A Scalable Remote Sensing and Machine Learning Framework for High-Resolution Land Surface Temperature Modeling

Understanding urban heat trends, biological changes, and effects of climate change depends on knowing Land Surface Temperature (LST) in dynamic coastal settings. This work integrates multi-sensor satellite images with socio-environmental characteristics using machine learning to provide a scalable framework for high-resolution LST estimation in the coastal area. We combined thermal data from the Moderate Resolution Imaging Spectroradiometer (MODIS), optical imaging from Sentinel-2, and auxiliary information like precipitation, elevation, and population density using the Google Earth Engine (GEE) platform into a single geographic model. A Random Forest (RF) regression approach is used to forecast LST, designed on 4,305 samples and tested on 1,076 randomly dispersed sites at 100-meter resolution. The model shows good predictive ability with a Root Mean Squared Error (RMSE) of 0.624°C and a Mean Absolute Error (MAE) of 0.484°C. Significant correlations are found between surface temperature, topography, rainfall, and human activity through spatial analysis. The generated fine-scale LST maps provide practical information for reducing urban heat, adapting to climate change, and planning in highly populated coastal areas. This study highlights the potential of cloud-based geospatial platforms and machine learning for precise, effective, and scalable environmental monitoring in complex coastal systems.