## 5 Conclusions  
The current investigation examined the spatiotemporal variations in land surface temperature (LST) data obtained from the MODIS sensor aboard Terra satellites, encompassing both daytime and nighttime observations. This study spanned a 23-year duration, commencing in 2000 and extending through 2023, focusing on the 20 largest agglomerations in India and surrounding countries which were selected based on population (>0.5 million). The annual, seasonal, and diurnal variations in the average daytime and nighttime temperatures, their linear trends and total change in 23 years over India and particularly in the selected cities, were analyzed. The main objective of the study was to assess the nighttime and daytime surface urban heat island intensity of the selected cities by computing the difference in LST between urban and non-urban regions. The study highlights the complex diurnal and seasonal characteristics of SUHI in cities. The important findings from the work are listed as follows:  
* The Thar desert and Kutch region in India were the warmest regions identified, with an average nighttime temperature greater than 30 \({}^{\circ}\)C and daytime temperature greater than 50 \({}^{\circ}\)C for the warmest months, April-May-June;
* The nighttime SUHI effect was found to be more conspicuous, positive, and reliable compared with daytime satellite observations, and hence is a more suitable indicator of SUHI effect over cities;
* The annual nighttime temperature trend was observed to be rising in all cities that are expanding, and the highest warming based on trend (statistically significant, 95% CI) was observed in Ahmedabad and Delhi during MJJ (May-June-July) months (3.7 and 3.01 \({}^{\circ}\)C, respectively);
* The nighttime SUHI for AMJ (April-May-June) representing pre-monsoon and onset of monsoon months for the top 10 cities ranged from 0.92 to 2.33 \({}^{\circ}\)C, and from 1.38 to 2.63 \({}^{\circ}\)C for the winter months (DJF, December-January-February);
* Dhaka, Kolkata, Lahore, Surat, Yangon, Lucknow, Faisalabad, Dhanbad, and Columbo showed positive daytime SUHI, while Mumbai, Karachi, Bengaluru, Chennai, Hyderabad, Ahmedabad, Pune Jaipur, and Kanpur showed negative daytime SUHI during pre-monsoon to onset of monsoon months;
* The seasonal characteristics of daytime SUHI in Delhi, Mumbai, Chennai, Ahmedabad, and Kanpur were negative during pre-monsoon to onset of monsoon, while positive during winter months;
* Inland cities and coastal or near coastal cities showed different patterns of average daytime and nighttime LST pattern by monthly breakdown;
* Monthly daytime SUHI of inland cities such as Delhi, Kanpur, Lahore, Lucknow, Kolkata, Faisalabad, and Jaipur showed two peaks, with a dip during the peak of monsoon and winter season;* Monthly daytime SUHI of coastal to near coastal cities such as Mumbai, Bengaluru, Chennai, Hyderabad, Pune, and Coimbatore showed the highest peak during post-monsoon;
* In general, all cities uniformly exhibited higher nighttime mean SUHI during DJF (winter months) than that during AMJ months, which otherwise record the highest nighttime LST;
* The monthly or seasonal total change in nighttime LST based on linear trend (2000-2023) exhibited several hotspots not only around the major cities but also around the tier-2 and 3 cities, that can be attributed to rapid urbanization in India. The complex and different daytime and nighttime SUHI effects across the months in the studied cities are understood to be independent processes with different driving mechanisms. The geographical location of the city seems to have some influence on the daytime and nighttime SUHI patterns as seen from similar patterns of SUHI across months shown by inland and coastal to near coastal cities. Further scope of work could be the use of higher spatial and temporal resolution satellite datasets from other agencies such as ESA, and JAXA, etc., to decipher the anomalies and trends in SUHI effects.