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<p>Near-surface air temperature (<i>T</i><sub>a</sub>) plays important roles in the interactions between the atmosphere and land covers, serving as crucial indicators for characterizing urban heat island and pedestrian thermal comfort. Although satellite-based observations and numerical simulations have been proposed to obtain <i>T</i><sub>a</sub> across various spatiotemporal scales, there is a lack of physically grounded, precise and easy-to-implement means to spatially resolve <i>T</i><sub>a</sub> at the centimeter level. Based on multimodal images derived from the unmanned aerial vehicle and synchronously measured meteorological parameters, this study combined the surface energy balance model and the automated machine learning to predict air temperature near the observed surfaces (<i>T</i><sub>a_predicted</sub>). The validations via near-ground measurements demonstrate the accuracy of this methodology, biases between predicted values and measured ones were almost maintained within 0.55 °C. According to the spatial distribution of <i>T</i><sub>a_predicted</sub>, it can be inferred that, at clear and calm noon in subtropical regions, dense shrubs or lawns exhibit limited significant cooling effect on <i>T</i><sub>a_predicted</sub> compared to granite paving with higher reflectance. Such a finding can be attributed to limited evapotranspiration from insufficient irrigation, lower reflectance increasing shortwave absorption of vegetation surfaces, and weak winds limiting convective heat removal. The developed model can be extended to other outdoor settings with similar meteorological conditions and morphologies of local climate zone 4, but further refinement is required for robust application across more diverse spatiotemporal scenarios.</p>

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Centimeter-level mapping of urban near-surface air temperature reveals limited midday cooling by small greenspace in subtropical environments under clear and calm weather

  • 期刊论文

  • 发表日期：

    2026年1月20日

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  • Xue Zhong,
  • Lihua Zhao,
  • Peng Ren,
  • Renzhi Wu,
  • Xiang Zhang,
  • Zhongwei Shen,
  • Yurong Shi,
  • Mohammad Asrafur Rahman

摘要

Near-surface air temperature (Ta) plays important roles in the interactions between the atmosphere and land covers, serving as crucial indicators for characterizing urban heat island and pedestrian thermal comfort. Although satellite-based observations and numerical simulations have been proposed to obtain Ta across various spatiotemporal scales, there is a lack of physically grounded, precise and easy-to-implement means to spatially resolve Ta at the centimeter level. Based on multimodal images derived from the unmanned aerial vehicle and synchronously measured meteorological parameters, this study combined the surface energy balance model and the automated machine learning to predict air temperature near the observed surfaces (Ta_predicted). The validations via near-ground measurements demonstrate the accuracy of this methodology, biases between predicted values and measured ones were almost maintained within 0.55 °C. According to the spatial distribution of Ta_predicted, it can be inferred that, at clear and calm noon in subtropical regions, dense shrubs or lawns exhibit limited significant cooling effect on Ta_predicted compared to granite paving with higher reflectance. Such a finding can be attributed to limited evapotranspiration from insufficient irrigation, lower reflectance increasing shortwave absorption of vegetation surfaces, and weak winds limiting convective heat removal. The developed model can be extended to other outdoor settings with similar meteorological conditions and morphologies of local climate zone 4, but further refinement is required for robust application across more diverse spatiotemporal scenarios.