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Remote Sensing Technology and Application  2022, Vol. 37 Issue (2): 408-415    DOI: 10.11873/j.issn.1004-0323.2022.2.0408
Automatic Detection of Night Land Fog based on H8/AHI Satellite Data
Huiyun Ma1(),Yanan Li1,Xiaojing Wu2,Yinze Ran1,Junjie Yan3()
1.Department of Surveying and Geo-informatics,Central South University,Changsha 410083,China
2.Nation Satellite Meteorological Center,Beijing 100080,China
3.Beijing Huayun Shinetek Science and Technology Co. ,Ltd. ,Beijing 100081,China
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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.

Key words:  Bright temperature difference      Canny edge detection      Nighttime fog detection      H8/AHI      Automatic     
Received:  26 October 2021      Published:  17 June 2022
ZTFLH:  P343.6  
Corresponding Authors:  Junjie Yan     E-mail:;
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Huiyun Ma
Yanan Li
Xiaojing Wu
Yinze Ran
Junjie Yan

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Huiyun Ma,Yanan Li,Xiaojing Wu,Yinze Ran,Junjie Yan. Automatic Detection of Night Land Fog based on H8/AHI Satellite Data. Remote Sensing Technology and Application, 2022, 37(2): 408-415.

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Fig.1  The study area
Fig.2  The max and min graph of bright temperature difference on cloud, fog and a surface with clear sky at different times of night
Fig.3  A statistical histogram and a first-order derivative image of the edge mixed pixel data
Fig.4  Automatic detection flow chart of mighttime land fog
Fig.5  Overlaying the result of observation station and the result of satellite fog detection at 20:00
Table 1  The index value of nighttime land fog automatic detection and references
Fig.6  Overlaying the sequential results of fog detection on false color image of H8/AHI
1 Eyre J R, Brownscombe J L, Allam R J. Detection of fog at night using Advanced Very High Resolution Radiometer(AVHRR) Imagery[J]. Meteorological Magazine,1984,113(1346):266-271.
2 Ellrod G P. Advances in the detection and analysis of fog at night using GOES multispectral infrared grain[J].Weather and Forecasting,1995,10(3):606-619.
3 Lee J R, Chung C Y, Ou M L. Fog detection using geostationary satellite data: temporally continuous algorithm[J]. Asia-Pacific Journal of Atmospheric Sciences,2011,47(2):113-122.
4 Zhang Shunqian, Yang Xiurong. Remote sensing monitoring technology of thick fog at night based on neural networks and fractal grain[J]. Journal of Applied Meteorological Science, 2005,16(6):804-810.
4 张顺谦,杨秀蓉.基于神经网络和分形纹理的夜间浓雾遥感检测技术[J].应用气象学报, 2005, 16(6):804-810.
5 Zhou Xuan, Zhou Xiaozhong, Wu Yaoping, et al. Detection of nighttime fog using MODIS data[J]. Geomatics and Information Science of Wuhan University, 2008, 33(6): 581-583.
5 周旋, 周晓中, 吴耀平, 等.利用MODIS数据监测夜间雾[J].武汉大学学报:信息科学版, 2008,33(6):581-583.
6 Ma Huiyun, Wang Zhao. The function of DEM in separating fog from cloud based on remote sensing image[J]. Remote Sensing for Land and Resources, 2010, 22(1): 55-59.
6 马慧云, 范冲, 赵向东.基于云雾与晴空地表混合像元的云雾检测算法[J].国土资源遥感, 2010, 22(1): 55-59..
7 Zhang Weikang, Ma Huiyun, Zou Zhengrong, et al. Based on SBDART radiative transfer model radiation fog at night automatically cetect the research and time series analysis[J]. Remote Sensing for Land and Resources, 2014, 26(2): 80-86.
7 张伟康,马慧云,邹峥嵘,等.基于SBDART辐射传输模型的夜间辐射雾自动检测及时间序列分析[J].国土资源遥感, 2014, 26(2): 80-86.
8 Zhang Weikang, Ma Huiyun, Zou Zhengrong, et al.Nighttime radiation fog detection and visibility retrieval based on SBDART and BP neural networks[J]. Journal of PLA University of Science and Technology (Natural Science Edition), 2014,15(2):197-202.
8 张伟康,马慧云,邹峥嵘,等.基于SBDART和BP的夜间雾遥感检测和能见度反演[J].解放军理工大学学报(自然科学版), 2014, 15(2):197-202.
9 Wen Xiongfei. The fesearch of dynamical detection method for radiation fog over Land based on remote sensing data[D]. Wuhan: Wuhan University, 2010.[文雄飞,陆地辐射雾遥感动态检测方法研究[D].武汉: 武汉大学,2010.]
10 Chen Wei, Yuan Zhikang, Zhou Hongmei,et al. The segmentation experiment of night fog using GMS-5 IR image[J]. Scientia Meteorologica Sinica, 2004,24(2):193-197.
10 陈伟,袁志康,周红妹,等. GMS-5红外图像上夜间雾的分离实验[J]. 气象科学, 2004, 24(2):193-197.
11 Du Juan, Li Wei, Zhang Penglin. Nighttime terrestrial radiation fog detection using time series remote sensing data[J]. Geomatics and Information Science of Wuhan University, 2019, 44(8): 1162-1168.
11 杜鹃,李维,张鹏林. 夜间陆地辐射雾的遥感时序数据检测[J].武汉大学学报(信息科学版),2019, 44(8):1162-1168.
12 Lu Hui. Research on fog identification based on Himawari 8 satellite remote sensing data[D]. Hefei:Anhui University,2019.
12 陆会. 基于葵花8卫星遥感数据的大雾识别研究[D]. 合肥:安徽大学,2019.
13 Xu Yun, Xu Ai Wen. Classification and detection of cloud, snow and fog in remote sensing images based on random forest[J].Remote Sensing for Land and Resources, 2021, 33(1): 96-101.
13 许赟,许艾文.基于随机森林的遥感影像云雪雾分类检测[J].国土资源遥感,2021,33(1):96-101.
14 Wang Liping, Chen Shaoyong, Dong Anxiang. The distribution and seasonal variations of fog in China[J].Acta Geographica Sinica,2005,60(4):689-697.
14 王丽萍,陈少勇,董安祥.中国雾区的分布及其季节变化[J].地理学报,2005,60(4):689-697.
15 Li Zihua. Studies of fog in China over the past 40 years[J]. Acta Meteorologica Sinica, 2001, 59(5): 616-623.
15 李子华.中国近40年来雾的研究[J].气象学报,2001,59(5):616-624.
16 Hunt G E. Radiative properties of terrestrial clouds at visible and infrared thermal wavelengths[J]. Quraterly Journal of the Royal Meteorological Society, 1973, 99(420): 346-369.
17 Zhang Lingyan. Adaptive edge-detection method research ba-sed on Canny algorithm[D]. Xi'an: Northwest University, 2009.
17 张玲艳.基于canny理论的自适应边缘检测方法研究[D]. 西安:西北大学, 2009.
18 Cermak J, Bendix J. Dynamical nighttime fog/low stratus detection based on meteosat SEVIRI data: A feasibility study[J]. Pure and Applied Geophysics, 2007, 164(7-8):1179-1192.
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[3] . [J]. Remote Sensing Technology and Application, 1994, 9(2): 67 -68 .
[4] . [J]. Remote Sensing Technology and Application, 2005, 20(1): 1 -5 .
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[8] . [J]. Remote Sensing Technology and Application, 1989, 4(2): 39 -43 .
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