Night fog has become an important hidden danger of frequent traffic accidents. Night fog detection is of great significance to prevent and reduce accidents and losses caused by fog， and protect the safety of people's lives and property. There is an obvious boundary between fog and surface of clear sky in the nighttime brightness temperature difference image. Canny edge detection is used to obtain the edge mixed pixel， and the separation detection threshold is automatically obtained by the average brightness temperature difference value of the edge mixed pixel to detect the night land fog. The results of 5-day H8/AHI night fog detection show that the probability of detection is 93.3%， the false alarm ratio is 29.8% and the critical success index is 67.8%. The results show that the algorithm is more suitable for large area dense fog detection， and it is prone to false alarm for special weather such as haze， rainy and snowy days， and fog developing into low cloud. If there is no weather phenomenon associated with fog， the probability of detection is 94.6%， the false alarm ratio is 0.05%， and the critical success index is 90.1%. This algorithm can automatically determine the separation detection threshold of fog and clear sky surface. Compared with the existing automatic detection algorithms of night land fog， this algorithm has higher detection accuracy. The qualitative verification results of night land fog timing detection at different times from 17：00~07：00 on November 27， 2015 to December 1， 2015 show that the algorithm is suitable for the fog detection in the night area of the remote sensing image at dawn and dusk， which can detect about 90% of fog area； for the whole image at night， the algorithm can detect more than 90% of fog area. The results of qualitative verification further prove the stability and reliability of the algorithm.