Urban microclimate are localized climatic conditions within urban areas that are shaped by the buildings, population density, land cover, and vegetation, and that differ significantly from surrounding rural contexts. These localized conditions influence the intensity of microclimate extremes such as elevated temperatures and altered airflow thereby affecting human health, environmental quality, and urban functionality. This study used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to investigate remote sensing technologies employed to assess urban microclimate extremes (UMCE), associated impacts, and the mitigation strategies proposed in the literature, with the objective of informing sustainable urban planning and identifying critical research gaps. This study explored that remote sensing methods were widely used to map Urban Heat Island (UHI) effects, altered wind flow, modified rainfall patterns, hotspots and cool islands in cities and changes were linked with the expansion of built-up areas, compact building designs, and the loss of green spaces. Particularly, collective upsurges in Land Surface Temperature (LST) measured using absolute surface temperature, exacerbated thermal stress, UHI intensity increases were observed in cities compared to rural surroundings. The review identified the integration of green and blue infrastructure as a key mitigation strategy to reduce microclimate variability and offset impacts. Furthermore, trend analysis using remote sensing and machine learning methods are critical tools for monitoring changes and enables targeted interventions. Balanced land use development embedding built-ups with natural land covers, and data-driven monitoring contributes to the UMCE impacts mitigations and support endeavours to sustainable urban development and climate action goals (SDG 11 and 13) . • Remote sensing provides robust, scalable tools for assessing urban microclimate extremes across diverse climatic regions. • Urban heat island intensity is primarily driven by built-up expansion, compact urban form, and loss of vegetation and water bodies. • Land surface temperature derived from thermal satellites remains the dominant indicator of urban microclimate extremes. • Green and blue infrastructures associated with reduction in urban thermal stress and enhance climate resilience. • Integrating multi-sensor remote sensing and machine learning strengthens climate-sensitive urban planning and supports SDGs 11 and 13.
Mengiste et al. (Sun,) studied this question.