Satellite-Based Spatiotemporal Evaluation of Urban Growth Impacts on Surface Energy Balance and Thermal Conditions in Mumbai, India

Abstract

This study examines the spatiotemporal variations in land use/land cover (LULC), land surface temperature (LST), and surface energy fluxes in Mumbai, India, from 2014 to 2024 using Landsat 8 (OLI/TIRS) data and the SEBAL (Surface Energy Balance Algorithm for Land) model on the Google Earth Engine (GEE) platform. Built-up areas expanded from 18.6% to 30.5%, while natural vegetation declined from 37.8% to 27.7%, indicating rapid urban growth and ecological stress. LST increased by about 2 °C, showing a strong inverse relationship with the Normalized Difference Vegetation Index (NDVI), emphasizing the role of vegetation loss in urban heat amplification. Surface energy balance results revealed a 34.9% increase in net radiation (Rn) and higher sensible heat flux (H) in dense urban zones, while latent heat flux (LE) and evapotranspiration (ET) remained low and vegetation-dependent. Seasonal patterns highlighted elevated Rn and H during summer, with LE peaking in the monsoon. Correlation analysis confirmed a strong positive link between ET and LE (R² = 1.0) and a negative correlation with H (R² = 0.44). Findings suggest that urbanization intensifies thermal stress and alters energy dynamics, stressing the need for vegetation-based climate adaptation strategies in city planning.

Introduction

Rapid urbanization and land cover changes have significantly altered the surface energy balance (SEB), contributing to climate variability and intensifying Urban Heat Island (UHI) effects. These changes disrupt the natural distribution of energy fluxes—net radiation, sensible heat, latent heat, and ground heat—reducing urban climate resilience. Existing studies often lack high-resolution, seasonal, and location-specific analysis, especially in semi-arid Indian cities.

Traditional SEB estimation models like SEBAL and SEBS rely on coarse-resolution satellite data, which is insufficient for capturing detailed intra-urban variability. Medium-resolution data from Landsat 8, combined with cloud platforms like Google Earth Engine (GEE), provide improved spatial detail and enable large-scale, long-term analysis. However, comprehensive multi-year and multi-season SEB studies in Indian urban contexts remain limited.

This study addresses these gaps by conducting a decade-long (2014–2024) spatiotemporal analysis of SEB in Mumbai using Landsat 8 and the SEBAL model within GEE. It estimates key parameters such as land surface temperature (LST), vegetation (NDVI), albedo, emissivity, and energy flux components across summer and monsoon seasons. The research evaluates how urbanization and vegetation influence spatial and temporal energy patterns, contributing to better understanding of urban thermal dynamics and supporting climate-resilient urban planning.

The methodology involves processing Landsat 8 imagery (2014–2023) to derive brightness temperature, NDVI, emissivity, and LST using standard equations, along with cloud masking and atmospheric corrections in GEE. Mumbai, a densely populated and climatically diverse coastal city, serves as the study area due to its varied land use, strong UHI effects, and seasonal contrasts.

Overall, the study highlights the importance of high-resolution, long-term SEB analysis for understanding urban climate behavior and guiding sustainable city development strategies.

Conclusion

The surface energy balance, land cover change, and thermal patterns of the decadal (2014-2024) spatiotemporal analysis show significant anthropogenic influence on the study site due to the rapid pace of urbanization in Mumbai, India. The urban microclimate has been significantly changed by a 11.9% increase (from 18.6% to 30.5%) in a built-up area and a 21.71% decrease in vegetative cover. In correspondence, there was an increase of 2°C realized in Land Surface Temperature (LST) (i.e., 27.41°C to 29.18°C), which translated to a significant urban heat island effect. An improvement in net radiation (Rn) of 34.94% (339.71W/m² to 458.42W/m²) occurred as a result of decreasing albedo and an increase in impervious surfaces. The most significant energetic factor that dominated was sensible heat flux (H), which still amounted to more than 220 W/m², especially in the years with a decreased vegetation cover and an increase in surface temperatures, like 2017. Conversely, latent heat flux (LE) and evapotranspiration (ET) were subject to less variability and consistently lower values (LE: ~50–100 W/m2), particularly in the summer, due to the cooling effect of urban expansion being counteracted by evaporation. There was a significant positive relationship between the Rn and the LST (R² = 0.335) and between LE and ET (R² = 1), while H had an inversely proportional relationship with LE (R² = 0.441), which indicates the moderating effects of vegetation in surface energy partitioning. The results confirm that reduction in vegetative cover, increase in imperviousness, and altered surface flux dynamics are of primary concern in compensating for urban thermal stress. Thus, there is a need to strategically increase green infrastructure and urban climate resilient planning to replenish the surface energy budget and enhance urban resiliency in semi-arid environments. Future studies should extend this research by confirming the findings through ground-based observations at particular sites and by applying this remote sensing-based SEBAL methodology to other cities with comparable semi-arid climates and urban development challenges.

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Copyright © 2026 Mahi Patel, Ajay Patel, Hetkumar Parmar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.