Land use and land cover dynamics are pivotal to communicating land surface temperature (LST) scenarios. This study characterises the influence of biophysical variables on LSTs in the Dar es Salaam Metropolitan City (DMC). Landsat images were analysed using geographically weighted regression (GWR) and ordinary least square (OLS) models to determine biophysical variables (soil adjusted vegetation index, normalized difference built-up index, and normalised difference bareness index) and LST relationships. The GWR analysis resultsrevealed that LST had a weak to strong negative correlation with the soil adjusted vegetation index, a moderate positive correlation with normalized difference built-up index, and a low positive correlation with the normalised difference bareness index. GWR predicted LST better than OLS, with coefficient of determination -R² values of 55%, 80%, and 62% for 1995, 2009, and 2017, respectively. In addition, higher model residuals values were observed in high building density compared to low building density areas. This study provides a broad understanding of the biophysical variables’ impact on LST in DMC and provides reference for site-specific urban land-use planning and designing strategies for LST mitigation.

Land use land cover (LULC) changes affect the planet's energy balance and region's climate. Land Surface Temperature (LST) is a vital indicator of this change. Studies in Dar es Salaam Metropolitan City have investigated LST and its relationships with building heights and densities, urban heat islands, spectral indices, and urban morphological determinants. The present study used cross-sectional profiles, chord diagrams, and simple linear regression models to examine the influence of LULC changes on the LST in Dar es Salaam Metropolitan City (DMC). LST was extracted from Landsat 5 TM and 8 OLI/TIRS images for 1995, 2009, and 2017. LULC was identified via the supervised random forest classification algorithm. Between 1995 and 2017, built-up areas rose by 8%, vegetation fell 7%, and bare soil 3%. As a result, the average LST rose by 3 °C. Built-up areas had the highest temperatures (24–26.5 °C), followed by bare soil (22–25.5 °C). The lowest temperatures (21–25 °C) were on vegetation and water. Built-up area positively correlated with LST, while vegetation, water bodies, and bare soil negatively correlated. The study results can assist local authorities in enforcing urban planning regulations, raising public awareness, and guiding policymakers in creating sustainable planning and management strategies for the future. Keywords: Dar es Salaam, Land use land cover, simple linear regression model, land surface temperature, chord diagrams