Abstract: In response to the urban heat island problem caused by the rapid urbanization development of the Pinglu Canal Economic Belt. Based on the GEE platform, and by using spatiotemporal cube models, emerging spatiotemporal hotspot analysis, and spatial analysis methods, this article aims to study the spatiotemporal evolution characteristics and driving factors of urban heat islands in the Pinglu Canal Economic Belt from 2010 to 2020. The following conclusions can be drawn: 1) The proportion of high-temperature areas in core cities increased from 10% in 2010 to 21% in 2020, with newly added high-temperature patches transformed from medium temperature areas. 2) Emerging hotspot analysis identified 17 spatiotemporal patterns of cold hotspots, with the exception of Fangchenggang City, the other four cities showing significant new hot spots around the built-up areas, ranked in the order of Beihai City>Nanning City>Qinzhou City>Guigang City; 3) The intensity of the urban heat island had significant spatial autocorrelation, with high to high clustering overlapping highly with impermeable surfaces. The intensity of the urban heat island was highly matched with land use types in space, such as the average surface temperature of impermeable surfaces and bare land being higher, while the average surface temperature of water bodies was the lowest; 4) In 2020, the order of the impact of the three driving factors on urban heat island effect was NDVI>DEM>POP. The dual factor interaction was most strongly driven by the combination of NDVI and DEM. This study has important scientific implications for how cities in the Pinglu Canal Economic Belt can cope with the intensified heat wave risk, and contributes to exploring the comprehensive mechanism of urban heat island formation in the Pinglu Canal Economic Belt.

Key words: urban heat island, spatiotemporal characteristics, spatial autocorrelation, geographic detector, driving factors, Pinglu Canal Economic Belt, China