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Urban heat island and its regional impacts using remotely sensed thermal data – A review of recent developments and methodology

August 18, 2021
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.

Citation Information

Publication Year 2021
Title Urban heat island and its regional impacts using remotely sensed thermal data – A review of recent developments and methodology
DOI 10.3390/land10080867
Authors Hua Shi, George Z. Xian, Roger F. Auch, Kevin Gallo, Qiang Zhou
Publication Type Article
Publication Subtype Journal Article
Series Title Land
Index ID 70224305
Record Source USGS Publications Warehouse
USGS Organization Earth Resources Observation and Science (EROS) Center