Multi-index remote sensing assessment of Urban heat risk and vegetation cooling effects for sustainable planning in Errachidia a Pre-Saharan City in Southeastern Morocco

Extreme heat in dryland cities represents one of the most undercharacterized risks in urban climate science, yet spatially explicit heat risk frameworks for pre-Saharan environments remain largely absent from the literature. This study addresses this gap by developing and applying an integrated Heat Risk Index (HRI) for Errachidia, Morocco, a rapidly urbanizing oasis city in the pre-Saharan zone during the record-breaking summer of July 2023. A multi-sensor remote sensing approach combining Landsat-8 thermal data and Sentinel-2 multispectral imagery was employed alongside a deep learning U-Net architecture (ResNet34 backbone) for land use/land cover (LULC) classification, achieving an overall accuracy of 94.0% (Kappa = 0.925). Land surface temperature (LST) ranged from 33.8 °C to 50.1 °C, with bare soil surfaces recording the highest mean LST (47.2 °C), representing a 9.4 °C thermal contrast with water bodies. The HRI derived from normalized LST, NDBI, and inverse SAVI ranged from 0.153 to 0.844 (mean = 0.653), classifying the majority of the urban area as high-to-very-high risk. Cooling distance analysis quantified a rapid thermal decay of 5.39 °C within 25 m of dense vegetation, diminishing to below 1 °C beyond 150 m, suggesting an approximate distance threshold for green infrastructure placement under the analyzed conditions. A marginal negative daytime Surface Urban Heat Island Intensity (SUHII = − 1.08 °C) indicated that suburban bare soil surfaces recorded slightly higher daytime land surface temperatures than the urban core; however, mean urban LST remained at 45.3 °C, underscoring that absolute heat stress in the urban area is extreme regardless of this differential. These findings provide the first quantitative heat risk framework for a pre-Saharan oasis city, offering spatially explicit, indicative insights to inform climate-adaptive urban planning aligned with SDG 11 and SDG 13.