Land surface temperature (LST), which is heavily influenced by urban surface structures, is a significant parameter in urban environmental analysis. This study examined the effect impervious surfaces (IS) spatial patts have on LST in Beijing, China. A classification and regression tree model (CART) was adopted to estimate IS as a continuous variable using Landsat images from two seasons combined with QuickBird. LST was retrieved from the Landsat Thematic Mapper (TM) image to examine the relationships between IS and LST. The results revealed that CART was capable of consistently predicting LST with acceptable accuracy (correlation coefficient of 0.94 and the average error of 8.59%). Spatial patts of IS exhibited changing gradients across the various urban-rural transects, with LST values showing a concentric shape that increased as you moved from the outskirts towards the downtown areas.Transect analysis also indicated that the changes in both IS and LST patts were similar at various resolution levels, which suggests a distinct linear relationship between them. Results of correlation analysis further showed that IS tended to be positively correlated with LST, and that the correlation coefficients increased from 0.807 to 0.925 with increases in IS pixel size. The findings identified in this study provide a theoretical basis for improving urban planning efforts to lessen urban temperatures and thus dampen urban heat island effects.