Global warming and the urban heat island effect exacerbate excessive heat in urban environments, adversely impacting human health. Effective urban planning can mitigate these effects by influencing land surface temperature (LST). While previous studies have examined the influence of urban features on LST across seasons and between day and night, detailed hour-by-hour comparisons remain unexplored. This study addresses this gap by analyzing the hourly impacts of various urban features on LST in 30 U.S. cities using geostationary weather satellite data. We employed cloud-based analytics and machine learning techniques to aggregate data from thousands of images, identifying the relative importance and correlation curve of each urban feature on LST at each hour. Our findings revealed two distinct correlation patterns: dynamic daytime patterns with significant hourly variability and stable nocturnal patterns with minimal hourly differences. These results demonstrate that sunlight intensity greatly affects the correlation between urban features and LST. Urban planners should therefore consider broader patterns rather than focusing on specific hours. These insights provide valuable guidance for landscape and urban planners in developing strategies for climate adaptation and heatwave mitigation, contributing to the growing body of literature on sustainable cities.