The rising global urban population, coupled with climate change and urban expansion, is increasing exposure to extreme heat. This study introduces a spatial regression model, utilizing remote sensing data from 200 cities, to assess population exposure to Land Surface Temperature (LST) extremes. Exposure is defined as person-days exceeding an LST threshold. The model reveals a significant role for urban vegetation in reducing this exposure, highlighting the efficiency of targeting high-exposure areas for greening initiatives.
