This study is aimed at modelling land surface temperature (LST) using the spatial lag model (SLM) and spatial error model (SEM). Landsat-8 OLI/TIRS data and digital elevation model were used to generate the dependent and explanatory variables. First, correlation between the variables and LST, and presence of spatial autocorrelation were assessed. Second, the modelled LST was validated. The findings revealed that built-up areas, green areas and water bodies exhibit lower LST compared to the non-urbanized areas around the city. A moderate inverse relationship (r² = 0.6) is observed between LST and vegetation index with p-value = 4*10⁻¹¹. In contrast, built-up and surface water indices, albedo, and elevation indicate a weak positive correlation with LST. The LST predicted by SLM ranged between 20 and 42.9 °C (0.5 °C and 0.7 °C below the minimum and maximum of the original data, respectively). In the case of SEM, the predicted LST ranged between 20.4 and 42.2 °C (only 0.1 °C below the minimum of the original data). At 0.01 level of significant, all the variables are significant predictor of the LST except elevation. Both models performed well but SEM showed more superiority. The outcome of this study will enable planners to obtain insight into interventions that are necessary in order to mitigate surface temperature in urban areas.

本研究旨在使用空间滞后模型 (SLM) 和空间误差模型 (SEM) 对地表温度 (LST) 进行建模。Landsat-8 OLI/TIRS 数据和数字高程模型用于生成因变量和解释变量。首先，评估变量和 LST 之间的相关性，以及空间自相关的存在。其次，对建模的 LST 进行了验证。调查结果显示，与城市周围的非城市化地区相比，建成区、绿地和水体的 LST 较低。 在 LST 和植被指数之间观察到适度的反比关系 ( r ² = 0.6)， p 值 =  4*10 ^-11. 相比之下，积水和地表水指数、反照率和高程表明与 LST 呈弱正相关。SLM 预测的 LST 介于 20 和 42.9°C 之间（分别比原始数据的最小值和最大值低 0.5°C 和 0.7°C）。在 SEM 的情况下，预测的 LST 介于 20.4 和 42.2 °C 之间（仅比原始数据的最小值低 0.1 °C）。在 0.01 的显着水平下，除海拔高度外，所有变量都是 LST 的显着预测因子。两种模型都表现良好，但 SEM 显示出更多的优势。这项研究的结果将使规划者能够深入了解降低城市地区地表温度所必需的干预措施。