral satellite data and machine learning.Remote Sensing of Environment,252.
MLA	Hrisko J.,et al."Estimating heat storage in urban areas using multispectral satellite data and machine learning".Remote Sensing of Environment 252(2021).