气候变化领域集成服务门户(CCPortal): Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data

CCPortal

DOI	10.1016/j.rse.2020.111791
	Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data
	Venter Z.S.; Brousse O.; Esau I.; Meier F.
发表日期	2020
ISSN	00344257
卷号	242
英文摘要	The impacts of climate change such as extreme heat waves are exacerbated in cities where most of the world's population live. Quantifying urbanization impacts on ambient air temperatures (Tair) has relevance for human health risk, building energy use efficiency, vector-borne disease control and urban biodiversity. Remote sensing of urban climate has been focused on land surface temperature (LST) due to a scarcity of data on Tair which is usually interpolated at 1 km resolution. We assessed the efficacy of mapping hyperlocal Tair (spatial resolutions of 10–30 m) over Oslo, Norway, by integrating Sentinel, Landsat and LiDAR data with crowd-sourced Tair measurements from 1310 private weather stations during 2018. Using Random Forest regression modelling, we found that annual mean, daily maximum and minimum Tair can be mapped with an average RMSE of 0.52 °C (R2 = 0.5), 1.85 °C (R2 = 0.05) and 1.46 °C (R2 = 0.33), respectively. Mapping accuracy decreased sharply with <250 weather stations (approx. 1 station km−2) and remote sensing data averaged within a 100-500 m buffer zone around each station maximized accuracy. Further, models performed best outside of summer months when the spatial variation in temperatures were low and wind velocities were high. Finally, accuracies were not evenly distributed over space and we found the lowest mapping errors in the local climate zone characterized by compact lowrise buildings which are most relevant to city residents. We conclude that this method is transferable to other cities given there was little difference (0.02 °C RMSE) between models trained on open- (satellite and terrain) vs closed-source (LiDAR) remote sensing data. These maps can provide a complement to and validation of traditional urban canopy models and may assist in identifying hyperlocal hotspots and coldspots of relevance to urban planners. © 2020 The Authors
英文关键词	High resolution; Landsat; Satellite; Sentinel; Urban heat island; Urban planning
语种	英语
scopus关键词	Biodiversity; Climate change; Crowdsourcing; Decision trees; Disease control; Energy efficiency; Health risks; Land surface temperature; Mapping; Optical radar; Random forests; Remote sensing; Satellites; Space optics; Urban planning; Weather information services; Ambient air temperature; High resolution; LANDSAT; Regression modelling; Sentinel; Urban heat island; Urbanization impact; Vector-borne disease; Atmospheric temperature; air temperature; ambient air; buffer zone; climate change; disease control; Landsat; mapping method; regression analysis; remote sensing; spatial variation; urban climate; urbanization; Norway; Oslo [Norway]
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/179345
作者单位	Terrestrial Ecology Section, Norwegian Institute for Nature Research - NINA, Oslo, 0349, Norway; Department of Earth and Environmental Sciences, KU Leuven, Celestijnenlaan 200E, Leuven, 3001, Belgium; Nansen Environmental and Remote Sensing Centre, Thormohlensgt. 47, Bergen, 5006, Norway; Chair of Climatology, Institute of Ecology, Technische Universität Berlin, Rothenburgstraße 12, Berlin, D-12165, Germany
推荐引用方式 GB/T 7714	Venter Z.S.,Brousse O.,Esau I.,et al. Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data[J],2020,242.
APA	Venter Z.S.,Brousse O.,Esau I.,&Meier F..(2020).Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data.Remote Sensing of Environment,242.
MLA	Venter Z.S.,et al."Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data".Remote Sensing of Environment 242(2020).