The utilization of ground observation stations for land fog monitoring is constrained by station distribution, which results in incomplete and nonuniform coverage of actual conditions. This problem can be overcome by using geostationary meteorological satellite multichannel observation images and semantic segmentation to recognize fog areas. Existing methods, however, rely on a large number of annotated satellite images and fail to make full use of other auxiliary information, such as the ground observation station data. The segment anything model (SAM) proposed by Meta AI can use prompts to guide segmentation, making it possible to use data from multiple sources to undertake land fog recognition tasks. In this letter, we proposed LF-SAM, a method designed to automatically generate point prompts derived from ground observation station data and extract high-frequency features, thereby aiding in the segmentation of satellite images. We created a dedicated dataset named FY-OBS for land fog recognition, including 300 training sets and 60 test sets, and validated our method on it. Our method reduces the amount of fully annotated data required and achieves significantly better performance than prompt-free methods. The mean intersection over union (mIoU) and average accuracy (aAcc) of LF-SAM reach 73.99% and 95.24%, which were increased by 3.05% and 0.93%, respectively, compared with the method without prompts.