Abstract：Taking 15 cities in the Yangtze River Delta urban agglomeration as the study area, the LST data were retrieved from Landsat 8 images in the summer (from June to September) from 2016 to 2018 by the radiative transfer equation method, and the three-dimensional (3D) building data in 2018 were derived from the online map service platform Gaode maps through the open API. At six spatial scales of 200m, 400m, 600m, 800m, 1000m, and 1200m, the spatial regression models were used to explore the influence of the 3D building landscape pattern on LST. The results showed that the thermal environment in the 15 cities was relatively harsh, and more than 50% of the areas were high-temperature or sub-high-temperature zones. There was a scale effect on the influence of 3D building landscape pattern on LST. As the scale increased, the influence of the 3D building landscape pattern gradually weakened. Among the six scales, 200m was the appropriate scale for all cities to analyze the influence of 3D landscape pattern of buildings on LST. Among the nine selected 3D landscape metrics, building structure index (BSI), average building volume (AV) and building evenness index (BEI) were the most powerful indicators affecting LST. BSI and BEI were positively correlated with LST for all the cities. Except for Shanghai, Suzhou and Taizhou, AV was also positively correlated with LST, indicating that lean, tall and evenly distributed buildings could help reduce the LST and improve the urban thermal environment.
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