Regional and urban heat island studies in megacities: A systematic analysis of research methodology. Issue 7 (August 2022)

• Record Type: Journal Article
• Authors: Degefu, Mekonnen Amberber; Argaw, Mekuria; Feyisa, Gudina Legese; Degefa, Sileshi
• Is Part Of: Indoor + built environment. Volume 31:Issue 7(2022)
• Page Start: 1775
• Page End: 1786
• Publication Date: 2022-08
• Subjects: Megacities -- Remote sensing -- Reginal heat island -- Urban heat island -- VOS viewer; Indoor air pollution -- Periodicals; Sick building syndrome -- Periodicals; Buildings -- Environmental engineering -- Periodicals; Environmental health -- Periodicals; 613.5
• DOI: 10.1177/1420326X211061491
• Languages: English
• ISSNs: 1420-326X

Abstract

The paper provides a systematic review of satellite-based regional and urban heat island (RHI and UHI) studies in cities and their challenges, from 2010 to the present based on visualizing scientific landscapes (VOS) viewer analysis and Scopus and science database search using a set of standard criteria. The review results show that 52.17% of the studies used Landsat images followed by MODIS (36.65%). Based on VOS viewer analysis author keywords, remote sensing was strongly linked to urban heat island, urban greenspace, and improvise surface, respectively. Regarding, Co-authorship network China, Canada and the United kingdom's authors actively collaborated with different world researchers. The most frequently studied regions and periods of research are China and summer daytime, respectively. A total of 55% of the articles reported the use of a mono-window algorithm for retrieving LST from sensors. On the other hand, remotely sensed UHI studies have been facing a series of challenges, including differences between remote sensing satellite-derived LST and air temperature, impacts of clouds and other factors on LST data, methods to quantify UHI, accuracy assessment and attribution of RHI and UHI. Thus, consideration was given to the understudied cities, the methods to compute RHI and/or UHI intensity, inter-annual variability and modeling in the future.