Land surface and air temperature dynamics: The role of urban form and seasonality

Marzie Naserikia; Melissa A Hart; Negin Nazarian; Benjamin Bechtel; Mathew Lipson; Kerry A Nice

doi:10.1016/j.scitotenv.2023.167306

Land surface and air temperature dynamics: The role of urban form and seasonality

Sci Total Environ. 2023 Dec 20:905:167306. doi: 10.1016/j.scitotenv.2023.167306. Epub 2023 Sep 22.

Authors

Marzie Naserikia¹, Melissa A Hart², Negin Nazarian³, Benjamin Bechtel⁴, Mathew Lipson⁵, Kerry A Nice⁶

Affiliations

¹ Australian Research Council Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia. Electronic address: m.naserikia@unsw.edu.au.
² Australian Research Council Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia.
³ Australian Research Council Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia; School of Built Environment, University of New South Wales, Sydney, Australia; City Futures Research Centre, University of New South Wales, Sydney, Australia.
⁴ Department of Geography, Ruhr-University Bochum, Bochum, Germany.
⁵ Bureau of Meteorology, Canberra, Australia.
⁶ Transport, Health and Urban Systems Research Lab, Faculty of Architecture, Building, and Planning, University of Melbourne, Melbourne, Australia.

PMID: 37742968
DOI: 10.1016/j.scitotenv.2023.167306

Abstract

Due to the scarcity of air temperature (T_a) observations, urban heat studies often rely on satellite-derived Land Surface Temperature (LST) to characterise the near-surface thermal environment. However, there remains a lack of a quantitative understanding on how LST differs from T_a within urban areas and what are the controlling factors of their interaction. We use crowdsourced air temperature measurements in Sydney, Australia, combined with urban landscape data, Local Climate Zones (LCZ), high-resolution satellite imagery, and machine learning to explore the influence of urban form and fabric on the interaction between T_a and LST. Results show that LST and T_a have distinct spatiotemporal characteristics, and their relationship differs by season, ecological infrastructure, and building morphology. We found greater seasonal variability in LST compared to T_a, along with more pronounced intra-urban spatial variability in LST, particularly in warmer seasons. We also observed a greater temperature difference between LST and T_a in the built environment compared to the natural LCZs, especially during warm days. Natural LCZs (areas with mostly dense and scattered trees) showed stronger LST-T_a relationships compared to built areas. In particular, we observe that built areas with higher building density (where the heat vulnerability is likely more pronounced) show insignificant or negative relationships between LST- T_a in summer. Our results also indicate that surface cover, distance from the ocean, and seasonality significantly influence the distribution of hot and cold spots for LST and T_a. The spatial distribution for T_a hot spots does not always overlap with LST. We find that relying solely on LST as a direct proxy for the urban thermal environment is inappropriate, particularly in densely built-up areas and during warm seasons. These findings provide new perspectives on the relationship between surface and canopy temperatures and how these relate to urban form and fabric.

Keywords: Air temperature; Crowdsourcing; Land surface temperature; Land use data; Local climate zone; Remote sensing; Urban heat.