Urban agglomerations, characterized by the synergetic development of large, medium, and small cities, are becoming the main form of urbanization in China. The climate effects of urbanization and agriculture, two most pervasive land use activities, have been the subject of active investigation. However, previous studies focused mainly on the urban heat island (UHI) effect of mega cities, while the climate effects of the more widespread agricultural activities and the urbanization in small/medium cities remain poorly understood. Using natural forests as reference, we proposed a new method to estimate the surface thermal environmental effects of human land use activities on a per-pixel basis based on MODIS land surface temperature (LST). We then compared the thermal effects of urban and agricultural lands and their drivers in three major urban agglomerations, China, namely Beijing-Tianjin-Hebei (BTHUA), Yangtze River Delta (YRDUA), and Pearl River Delta (PRDUA). Results showed that the daytime UHI effects were significant in all the urban agglomerations, with annual mean intensity larger than 3.2 ℃ for all the cities above prefecture-level, but the strongest did not occur in the core cities. The UHI effects reduced substantially at night, and even turned to be cold islands in some cities of the BTHUA and YRDUA. The agricultural lands also warmed the LST substantially during the daytime, especially in the BTHUA. Whereas they cooled the LST obviously at night for the BTHUA and YRDUA, with annual mean intensity of 2.3 ℃ and 0.7 ℃, respectively. Although the urban lands had greater warming potentials, the agricultural lands dominated the regional temperature changes due to their larger area share. The urban and agricultural lands together warmed the daytime LST in all urban agglomerations, with the greatest LST increase in the BTHUA (4.0 ℃), and cooled the LST at night for the BTHUA and YRDUA. Also, this study indicated that the surface thermal environmental effects of urban and agricultural lands varied greatly with the space and time. They were mainly controlled by vegetation activities, surface albedo, background climate, and population density. Our results provide important insights for formulating local land use strategies to mitigate climate change.