Multi-Sensor Downscaling of Land Surface Temperature Using Sentinel-2 and Landsat 8 Imagery: Evidence from Dhaka City, Bangladesh

Abstract

Rapid urbanization and increasing land surface temperatures (LSTs) have intensified urban heat stress in rapidly growing tropical megacities such as Dhaka. However, the coarse spatial resolution of conventional thermal satellite imagery limits the identification of fine-scale urban thermal variability required for climate-sensitive urban planning. This study develops a multi-sensor LST downscaling framework by integrating Landsat 8 thermal imagery with Sentinel-2-derived spectral indices within the Google Earth Engine (GEE) platform. A random forest regression model was developed using the normalized difference vegetation index, normalized difference built-up index, and modified normalized difference water index as predictors for statistical downscaling from the 30 m Landsat grid to a nominal 10 m grid. To preserve localized thermal heterogeneity and improve radiometric consistency, a bicubic residual correction approach was incorporated into the downscaling workflow. The resulting statistically downscaled LST estimate on a nominal 10 m grid was subsequently used to classify Urban Thermal Zones (UTZs) across Dhaka City. The results showed that LST was negatively associated with vegetation and water-related indices and positively associated with the built-up index. The statistically downscaled product provided a more spatially detailed representation of the Landsat-derived thermal field and delineated relative surface-temperature hotspots and cooler zones across the study area. High-temperature zones were primarily concentrated within densely built-up commercial and industrial areas, whereas comparatively lower temperatures were observed in vegetated and water-dominated regions. The proposed framework demonstrates a computationally efficient approach to spatially refining Landsat-derived LST in a data-constrained tropical megacity. The findings provide valuable spatial information for urban climate adaptation, heat mitigation planning, and climate-resilient urban development.