The rapid urbanization of Jaipur has profoundly altered its urban climate, driven by anthropogenic heat flux (A_F) and shifts in surface energy dynamics. This study leverages remote sensing techniques, utilizing Landsat data, to quantify A_F and assess its influence on the city’s climate. The findings reveal a striking paradox; despite a significant rise in A_F from 127.31 W/m² in 1993 to 201.82 W/m² in 2020, Jaipur exhibits an anomalous urban cool island (UCI) effect during the daytime. In this phenomenon, surrounding fallow lands experience higher land surface temperatures (LSTs) than the urban core, defying the typical urban heat island (UHI) effect observed in most cities worldwide. This paradox is especially pronounced in semi-arid urban environments, where factors such as limited vegetation, arid conditions, and water scarcity intricately shape peculiar thermal behaviour. This study further highlights the role of urban expansion, with built-up areas growing from 11.95% in 1993 to 19% in 2020, intensifying A_F. Notably, the latent heat flux was highest in vegetated areas, significantly reducing LSTs by facilitating evapotranspiration. Daytime surface temperatures have surged significantly, with temperatures ranging from 26–46.9 °C in 1993 to 31–56.5 °C in 2020, indicating an overall increase in surface heat intensity. Despite these increases, the UCI effect remains observable, further illustrating the cooling potential of urban vegetation. This study offers novel insights into the intricate dynamics of urban heat in semi-arid cities, providing refined perspectives on urban heat mitigation strategies and climate adaptation, with implications for future sustainable urban planning and environmental management.

Assessment of urban growth is considered an important study for urban areas. The growth patterns and dynamics play a pivotal role in defining urban areas. Spatial metrics can prove to be instrumental in analysing these dynamics. The study focuses on categorising urban growth and quantifying the growth compactness and speed of Bengaluru and Karnataka using built-up obtained from Global Human Settlement Layer and Nighttime Lights data respectively. The evolution of Bengaluru and the skewed development in Karnataka are also observed. Landscape Expansion Index (LEI) is used to identify growth categories showing that 37.75% of Bengaluru's growth was as edge expansion. Area-weighted Mean Expansion Index, Urban Expansion Intensity Index and Shannon’s entropy are used to quantify growth compactness, speed and entropy respectively. The city has more growth compactness and speed compared to any other settlement in Karnataka. These urban growth dynamics shall help decision-makers in making data-driven decisions for urbanscape.