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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hua Shi & George Xian & Roger Auch & Kevin Gallo & Qiang Zhou, 2021. "Urban Heat Island and Its Regional Impacts Using Remotely Sensed Thermal Data—A Review of Recent Developments and Methodology," Land, MDPI, vol. 10(8), pages 1-30, August.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:8:p:867-:d:616832
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    Cited by:

    1. Abdullah Addas, 2023. "Machine Learning Techniques to Map the Impact of Urban Heat Island: Investigating the City of Jeddah," Land, MDPI, vol. 12(6), pages 1-14, May.

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