Increased urbanisation is boosting the intensity and frequency of the Urban Heat Island (UHI) effect in highly developed cities. The advances in satellite measurement are facilitating the analysis of this phenomenon using Land Surface Temperature (LST) as an indicator of the Surface UHI (SUHI). Due to the spatial implications of using remote sensing data, this research developed ArcUHI, a Geographic Information System (GIS) add-in for modelling SUHI. The tool was designed in ArcGIS, which was bound with R to run machine learning algorithms in the background. ArcUHI was tested using the metropolitan area of Madrid (Spain) in 2006, 2012 and 2018 as a case study. The add-in was found to predict observed LST with high accuracy, especially when using Random Forest Regression (RFR). Building height and albedo were identified as the main drivers of SUHI, whose magnitude and extension increased with time. In view of these results, strategic roof and wall greening was suggested as a measure to mitigate the street canyon effect entailed by buildings and offset the heat retention capacity of built-up surfaces.

城市化进程的加快正在提高高度发达城市的城市热岛效应 (UHI) 的强度和频率。卫星测量的进步正在促进使用地表温度 (LST) 作为地表 UHI (SUHI) 的指标来分析这种现象。由于使用遥感数据的空间影响，本研究开发了 ArcUHI，一种用于建模 SUHI 的地理信息系统 (GIS) 插件。该工具是在 ArcGIS 中设计的，它与 R 绑定以在后台运行机器学习算法。ArcUHI 在 2006 年、2012 年和 2018 年以马德里（西班牙）大都市区为案例研究进行了测试。发现该插件可以高精度地预测观察到的 LST，尤其是在使用随机森林回归 (RFR) 时。建筑高度和反照率被确定为 SUHI 的主要驱动因素，其幅度和范围随着时间的推移而增加。鉴于这些结果，建议采取策略性的屋顶和墙壁绿化措施，以减轻建筑物带来的街道峡谷效应并抵消建筑表面的保温能力。