Urban Heat Island Intensity Changes in Guangdong-Hong Kong-Macao Greater Bay Area of China Revealed by Downscaling MODIS LST with Deep Learning

The urban heat island (UHI) effect caused by urbanization negatively impacts the ecological environment and human health. It is crucial for urban planning and social development to monitor the urban heat island effect and study its mechanism. Due to spatial and temporal resolution limitations, existing land surface temperature (LST) data obtained from remote sensing data is challenging to meet the long-term fine-scale surface temperature mapping requirement. Given the above situation, this paper introduced the ResNet-based surface temperature downscaling method to make up for the data deficiency and applied it to the study of thermal environment change in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2000 to 2020. The results showed (1) the ResNet-based surface temperature downscaling method achieves high accuracy (R(2) above 0.85) and is suitable for generating 30 m-resolution surface temperature data from 1 km data; (2) the area of severe heat islands in the GBA continued to increase, increasing by 7.13 times within 20 years; and (3) except for Hong Kong and Macau, the heat island intensity of most cities showed an apparent upward trend, especially the cities with rapid urban expansion such as Guangzhou, Zhongshan, and Foshan. In general, the evolution of the heat island in the GBA diverges from the central urban area to the surrounding areas, with a phenomenon of local aggregation and the area of the intense heat island in the Guangzhou-Foshan metropolitan area is the largest. This study can enrich the downscaling research methods of surface temperature products in complex areas with surface heterogeneity and provide a reference for urban spatial planning in the GBA.