Remote Sensing‐Based Assessment of Vegetation and Land Surface Temperature Effects on Nitrogen Dioxide Concentrations in Chennai and Bengaluru Using Google Earth Engine

ABSTRACT Urban air pollution, particularly nitrogen dioxide (NO 2 ), remains a critical environmental and public health concern in rapidly growing cities. This study explores the spatiotemporal patterns of NO 2 concentrations in Chennai and Bengaluru from 2019 to 2023 by integrating satellite‐based datasets and statistical modeling on the Google Earth Engine (GEE) platform. Sentinel‐5P TROPOMI data were used to assess NO 2 levels, Sentinel‐2‐derived NDVI represented vegetation cover, and Landsat 8 imagery provided land surface temperature (LST) estimates. Seasonal trends were analyzed for both summer (March–June) and winter (November–February) periods. Results revealed pronounced seasonal variability, with Chennai exhibiting consistently higher NO 2 concentrations in winter, while Bengaluru displayed more stable or decreasing trends. Notably, NO 2 levels in Chennai rose by 15.4% during summers over the study period, whereas Bengaluru saw a 16.6% decrease. A comparative regression analysis showed that the relationship between NO 2 and vegetation cover (NDVI) strengthened in Chennai during winter ( R 2 = 0.043 in 2023), suggesting reduced green cover may intensify pollutant accumulation. Conversely, Bengaluru showed stronger NO 2 –NDVI correlations during summer ( R 2 = 0.049 in 2023), indicating vegetation's role in pollutant mitigation during active growing seasons. The NO 2 –LST relationship also varied: Chennai experienced the strongest positive correlation in summer 2022 ( R 2 = 0.30), whereas Bengaluru exhibited increasing winter correlations, potentially driven by surface warming and enhanced atmospheric mixing. Although direct meteorological parameters such as rainfall, humidity, wind speed, solar radiation, and visibility were not included in the present analysis, their influence on NO 2 dynamics is acknowledged and warrants future exploration. Overall, the findings underscore the complex, season‐specific interactions among urban heat, vegetation, and air pollution in different metropolitan contexts. These insights support the need for tailored, climate‐responsive pollution control strategies that integrate urban greening, emission reductions, and adaptive planning.