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<p>The Urban Heat Island (UHI) effect requires high resolution and temporally explicit thermal observations for accurate characterization of urban climate variability. This study analyzes land surface temperature (LST) variability using 8&#xa0;m thermal data from the EOS-8 satellite over Delhi (daytime) and Pune (nighttime). Daytime observations were acquired at 09:30 AM (IST) on 30<sup>th</sup> December 2024, while nighttime data were acquired at 01:01 AM (IST) on 28<sup>th</sup> April 2025, enabling assessment of diurnal thermal behavior. Surface emissivity derived from Sentinel-2A land use/land cover classification (10:30 AM) was integrated with brightness temperature to retrieve LST using a single-channel algorithm (SCA), and results were compared with Landsat 9 LST (10:30 AM IST). Correlation analysis LST with spectral indices and Albedo analysis shows strong daytime LST &amp; NDBI relation, while nighttime patterns highlight NDWI influence and reduced built-up sensitivity. Daytime patterns highlight the influence of impervious surfaces, while nighttime behavior reflects moisture and thermal inertia effects. Urban thermal patterns were analyzed using the Local Climate Zone (LCZ) framework. The results reveal pronounced intra urban thermal heterogeneity, with built-up areas exhibiting higher LST than vegetated and water surfaces. Multiscale analysis at 8&#xa0;m, 80&#xa0;m, 800&#xa0;m shows that spatial aggregation reduces variability and masks localized hotspots, demonstrating that spatial resolution and time of acquisition jointly influence urban thermal interpretation. This study shows potential of EOS-8 Satellite for fine scale urban heat island assessments.</p>

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Fine-Scale Urban Heat Island Assessment with EOS-8 Thermal Imagery: Insights from Delhi and Pune

  • 期刊论文

  • 发表日期：

    2026年7月30日

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  • Priya Madhuri Penchala,
  • M. Ramdas,
  • J. Kamini,
  • A. V. Suresh Babu,
  • K. H. V. Durga Rao

摘要

The Urban Heat Island (UHI) effect requires high resolution and temporally explicit thermal observations for accurate characterization of urban climate variability. This study analyzes land surface temperature (LST) variability using 8 m thermal data from the EOS-8 satellite over Delhi (daytime) and Pune (nighttime). Daytime observations were acquired at 09:30 AM (IST) on 30th December 2024, while nighttime data were acquired at 01:01 AM (IST) on 28th April 2025, enabling assessment of diurnal thermal behavior. Surface emissivity derived from Sentinel-2A land use/land cover classification (10:30 AM) was integrated with brightness temperature to retrieve LST using a single-channel algorithm (SCA), and results were compared with Landsat 9 LST (10:30 AM IST). Correlation analysis LST with spectral indices and Albedo analysis shows strong daytime LST & NDBI relation, while nighttime patterns highlight NDWI influence and reduced built-up sensitivity. Daytime patterns highlight the influence of impervious surfaces, while nighttime behavior reflects moisture and thermal inertia effects. Urban thermal patterns were analyzed using the Local Climate Zone (LCZ) framework. The results reveal pronounced intra urban thermal heterogeneity, with built-up areas exhibiting higher LST than vegetated and water surfaces. Multiscale analysis at 8 m, 80 m, 800 m shows that spatial aggregation reduces variability and masks localized hotspots, demonstrating that spatial resolution and time of acquisition jointly influence urban thermal interpretation. This study shows potential of EOS-8 Satellite for fine scale urban heat island assessments.