Geospatial Prediction of Surface Urban Heat Island Intensity Using an Integrated Remote Sensing–Machine Learning Framework: A Case Study in Hue City, Vietnam

The surface urban heat island (SUHI) effect, driven by rapid urbanization and global climate change, has become a critical concern for urban environments, particularly in tropical coastal cities. This growing concern necessitates robust assessment and forecasting approaches to support effective mitigation and urban planning. This study proposes an integrated remote sensing–machine learning framework to model and predict SUHI intensity. An extreme gradient boosting (XGBoost) model is employed to quantify SUHI intensity and its relationships with various conditioning factors, including topographic variables, land use/land cover (LULC), land surface characteristics (bare land, built-up, tree canopy, and low vegetation densities), and proximity variables (distance to roads, rivers, lakes, and coastline). The coastal urban core of Hue City, Vietnam, is selected as the case study, with SUHI intensity modeled for the 2025 dry season. The projected LULC pattern for 2035 is generated using an integrated cellular automata–Markov chain–artificial neural network framework, which enables prediction of SUHI intensity under the anticipated urban growth scenario. Prediction results indicate that the maximum SUHI intensity is expected to increase from 12.74 °C in 2025 to 13.55 °C in 2035. The SUHI intensity map helps identify intra-urban hot spots experiencing severe surface heat stress. Notably, the area exposed to strong SUHI intensity (greater than approximately 6 °C) in 2035 is projected to increase by 10% relative to its extent in 2025. These findings emphasize the urgency of targeted SUHI mitigation and provide insights for climate-resilient urban planning in the study area.