Urban heat island (UHI) effect tends to harm health, increase anthropogenic energy consumption, and water consumption. Some policies targeting UHI mitigation have been implemented for a few years and thus needs to be evaluated for changes or modifications in the future. A low-cost approach to rapidly monitoring UHI intensity variations can assist in evaluating policy implementations. In this study, we proposed a new approach to local-scale UHI intensity estimates by using nighttime light satellite imageries. We explored to what extent UHI intensity could be estimated according to nighttime light intensity at two local scales. We attempted to estimate district-level and neighbourhood-level UHI intensity across London and Paris. As the geography level rises from district to neighbourhood, the capacity of the models explaining the variations of the UHI intensity decreases. Although the possible presence of residual spatial autocorrelation in the conventional regression models applied to geospatial data, most of the studies are likely to neglect this issue when fitting data to models. To remove negative effects of the residual spatial autocorrelation, this study used spatial regression models instead of non-spatial regression models (e.g., OLS models) to estimate UHI intensity. As a result, district-level UHI intensity was successfully estimated according to nighttime light intensity (approximately R² = 0.7, MAE =1.16 °C, and RMSE =1.74 °C).

城市热岛效应（UHI）趋向于损害健康，增加人为能源消耗和水消耗。针对减低UHI的某些政策已经实施了几年，因此需要在将来进行评估以进行更改或修改。快速监测UHI强度变化的低成本方法可以帮助评估政策实施情况。在这项研究中，我们通过使用夜间光卫星图像，提出了一种新的本地UHI强度估算方法。我们探索了在两个地方尺度上可以根据夜间光照强度在多大程度上估计UHI强度。我们试图估计整个伦敦和巴黎的区级和街区级UHI强度。随着地理位置的提高，从地区到周边地区，解释UHI强度变化的模型的能力降低了。尽管在应用于地理空间数据的常规回归模型中可能存在残余空间自相关，但是在将数据拟合到模型时，大多数研究都可能忽略了这个问题。为了消除残留空间自相关的负面影响，本研究使用空间回归模型代替非空间回归模型（例如OLS模型）来估计UHI强度。结果，根据夜间光线强度（大约是 为了消除残留空间自相关的负面影响，本研究使用空间回归模型代替非空间回归模型（例如OLS模型）来估计UHI强度。结果，根据夜间光线强度（大约是 为了消除残留空间自相关的负面影响，本研究使用空间回归模型代替非空间回归模型（例如OLS模型）来估计UHI强度。结果，根据夜间光线强度（大约是R ² = 0.7，MAE = 1.16°C，RMSE = 1.74°C。