### UHI Effect in India
During summer, SUHI was more pronounced, ranging in a difference of 0.398 to 4.016 \({}^{\circ}\)C between city center and rural/suburban areas, while in winter, it varied between 0.45 and 2.24 \({}^{\circ}\)C [62]. The day/night, seasonal and inter annual variations of surface heat island intensity in Bhopal and Guwahati were analyzed using MODIS LST data from 2003 to 2018, which revealed negative SUHI during summer/daytime in Bhopal, and a positive urban surface heat island in Guwahati. Conclusive indication of a greater nighttime SUHI effect than daytime for Bhopal city, and a greater daytime SUHI effect compared with nighttime in Guwahati, was observed in the study [63]. Global urbanization and anthropogenic activities drive land use changes, LST and intensifying UHI effects in cities such as Kathmandu, Delhi, and Dhaka. Evaluation of UHI effects in these cities revealed that central urban areas exhibit heightened heat zones, particularly in highly built-up regions, emphasizing the need for detailed assessments and mitigation strategies to address rising temperature impacts [64]. The study underscored the urgency of understanding and managing temperature variations in densely populated urban centers. A significant gap in urban thermal environment studies, necessitating attention, was addressed by an investigation of diurnal LST variation and comparison [65]. Novel insights into diurnal LST variation were provided through the utilization of two satellite MODIS data timeframes over 18 years. The examination of seasonal thermal variance in correlation to Ahmedabad's UHI intensity revealed that surface temperature anomalies are lower in rural areas during nighttime and in urban areas during daytime, regardless of season. UHI effects in Bangalore and Hyderabad surpass global and Asian averages but align with trends observed in other Indian cities [66]. The study revealed a localized rise in LST in both cities, likely influenced by the expansion of built-up areas. Hot spot regions, inhabited by approximately 8.3 million people (34.18% of the total population) in the combined urban areas of Bangalore and Hyderabad, were reported to experience the highest UHI effects. This population, facing an LST 2.57 \({}^{\circ}\)C-2.85 \({}^{\circ}\)C higher than the citywide average, is considered vulnerable to these temperature variations. An analysis of the relationship between diurnal LST and diverse land cover variables, including vegetation, urbanization, soil, and water, in Ahmedabad, revealed that the diurnal LST variations are influenced by various land covers, but have a direct relationship with urbanization, and an inverse relationship with vegetation [67]. These correlations differ between day and night, with nighttime correlations considered more significant. The investigation of SUHI intensity from 2001 to 2020 determined that urban Hyderabad exhibited characteristics of a surface urban cool island (SUCI) during the daytime and transformed into SUHI during the nighttime, with an elevated LST concentrated within 0-15 km from the city center [68]. The spatiotemporal variation of air pollutants in Bengaluru city was examined over a five-year period from 2018 to 2022, revealing a stronger relationship between air pollutants, meteorological parameters, and LST during the winter season compared with the summer season [69]. Significant insights into the dynamics of UHI, particularly the SUHI and AUHI effects, their diurnal variations, and the influence of land cover types in a study conducted in Jaipur city revealed that SUHI is more pronounced at night, while AUHI manifests both during the day and night [70]. Less discussion exists on the diurnal, seasonal and monthly variations of both daytime and nighttime LST and SUHI, including for all the major cities in India. This paper fills the gap in the literature by discussing the diurnal, seasonal and annual trends and patterns in both nighttime and daytime LST and SUHI in the 20 most populated cities in India and surrounding countries using long term (2000-2023) MODIS LST datasets.