The urban microclimate is influenced by many factors which trigger the well-known Urban Heat Island (UHI) phenomenon. Different approaches have been developed in the literature for estimating urban temperatures, but a compromise is always needed, either spatial or temporal. To solve this issue, this work presents a new way for estimating urban temperatures with a fine spatial resolution (even specific streets) while also keeping a high temporal resolution (hourly time-steps) for prolonged periods. This is done by developing an empirical model, based on the measurements of a reference weather station and data taken from mobile transects.

The proposed method was tested in Seville (Spain). The validation was done comparing the prediction of the model with the measurements of a fixed temperature sensor, from the 7th of June 2019 until the 7th of December 2019 (4390 hours). The results showed a high R² coefficient of 0.976 and a low RMSE of 1.21, improving the accuracy of previous literature for estimating urban temperatures. The methodology is applicable for any geographical location around the world, with different climates or population densities. In addition, it offers a precise way to verify the real impact of UHI mitigation strategies and concentrate climate change mitigation efforts.

城市小气候受许多因素的影响，这些因素触发了众所周知的城市热岛现象。文献中已经开发出用于估计城市温度的​​不同方法，但是在空间或时间上总是需要折衷。为了解决这个问题，这项工作提出了一种新的方法来估算具有良好空间分辨率（甚至是特定街道）的城市温度，同时还可以长时间保持较高的时间分辨率（每小时的时间步长）。这是通过根据参考气象站的测量值和从移动样带获取的数据开发经验模型来完成的。

所提出的方法在塞维利亚（西班牙）进行了测试。从2019年6月7日到2019年12月7日（4390小时），将模型的预测与固定温度传感器的测量值进行了比较以进行验证。结果表明，R ²系数为0.976，RMSE较低，为1.21，从而提高了先前文献估计城市温度的​​准确性。该方法适用于全球任何具有不同气候或人口密度的地理位置。此外，它提供了一种精确的方法来验证UHI缓解策略的实际影响并集中缓解气候变化的努力。