The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (May 2024)

Bridging the Gap: Matching Data from Low-Cost Mobile Sensors and Satellite for Urban Heat Island Research. A case study in Padua, Italy

  • C. Zanetti,
  • L. Rubert,
  • M. De Marchi,
  • S. E. Pappalardo

DOI
https://doi.org/10.5194/isprs-archives-XLVIII-4-W10-2024-205-2024
Journal volume & issue
Vol. XLVIII-4-W10-2024
pp. 205 – 211

Abstract

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In recent decades, the phenomenon of urban heat islands has intensified due to the increased frequency, magnitude, and duration of extreme weather and climate events. The study of urban microclimates plays a crucial role in implementing actions to reduce thermal stress caused by urban heat islands. Typically, urban heat islands are identified through thermal satellite images (e.g. Sentinel, Landsat). However, these tools are inadequate for detecting heat islands at an appropriate spatial and temporal scale. Furthermore, satellite images do not measure air temperature but rather the land surface temperature, which is not directly usable to estimate thermal stress on the population. To address this issue, we explored, during Summer 2023 in Padua, the feasibility of using a low-cost sensor (MeteoTracker©) to map urban heat islands, to assess spatial relationships with impermeable surfaces, and to investigate potential correlations with land surface temperature. Over an overall mobile mapping of 540 km, on average, air temperature is 1 °C higher in impermeable areas compared to permeable ones. Confirming this, a 0.1 increase in Normalized Difference Vegetation Index (NDVI) corresponds to a temperature decrease of 0.23 °C in the afternoon and 0.3 °C in the night. Moreover, a positive relationship was found between Land Surface Temperature (LST) and air temperature: an increase of approximately 2 °C in LST for every 1 °C increase in air temperature. This study lays the foundation for further research on urban heat islands, integrating satellite and ground data for the development of high-resolution adaptation actions.