Urban Heat Island and Its Regional Impacts Using Remotely Sensed Thermal Data—A Review of Recent Developments and Methodology

Hua Shi, George Xian, Roger Auch, Kevin Gallo and Qiang Zhou
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Hua Shi: ASRC Federal Data Solutions (AFDS), Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA
George Xian: U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA
Roger Auch: U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA
Kevin Gallo: Center for Satellite Applications and Research, National Oceanic and Atmospheric Administration (NOAA)/NESDIS, College Park, MD 20740, USA
Qiang Zhou: ASRC Federal Data Solutions (AFDS), Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA

Land, 2021, vol. 10, issue 8, 1-30

Abstract: Many novel research algorithms have been developed to analyze urban heat island (UHI) and UHI regional impacts (UHIRIP) with remotely sensed thermal data tables. We present a comprehensive review of some important aspects of UHI and UHIRIP studies that use remotely sensed thermal data, including concepts, datasets, methodologies, and applications. We focus on reviewing progress on multi-sensor image selection, preprocessing, computing, gap filling, image fusion, deep learning, and developing new metrics. This literature review shows that new satellite sensors and valuable methods have been developed for calculating land surface temperature (LST) and UHI intensity, and for assessing UHIRIP. Additionally, some of the limitations of using remotely sensed data to analyze the LST, UHI, and UHI intensity are discussed. Finally, we review a variety of applications in UHI and UHIRIP analyses. The assimilation of time-series remotely sensed data with the application of data fusion, gap filling models, and deep learning using the Google Cloud platform and Google Earth Engine platform also has the potential to improve the estimation accuracy of change patterns of UHI and UHIRIP over long time periods.

Keywords: urban heat island; UHI regional impacts; non-urban areas; remote sensing; thermal band; UHI intensity (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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