Comparative Remote Sensing Analysis of Urban Heat Islands in Contrasting Climates

Abstract. The formation and intensity of Urban Heat Islands (UHIs) are significantly influenced by the climatic conditions of different regions, making comparative studies across varying climates essential for understanding urban thermal behavior. The objective is to assess Land Surface Temperature (LST) dynamics and UHI patterns using multi-source remote sensing data. Landsat 8 OLI/TIRS imagery from July 2022 was used to derive LST using the Single Channel method and spectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Infrared Index (NDII), and Normalized Difference Impervious Surface Index (NDISI), while MODIS LST products (MOD11A2) provided diurnal and nocturnal LST as well as long-term trends (2000–2020). The findings indicated that the UHI phenomenon in both cities predominantly occurs at night, with the highest and lowest LST in Hamadan being 23.31°C and 15.61°C, respectively, while in Mashhad the maximum and minimum temperatures were 26.35°C and 19.21°C, respectively, with no appreciable thermal variations observed during the day. LST-derived thermal hotspots were primarily in desert regions, industrial zones, and newly urbanized regions with little vegetation cover. The 20-year MODIS time series indicated a warming trend consistent with urban expansion in the northwestern part of Mashhad and the eastern to southeastern areas of Hamadan. Regression analyses demonstrated a strong correlation between satellite-derived LST and meteorological data, with R² values of 0.90 for Mashhad and 0.94 for Hamadan. Multiple regression analysis showed that in Mashhad, LST was negatively correlated with NDVI (-0.25) and positively correlated with NDBI (+0.25), and in Hamadan, NDISI had the maximum positive correlation (+0.48). These results highlight the role of urban form and land cover in shaping thermal behavior across varying climates, providing insights for climate-responsive urban planning.