气候变化领域集成服务门户(CCPortal): Urban air temperature model using GOES-16 LST and a diurnal regressive neural network algorithm

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DOI	10.1016/j.rse.2019.111495
	Urban air temperature model using GOES-16 LST and a diurnal regressive neural network algorithm
	Hrisko J.; Ramamurthy P.; Yu Y.; Yu P.; Melecio-Vázquez D.
发表日期	2020
ISSN	00344257
卷号	237
英文摘要	An urban air temperature model is presented using the enterprise GOES-16 land surface temperature product. The model is constructed by fitting the difference between ground-truth air temperature data against satellite LST using a Gaussian function. A time-match algorithm aligns the ground and satellite measurements within 5-min of one another, and the resulting matched values are compared over ten months to investigate their correlation. Land cover, latitude, longitude, local time, and elevation are input to a regressive neural network to fit each unique GOES-16 pixel according to ground-based properties. Over 150 ground stations and satellite pixels throughout the continental U.S. are used near urban areas to construct the diurnal Gaussian relationship and approximate air temperature. Statistics from a five month validation period generates an RMSE of 2.6 K, a bias of 0.8 K, and R2 of 0.86, which are in strong competition with other studies at lower resolution, less geographic integration, and less temporal resolvability. The algorithm also produced strong spatial correlations with a high resolution numerical model, resulting in a mean RMSE value of 2.1 K for nearly 7000 pixels. The overall presentation of this model aims to simplify the calculation of air temperature from satellite LST and create a successful model that performs well in heterogeneous environments. The improvement of urban air temperature calculations will also result in improved satellite land surface products such as relative humidity and heat index. © 2019 Elsevier Inc.
英文关键词	Air temperature; Air temperature model; GOES-16; LST; Neural network; Regression; Satellite remote sensing
语种	英语
scopus关键词	Land surface temperature; Neural networks; Pixels; Remote sensing; Satellites; Surface measurement; Air temperature; GOES-16; Heterogeneous environments; High-resolution numerical models; Neural network algorithm; Regression; Satellite measurements; Satellite remote sensing; Atmospheric temperature; air temperature; algorithm; artificial neural network; atmospheric modeling; diurnal variation; Gaussian method; GOES; land surface; pixel; regression analysis; satellite data; surface temperature; urban area
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/179529
作者单位	Department of Mechanical Engineering and NOAA-CESSRST center, City College of New York, New York, NY 10031, United States; Center for Satellite Applications and Research, NOAA/NESDIS, College Park, MD 20740, United States; Earth System Science Interdisciplinary Center/Cooperative Institute for Climate and Satellites-Maryland, University of Maryland, College Park, MD 20740, United States
推荐引用方式 GB/T 7714	Hrisko J.,Ramamurthy P.,Yu Y.,et al. Urban air temperature model using GOES-16 LST and a diurnal regressive neural network algorithm[J],2020,237.
APA	Hrisko J.,Ramamurthy P.,Yu Y.,Yu P.,&Melecio-Vázquez D..(2020).Urban air temperature model using GOES-16 LST and a diurnal regressive neural network algorithm.Remote Sensing of Environment,237.
MLA	Hrisko J.,et al."Urban air temperature model using GOES-16 LST and a diurnal regressive neural network algorithm".Remote Sensing of Environment 237(2020).