Assessing Urban Heat Dynamics Using Satellite-Derived Land Surface Temperature: A Case Study of Chennai, India

Abstract

Coastal cities are increasingly facing thermal stress due to rapid urban growth, land-use change, and climate variability. Chennai, a major coastal city in southern India, exemplifies this challenge, where extensive urban development coexists with ecologically sensitive peri-wetlands and a dynamic land–sea boundary. This study examines the spatial and temporal variation of land surface temperature (LST) across distinct land-use categories in Chennai using satellite-derived data. LST was obtained from the MODIS MOD11A2 Version 6.1 product (8-day composite, 1 km spatial resolution) through the AppEEARS platform for four representative locations — urban, coastal, peri-wetland, and industrial — over an 11-year period (2015–2025). Coastal observations were affected by persistent mixed land–water pixel contamination (0% valid retrievals) and were therefore excluded from the quantitative analysis; this limitation and possible mitigation strategies are discussed explicitly. Statistical, diurnal, and seasonal analyses of the remaining three classes reveal a consistent Surface Urban Heat Island (SUHI) effect, with urban areas exhibiting the highest daytime and night-time temperatures and the lowest diurnal temperature range (DTR). Relative to earlier Chennai UHI studies, this work extends the observational record to 2025, explicitly incorporates a peri-wetland comparison class, and combines diurnal and seasonal decomposition within a single reproducible, low-code AppEEARS-based workflow. The findings can inform sustainable urban planning, climate adaptation, and ecosystem conservation in coastal urban settings.

Introduction

This study investigates the urban heat island (UHI) effect in Chennai, India, by analyzing land surface temperature (LST) across different land-use categories—urban, industrial, and peri-wetland—using an 11-year MODIS satellite dataset (2015–2025) obtained through the AppEEARS platform. The research aims to understand long-term spatial, seasonal, and diurnal variations in surface temperature to support sustainable urban planning.

Urban heat islands occur because natural vegetation is replaced by impervious surfaces such as buildings and roads, which absorb and retain heat. Additional factors such as reduced vegetation and anthropogenic heat emissions further increase urban temperatures. Chennai’s coastal location also influences its thermal patterns through sea breezes, humidity, and monsoon systems.

Unlike previous studies, this research extends the observation period to 2025, includes a peri-wetland category as a natural reference, and employs a reproducible workflow using freely available AppEEARS data. The literature consistently shows that rapid urbanization, industrialization, land-use changes, and declining vegetation have intensified Chennai’s UHI, while coastal influences also play an important role in shaping temperature patterns.

The study selected four sampling locations representing urban, industrial, peri-wetland, and coastal environments. MODIS LST data (8-day composites at 1 km resolution) were collected for both daytime and nighttime temperatures. Data quality was assessed using MODIS quality-control flags. Valid data were obtained for the urban, industrial, and peri-wetland sites, while the coastal site produced no usable observations because the 1 km MODIS pixel included both land and water, causing persistent mixed-pixel contamination. Consequently, the coastal location was excluded from statistical analysis.

Statistical analyses included descriptive temperature statistics, diurnal temperature range (DTR), and monthly climatological trends. Results show that:

• Urban areas recorded the highest average daytime (34.57°C) and nighttime (26.58°C) temperatures, confirming the strongest UHI effect.
• Peri-wetland areas were slightly cooler than urban areas but warmer than industrial areas during the day.
• Industrial areas had the lowest average daytime temperatures but remained relatively warm at night.

Analysis of the diurnal temperature range revealed that peri-wetland areas experienced the greatest difference between daytime and nighttime temperatures (8.36°C), indicating rapid heating during the day and efficient cooling at night due to vegetation and moisture. Urban areas showed a smaller DTR (7.98°C), reflecting heat retention by buildings and paved surfaces, while industrial areas had the smallest DTR (6.90°C), suggesting a stable but persistently warm environment resulting from dense infrastructure and continuous industrial activity.

Overall, the study confirms that urbanization significantly increases land surface temperatures in Chennai, while natural vegetation moderates thermal conditions. It also demonstrates the usefulness of long-term MODIS satellite observations and AppEEARS for monitoring urban heat islands, while highlighting the need for higher-resolution data to accurately assess coastal environments.

Conclusion

This study evaluated land surface temperature (LST) dynamics across three land-use categories in Chennai using an 11-year (2015–2025) MODIS MOD11A2 dataset accessed via AppEEARS. Statistical, diurnal, and seasonal analyses consistently demonstrate a Surface Urban Heat Island (SUHI) effect: Urban areas show the highest daytime (34.57 °C) and night-time (26.58 °C) mean LST and the lowest diurnal temperature range (7.98 °C), while Peri-Wetland areas act as a comparative cooling zone with the highest diurnal range (8.36 °C). The SUHI signal intensifies during the pre-monsoon summer (May–June) and narrows, but does not disappear, during the winter months. A fourth, Coastal, class could not be analyzed due to complete retrieval failure at 1 km resolution near the shoreline — a limitation discussed explicitly along with three concrete mitigation strategies for future work. Relative to earlier Chennai UHI studies, this work contributes a longer and more current observational record, an explicit peri-wetland comparison class, and a combined diurnal–seasonal analytical framework built on a fully reproducible, low-code workflow. The results support the case for conserving peri-wetland areas as natural cooling zones, targeting heat-mitigation measures at persistently warm urban and industrial pockets, and accounting for night-time heat retention — not only daytime peaks — in coastal urban planning and climate-adaptation policy.

References

[1] Rajan, E.H.S., Amirtham, L.R. \"Urban heat island intensity and evaluation of outdoor thermal comfort in Chennai, India,\" Environ Dev Sustain, vol. 23, pp. 16304–16324, 2021. [2] Jeganathan, A., Andimuthu, R., Prasannavenkatesh, R. et al. \"Spatial variation of temperature and indicative of the urban heat island in Chennai Metropolitan Area, India,\" Theor Appl Climatol, vol. 123, pp. 83–95, 2016. [3] Kesavan, R., Muthian, M., Sudalaimuthu, K. et al. \"ARIMA modeling for forecasting land surface temperature and determination of urban heat island using remote sensing techniques for Chennai city, India,\" Arab J Geosci, vol. 14, art. 1016, 2021. [4] Muthiah, K., Mathivanan, M., Duraisekaran, E. \"Dynamics of urban sprawl on the peri-urban landscape and its relationship with urban heat island in Chennai Metropolitan Area, India,\" Arabian Journal of Geosciences, vol. 15, no. 22, art. 1664, 2022. [5] Grace, C.A.Z., Soundranayagam, J.P., Sidhu, N. et al. \"Decadal dynamics of urban heat islands in a coastal industrial region of India: remote sensing insights for sustainable urban planning,\" Environ Dev Sustain, 2025. [6] Sethuraman, S., Alshahrani, H.M., Alotaibi, F.A. et al. \"Analysis of urban heat island by using remote sensing techniques and geo-spatial approach in Chennai Region, Tamil Nadu, India,\" J Earth Syst Sci, vol. 134, art. 141, 2025. [7] MuraliKrishna, I., Sahithi, V.S. \"Assessing the relation between land use/land cover changes and environmental parameters for Chennai City, Tamil Nadu: a remote sensing and GIS approach,\" Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1/W2-2023, pp. 471–482, 2023. [8] Rastogi, K., Sharma, S. \"Spatio-temporal relation of urban density and surface urban heat island index,\" Journal of Geomatics, vol. 18, no. 1, pp. 71–75, 2024. [9] Ranjan, A.K., Sahoo, K.K., Manasa, P. et al. \"Assessing the role of urban expansion in an industrial city (Rourkela, India) on land use/land cover dynamics and its impacts on aerosols and land surface temperature,\" J Indian Soc Remote Sens, vol. 53, pp. 3843–3864, 2025. [10] Badugu, A., Arunab, K.S., Mathew, A. \"Predicting land surface temperature using data-driven approaches for urban heat island studies: a comparative analysis of correlation with environmental parameters,\" Model. Earth Syst. Environ., vol. 10, pp. 1043–1076, 2024. [11] Panda, J., Rath, S.S. \"Observed and simulated characteristics of 2015 Chennai heavy rain event: impact of land-use change, SST, and high-resolution global analyses,\" Pure Appl. Geophys., vol. 179, pp. 3391–3409, 2022. [12] Mathew, A., Aljohani, T.H., Shekar, P.R. et al. \"Spatiotemporal dynamics of urban heat island effect and air pollution in Bengaluru and Hyderabad: implications for sustainable urban development,\" Discov Sustain, vol. 6, art. 134, 2025. [13] Rath, S.S., Panda, J. \"A study of near-surface boundary layer characteristics during the 2015 Chennai flood in the context of urban-induced land use changes,\" Pure Appl. Geophys., vol. 176, pp. 2607–2629, 2019.

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Copyright © 2026 Mohit Sheode. 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.