Explore the application of remote sensing in the dynamic monitoring of urban heat island effect

Abstract

With the acceleration of urbanization, the temperature of cities is higher than that of surrounding suburbs, thus producing the urban heat island effect. To more effectively respond to these challenges, accurate monitoring and scientific heat island effect have become the focus of current research. This study presents an advanced approach for urban heat island (UHI) monitoring by synergistically combining multi-sensor satellite data (Landsat, MODIS, and Sentinel) with machine learning-enhanced thermal algorithms. Focusing on Beijing, the research reveals key urban thermal patterns: building density shows a strong positive correlation (R=0.78) with heat intensity, while vegetation coverage demonstrates significant cooling effects. Current results achieve a ±1.8°C accuracy margin in urban fringe areas. The study highlights how next-generation satellite systems, coupled with deep learning techniques, are enabling more precise sub-kilometer thermal mapping to overcome existing limitations. These technological advancements provide urban planners with actionable insights for developing targeted cooling strategies, particularly in high-density metropolitan areas where heat mitigation is most critical. The integrated methodology offers a scalable framework for thermal environment analysis in global megacities.