Arctic sea ice, cloud, water, and lead classification using neural networks and 1.6-/spl mu/m data

Polar sea ice plays a critical role in regulating the global climate. Seasonal variation in sea ice extent, however, coupled with the difficulties associated with in situ observations of polar sea ice, makes remote sensing the only practical way to estimate this important climatic variable on the space and time scales required. Unfortunately, accurate retrieval of sea ice extent from satellite data is a difficult task. Sea ice and high cold clouds have similar visible reflectance, but some other types of clouds can appear darker than sea ice. Moreover, strong atmospheric inversions and isothermal structures, both common in winter at some polar locations, further complicate the classification. This paper uses a combination of feed-forward neural networks and 1.6-/spl mu/m data from the new Chinese Fengyun-1C satellite to mitigate these difficulties. The 1.6-/spl mu/m data are especially useful for detecting illuminated water clouds in polar regions because 1) at 1.6 /spl mu/m, the reflectance of water droplets is significantly higher than that of snow or ice and 2) 1.6-/spl mu/m data are unaffected by atmospheric inversions. Validation data confirm the accuracy of the new classification technique. Application to other sensors with 1.6-/spl mu/m capabilities also is discussed.