气候变化领域集成服务门户(CCPortal): Night and day: The influence and relative importance of urban characteristics on remotely sensed land surface temperature

CCPortal

DOI	10.1016/j.rse.2020.111861
	Night and day: The influence and relative importance of urban characteristics on remotely sensed land surface temperature
	Logan T.M.; Zaitchik B.; Guikema S.; Nisbet A.
发表日期	2020
ISSN	00344257
卷号	247
英文摘要	The characteristics of urban land surfaces contribute to the urban heat island, and, in turn, can exacerbate the severity of heat wave impacts. However, the mechanisms and complex interactions in urban areas underlying land surface temperature are still being understood. Understanding these mechanisms is necessary to design strategies that mitigate land temperatures in our cities. Using the recently available night-time moderate-resolution thermal satellite imagery and employing advanced nonlinear statistical models, we seek to answer the question “What is the influence and relative importance of urban characteristics on land surface temperature, during both the day and night?” To answer this question, we analyze urban land surface temperature in four cities across the United States. We devise techniques for training and validating nonlinear statistical models on geostatistical data and use these models to assess the interdependent effects of urban characteristics on urban surface temperature. Our results suggest that vegetation and impervious surfaces are the most important urban characteristics associated with land surface temperature. While this may be expected, this is the first study to quantify this relationship for Landsat-resolution nighttime temperature estimates. Our results also demonstrate the potential for using nonlinear statistical analysis to investigate land surface temperature and its relationships with urban characteristics. Improved understanding of these relationships influencing both night and day land surface temperature will assist planners undertaking climate change adaptation and heat wave mitigation. © 2020 Elsevier Inc.
英文关键词	Convolutional neural network; Geospatial machine learning; Land surface temperature; LandSat; Spatial random forest; Urban canyon
语种	英语
scopus关键词	Atmospheric temperature; Climate change; Satellite imagery; Surface measurement; Surface properties; Climate change adaptation; Geostatistical data; Impervious surface; Moderate resolution; Nighttime temperatures; Urban characteristics; Urban land surface temperature; Urban surface temperature; Land surface temperature; complexity; heat island; heat wave; land surface; mitigation; model validation; nonlinearity; remote sensing; satellite imagery; surface temperature; training; urban area; United States
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/179258
作者单位	Civil and Natural Resources Engineering, University of Canterbury, New Zealand; Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, United States; Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, United States; Menlo ParkCA, United States
推荐引用方式 GB/T 7714	Logan T.M.,Zaitchik B.,Guikema S.,et al. Night and day: The influence and relative importance of urban characteristics on remotely sensed land surface temperature[J],2020,247.
APA	Logan T.M.,Zaitchik B.,Guikema S.,&Nisbet A..(2020).Night and day: The influence and relative importance of urban characteristics on remotely sensed land surface temperature.Remote Sensing of Environment,247.
MLA	Logan T.M.,et al."Night and day: The influence and relative importance of urban characteristics on remotely sensed land surface temperature".Remote Sensing of Environment 247(2020).