Remote Sensing-Based Attribution of Urban Heat Islands to the Drivers of Heat

被引:1
作者
Guo, Fengxiang [1 ]
Hertel, Daniel [1 ]
Schlink, Uwe [1 ]
Hu, Die [2 ]
Qian, Jiangkang [3 ]
Wu, Wanben [4 ]
机构
[1] UFZ Helmholtz Ctr Environm Res, Dept Urban & Environm Sociol, D-04318 Leipzig, Germany
[2] Peking Univ, Sino French Inst Earth Syst Sci, Coll Urban & Environm Sci, Beijing 100871, Peoples R China
[3] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
[4] Aarhus Univ, Dept Biol, Aarhus, Denmark
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2024年 / 62卷
基金
中国国家自然科学基金;
关键词
Surface morphology; Heating systems; Land surface; Urban areas; Land surface temperature; Thermal pollution; Spatial resolution; Evapotranspiration; Google Earth Engine (GEE); remote sensing; surface energy balance (SEB); urban adaptation; urban heat island (UHI); SEBAL ALGORITHM; EVAPOTRANSPIRATION; CLIMATE; TEMPERATURE; RESOLUTION; INCREASE; FLUXES;
D O I
10.1109/TGRS.2024.3378287
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
As cities grow and develop, more natural landscapes are transformed into heat-absorbing surfaces, further exacerbating urban heat island (UHI) effect. To seek efficient strategies for UHI mitigation, it requires a good knowledge on the driving mechanisms of heat. Based on surface energy balance (SEB), this study decomposed surface UHI (SUHI) in terms of five biophysical drivers (radiation, anthropogenic heat, convection, evapotranspiration, and heat storage) and applied the approach in Beijing using remote sensing images on Google Earth Engine (GEE). The SUHI intensity, calculated by combining the contribution terms, and the observed SUHI through Landsat 8 land surface temperature product are in good agreement, with the root-mean-square error of 0.776 K and the coefficient of determination of 0.947. Besides building morphological blocks, it is the changes of the evapotranspiration term (a function to Bowen ratio, which describes the capacity of urban and rural surface to evaporate water), that controls the spatial variations of SUHI intensity during summer. For instance, in low-rise and high-density regions that exhibit a strong SUHI effect, the above five contribution terms were 0.03, 0.44, -0.74, 1.35, and -0.08 K on average. In comparison to building height, building density strongly affects the SUHI contribution terms. Based on the results, strategies for reducing the Bowen ratio, such as green spaces, cool roofs, and open building layouts, are recommended. The findings and suggestions refer to a particular city and season. Further experiments and research should be carried out for a deeper understanding of the driving mechanism of SUHI.
引用
收藏
页码:1 / 12
页数:12
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