Automated snow maps over North America have been produced at the National Environmental Satellite Data and Information Service (NESDIS) of the National Oceanic and Atmospheric Administration (NOAA) since 1999. The developed snow-mapping system is based on observations in the visible, middle infrared, infrared, and microwave spectral bands from operational geostationary and polar orbiting meteorological satellites and generates daily maps of snow cover at a spatial resolution of 4 km. Recently, the existing snow-mapping technique was extended to derive the fractional snow cover. To obtain snow fraction, we use measurements of the Imager instrument on board Geostationary Operational Environmental Satellite (GOES). The algorithm treats every cloud-clear image pixel as a “mixed scene” consisting of a combination of snow-covered and snow-free land surface. To determine the portion of the pixel that is covered with snow, we employ a linear mixture approach, which relies on the Imager measurements in the visible spectral band. The estimated accuracy of subpixel snow fraction retrievals is about 10%. In this paper, we present a description of the snow cover and snow fraction mapping algorithms. Application of the developed algorithms over North America for three winter seasons from 1999–2000 to 2001–2002 has shown that the spatial distribution of the fractional snow cover over areas affected by seasonal snow closely corresponds to the distribution of the forest cover. The fraction of snow in the middle of the winter season generally varied from 100% over croplands, grasslands, and other nonforested areas to 20–30% over dense boreal forests. The snow fraction over dense boreal forests exhibited a slight intraseason variability; however, no obvious correlation of these changes with snowfalls was noticed. Over areas with no or sparse tree vegetation cover (croplands, grasslands), snow fraction showed a noticeable correlation with snow depth for snow depths up to 35–40 cm.