Fog is a kind of disastrous weather， which seriously affects traffic order and causes serious loss of life and property. The Himawari-8/AHI （H8/AHI） image with 10-minute time resolution， which provides the possibility for near-real-time fog detection， but the reflectivity of the image is greatly affected by the sun altitude angle during the day， and the conventional fog detection algorithms are difficult to adapt. This paper takes the differences in spectral characteristics and motion characteristics of clouds， fog and the surface between time series images as the starting point， uses time series images to synthesize clear sky surface， uses the background difference method to remove the surface， and uses the ratio of adjacent time series images to remove the fast-moving and rough-textured clouds in images， finally combined with the traditional cloud and fog separation algorithm to remove scattered and unseparated clouds in the image， realized the rapid detection of land fog in the daytime. The test results show that the algorithm can realize near real-time automatic daytime fog detection. The algorithm is applicable in the daytime from 9：00 to 15：00. The algorithm has high quantitative verification accuracy.The average probability of detection for 6 consecutive days in winter is 96.6%，the false alarm ratio is 9.4%，and the critical success index is 87.9%.The advantage of this algorithm is that the detection threshold is not affected by the angle of the sun's altitude. Compared with the existing detection algorithms of daytime land fog， this algorithm has higher detection accuracy. The false alarm ratio of fog detection results for 120 days from February to May in winter and spring is 3.6%，which proves that the algorithm has certain reliability and stability.