Detecting Arctic Snow And Ice Cover With FY-1D Global Data

China meteorological satellite FY-1D can obtain global data (GDPT) from four spectral channels which include visible channel and infrared channels. This paper presents the format of GDPT data and the methods of the data processing, including data calibrating and projection transformation etc. According to the experts' experience, 2366 snow and ice samples, 2024 cloud samples, 1602 land samples and 1648 water samples were selected randomly from arctic imageries. The analysis of theory and samples verifies that only utilizing GDPT data can identify land, water, cloud and snow-ice by their following spectral features and textural features:(1) Clouds and snow/ice surface features have similar characteristics In both the visible and infrared spectral ranges, but the difference of channel 1 and channel 2 can effective for snow/ice identification.(2) The reflectance of land is less than 50%, and the value of ch1-ch2 is less than 0%.(3) The reflectance of water is less than 20%, and the value of ch1-ch2 is larger than 0% and less than or equal 10%.According to the spectral features described above, we can set a simple threshold method to exclude land, water, and some parts of clouds. According to the textual features described above, we can use bayes classifier to class clouds and snow-ice. By synthesizing five days class result of arctic snow and ice cover area, we can get complete arctic snow and ice cover area. The result agrees with NOAA/NESDIS IMS products up to 70%.The second generation of China orbit meteorological satellite FY-3 will be launched in 2007. By using FY-1D data received by Sweden station to test this method, we can estimate that this method can also be valuable to the application of FY-3 polar data.