Urban heat in Mumbai: From scenario analysis to the mitigation of nighttime thermal footprint

Rapid urbanisation has increased urban land surface temperatures, emerging as a critical environmental challenge in megacities such as Mumbai. Urban nighttime land surface temperature (LST) is associated with the Urban Heat Island (UHI) effect, leading to urban thermal stress, health hazards, and energy usage. Despite extensive research on urban LST, there remains a critical lack of spatially adaptive modelling frameworks capable of effectively capturing fine-scale local variations in environmental and anthropogenic drivers of LST, particularly in complex megacities such as Mumbai. This study bridges this gap by utilising satellite-derived datasets and advanced geospatial modelling techniques, including Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Random Forest (RF), to map the intensity, factors, and temporal dynamics of nighttime LST and UHI in the Mumbai Metropolitan Region (MMR) from 2000 to 2025. Temporal analysis reveals a steady increase in nighttime LST in built-up zones, with LST differentials reaching up to +5.07 °C in the southern and central MMR. Nighttime light emerges as the dominant factor (R 2 = 0.826), while NDVI (vegetation) and wind speed show strong negative correlations (r = −0.243 and r = −0.052). The GWR model outperforms both OLS and RF, achieving an R 2 of 0.9808 and an Adjusted R 2 of 0.9539, offering a more effective tool for capturing local spatial variations in LST. The study suggests innovative mitigation strategies, such as urban greening and reflective surfaces, alongside smart lighting systems, for alleviating UHI impacts. This research provides a valuable framework for addressing the UHI effect, not only in Mumbai but also in other rapidly urbanising cities across the Global South. • Built-up areas, especially in central and southern Mumbai, are UHI hotspots. • Vegetated and water zones provide thermal stability, mitigating UHI effects. • GWR model outperforms OLS and RF, revealing spatial variability in LST factors. • RF model highlights nighttime light, AOD, NDVI, wind speed, and albedo's roles. • A replicable framework for UHI mitigation in cities across the Global South.