Abstract. Urban heatwaves are intensifying due to climate change, posing significant risks to public health and infrastructure in densely populated cities. This study develops a spatially explicit framework to assess urban heat vulnerability in the Dhaka Metropolitan Area (DMA), Bangladesh, by integrating vegetation and soil moisture indicators derived from Synthetic Aperture Radar (SAR). Sentinel-1 imagery was used to compute the Radar Vegetation Index (RVI) and estimate surface soil moisture (SSM) through empirical modelling, combining a modified Water Cloud Model (mWCM) with regression calibration against SMAP data. MODIS-derived Land Surface Temperature (LST) was used to characterize thermal variation. A Geographically Weighted Regression (GWR) model, supported by Principal Component Analysis (PCA), quantified local relationships between LST, RVI, and SSM. Spatial autocorrelation analysis using Moran’s I confirmed clustering in both thermal and environmental variables. Results show that areas with higher vegetation and soil moisture correspond to lower LST, highlighting their cooling effects. The model achieved strong performance (R² = 0.8835; RMSE = 0.6126; MAE = 0.4753), demonstrating its robustness and applicability in data-scarce contexts. A Heat Vulnerability Index (HVI) was constructed to spatially map susceptibility to extreme heat. This SAR-based approach supports targeted urban heat adaptation strategies through spatially informed planning.
Aishi et al. (Wed,) studied this question.