Jia, WX; Zhao, SQ (2020). Trends and drivers of land surface temperature along the urban-rural gradients in the largest urban agglomeration of China. SCIENCE OF THE TOTAL ENVIRONMENT, 711, 134579.

Urban heat magnitude and effects may represent harbingers of future climate change and the urban-rural gradients provide a unique natural laboratory for identifying both problems and solutions to climate change mitigation and adaptation. Here, we explored the trends and driving forces of land surface temperature (LST) along the urban-rural gradients of 26 cities in the largest urban agglomeration of China, the Yangtze River Delta Urban Agglomeration, using MODIS LST data combined with urban intensity, background climate, vegetation greenness, landscape structure, albedo, population and gross domestic product (GDP). We found that LST generally increased with increasing urban intensity along the urban-rural gradients while with large diurnal and seasonal variability. Large variability also existed between the maximum and minimum LST within the same urban intensity (e.g., 6.4 degrees C), suggesting cities themselves provide ready-made solutions (minimum) to resolving heat island problems. However, the range of LST within the same intensity decreased with the urban intensity and narrowed drastically when the intensity reached certain thresholds (e.g., 58-87% varying with season, time of day, and city), implying that the space for climate mitigation is very limited once the urbanization intensity exceeds critical thresholds. The roles of landscape structure (composition and configuration) for greenspace and urban land have become increasingly important in driving the variation of LST with increasing urban intensity from low (20%-30%), middle (45%-55%) to high (70%80%), clearly indicating that subtle urban landscape designing, such as less aggregated urban configuration and more irregular greenspace shape are effective strategies to mitigate climate change in highly urbanized areas and cities themselves already provide such vivid demonstrations for us to find and learn. (C) 2019 Elsevier B.V. All rights reserved.