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Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Department of Engineering, University of Perugia, via G. Duranti 93, 06125 Perugia, Italy
Department for Earth Observation, Friedrich-Schiller-Universität Jena, Löbdergraben 32, 07743 Jena, Germany
School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA, USA 50011
Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
Key Lab. of Indoor Air Environment Quality Control, School of Environmental Science and Engineering, Tianjin University, Weijin Road 92, Tianjin 300072, China
Global Change Unit, Department of Thermodynamics, Faculty of Physics, University of Valencia, E-46071 Valencia, Spain
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(1), 48;
Received: 4 December 2018 / Revised: 23 December 2018 / Accepted: 24 December 2018 / Published: 29 December 2018
(This article belongs to the Special Issue Urban Heat Island Remote Sensing)
The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling. View Full-Text
Keywords: thermal remote sensing; land surface temperature; urban heat island; urbanization; review; impervious surface; MODIS; Landsat; ASTER; heat waves thermal remote sensing; land surface temperature; urban heat island; urbanization; review; impervious surface; MODIS; Landsat; ASTER; heat waves
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MDPI and ACS Style

Zhou, D.; Xiao, J.; Bonafoni, S.; Berger, C.; Deilami, K.; Zhou, Y.; Frolking, S.; Yao, R.; Qiao, Z.; Sobrino, J.A. Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives. Remote Sens. 2019, 11, 48.

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