Characterizing heatwave-driven urban heat patterns using multi-source geospatial data: SUHI dynamics and intra-urban thermal variability in Argentine cities

Urban heat represents a growing environmental and societal challenge, particularly during heatwave events, when the interaction between climate extremes and urban form amplifies thermal exposure. Advancing the characterization of urban heat therefore requires geospatial approaches capable of capturing both temporal dynamics and fine-scale spatial heterogeneity. This study investigates Surface Urban Heat Island (SUHI) intensity and intra-urban land surface temperature (LST) patterns during heatwaves in three climatically contrasting Argentine cities: Posadas (humid subtropical city), Buenos Aires (temperate coastal megacity), and Neuquén (semi-arid Patagonian city).

We apply an integrated geospatial framework combining high-temporal-resolution MODIS LST data with high-spatial-resolution Landsat 8 and Sentinel-2 imagery. Heatwave periods are analysed to quantify daytime and nighttime SUHI across urban, peri-urban, and rural zones, while Local Climate Zones (LCZs) are mapped to assess how urban morphology, land cover, and vegetation modulate thermal patterns at the intra-urban scale. Statistical analyses are used to evaluate significant temperature differences among zones and urban typologies under extreme heat conditions.

Results reveal strong inter-city contrasts and complex spatial responses. Posadas and Buenos Aires exhibit pronounced nocturnal SUHI, reflecting urban heat retention during heatwaves, whereas daytime patterns differ substantially depending on regional context. In Neuquén, a heterogeneous thermal response emerges, including a negative daytime SUHI relative to the surrounding semi-arid plateau, highlighting the influence of soil moisture, vegetation scarcity, and topography. Across all cities, compact and densely built LCZs consistently show higher LST, while vegetated areas, river corridors, and water bodies act as persistent cooling zones during heat extremes.

By integrating multi-source geospatial data within an LCZ-based analytical framework, this study advances the characterization of urban heat under extreme conditions and provides transferable insights for climate-resilient urban planning, heat risk mitigation, and spatially targeted adaptation strategies.