Snow/Cloud Discrimination with Multispectral Satellite Measurements

Abstract

An algorithm is developed and evaluated for discriminating between clouds, snow-covered land and snow-free land in satellite image data. The multispectral technique uses daytime images of NOAA AVHRR channels 1 (0.63 μm), 3 (3.7 μm), and 4 (11.0 μm). Reflectance is derived for channel 3 by using the channel 4 emission temperature to estimate and remove the channel 3 thermal emission. Separation of clouds from snow and land is based primarily on the derived channel 3 reflectance. Observed reflectance in channel 3 is 0.02 to 0.04 for snow, 0.03 to 0.10 for land, 0.02 to 0.27 for ice clouds and 0.08 to 0.36 for liquid clouds. These ranges overlap for thin cirrus and snow, so the routine attempts analysis of cirrus based on differences in transmission between channels 3 and 4. Six case were analyzed and the total cloud cover was verified against a total of 110 surface observations in the standard categories of clear, scattered, broken and overcast. One of the cases is presented in detail to illustrate the algorithm procedures and results. Analysis of cloud cover from the satellite algorithm matched surface observations at 55% of the stations and was one category different at 33% of the stations. The algorithm differed from the surface observations by two categories at 9% of the stations and by three categories at 4% of the stations. A major remaining problem is discrimination between ice clouds and snow cover.