Research on the Influence Mechanism of Urban Morphology Indicators on the Diurnal and Seasonal Surface Temperature

Urban morphology influences the distribution and variation of land surface temperature (LST) by altering surface cover type. However, the coupling effects of the daily LST cycle and the multidimensional morphological driving mechanisms remain insufficiently explored in existing studies. This study, based on ECOSTRESS diurnal LST data, focuses on Harbin, a representative city in China’s cold climate regions. By integrating land cover data, urban morphology vector data, and interpretable machine learning models, it investigates the intricate relationship between urban morphology indicators and LST over a 24 h cycle under cold climate conditions. LST prediction was carried out using Gradient Boosting Decision Trees (XGBoost), Random Forests (RF), Support Vector Machines (SVM), and Multiple Linear Regression (MLR), with an evaluation of the prediction accuracy of each model. The findings indicated the following: (1) The influence of 2D and 3D urban morphological indicators on LST exhibits significant seasonal variation, with Building Otherness (BO), Mean Building Height (BH), and the Normalized Difference Vegetation Index (NDVI) exerting notable impacts on LST in both winter and summer. (2) Significant interactions exist between certain urban morphological indicators that can effectively reduce LST, when the Patch Land Area Proportion (PLAND) exceeds 40%, increasing the Largest Patch Index (LPI) contributes to lowering LST in summer. (3) Among the evaluated machine learning algorithms, XGBoost demonstrates the highest prediction accuracy. This study provides scientific insights for urban planning and policy development, aiding in the optimization of urban morphological designs to effectively regulate LST.