Study Of Algorithm Of Detection Of Daytime Sea Fog Using FY-2 Geostationary Satellite Data

Leading to the sea and coastal areas visibility reduction, sea fog has adverse effects for navigation, human’s life, the marine fishery production and military action, which is an important cause of various accidents. Due to lack of ground measurement station, detecting sea fog in real-time and large-scale by traditional measurement is impossible. Satellite remote sensing technology with the advantages of real-time, dynamic, covering a large range and reliable, can detect sea fog in real-time and large-scale. Especially, geostationary satellite imagery data with the advantage of high time resolution, can realize continuous observation of sea fog. In this paper, the method of detection of daytime sea fog using FY-2 geostationary Satellite data is base on the spectral characteristics of clouds, fog and sea surface,which realizes automatic detection of sea fog on the China seas. The measured data and the polar-orbiting satellites FY-3B fog product data are used to verify the accuracy of test results. Some significant results are reveal as follows:1. There is a large difference between clouds/fog and sea surface in reflectance of visible band. Usually, clouds/fog reflectance is significantly higher than the sea surface, which indicates that the reflectance of visible band can be used to distinguish clouds/fog from sea surface. Due to the height of middle and high level clouds is higher than low clouds and fog, the middle/high level clouds brightness temperature is lower than low clouds and fog. So the height can be used to separate the middle and high level clouds from low-level clouds and fog. In the mid-infrared band, the reflectance of clouds/fog is mainly determined by the size of small water droplets or ice crystals. The decreasing of particle size, increases the reflectivity. The sea fog particles size is usually the smallest, so its reflectivity is the biggest. This spectral characteristic can be used to discriminate sea fog from low-level cloud.2. The dynamic threshold cloud detection method, proposed by Alan, is revised in the paper. The histogram smooth spacing and least-squares fitting order are automatically determined in ensuring the relatively optimal R-squared. The statistical histogram of visible channel reflectance is fitted by using the least square method, which makes the data smoother and the obtained dynamic threshold more accurate. Besides, it avoids errors caused by some unusual data, but increases calculated amount and processing time.3. Based on the spectral radiation and spatial textures characteristics of clouds, fog and sea surface, an algorithm of daytime sea fog detection is proposed, which uses cloud detection, cloud top height test, fog distinguishing index, thin low cloud detection index and smooth and stable index to distinguish clouds/fog from sea surface, separate mid- and high level clouds from low-level clouds and fog and discriminate sea fog from low-level cloud step by step, and realizes automatic detection of sea fog eventually. The measured data provided by the Guangdong Institute of Tropical and Marine Meteorology and polar-orbiting satellites FY-3B fog production issued by the China Meteorological Administration’s National Satellite Meteorological Center are used to validate the detection results. The result shows that the fog detection method in this paper is feasible. The sea fog case study on April 8, 2014 and March 26,2014, indicate that the dynamic monitoring of the sea fog can be realized by using the high time frequency of geostationary satellite.