MODIS, the moderate resolution imaging spectroradiometer, will be launched in 1998 as part of the ®rst earth observing system (EOS) platform. Global maps of land surface properties, including snow cover, will be created from MODIS imagery. The MODIS snow-cover mapping algorithm that will be used to produce daily maps of global snow cover extent at 500 m resolution is currently under development. With the exception of cloud cover, the largest limitation to producing a global daily snow cover product using MODIS is the presence of a forest canopy.
A Landsat Thematic Mapper (TM) time-series of the southern Boreal Ecosystem±Atmosphere Study (BOREAS) study area in Prince Albert National Park, Saskatchewan, was used to evaluate the performance of the current MODIS snow-cover mapping algorithm in varying forest types. A snow re¯ectance model was used in conjunction with a canopy re¯ectance model (GeoSAIL) to model the re¯ectance of a snow-covered forest stand. Using these coupled models, the eects of varying forest type, canopy density, snow grain size and solar illumination geometry on the performance of the MODIS snow-cover mapping algorithm were investigated.
Using both the TM images and the re¯ectance models, two changes to the current MODIS snow-cover mapping algorithm are proposed that will improve the algorithm's classi®cation accuracy in forested areas. The improvements include using the normalized dierence snow index and normalized dierence vegetation index in combination to discriminate better between snow-covered and snow-free forests. A minimum albedo threshold of 10% in the visible wavelengths is also proposed. This will prevent dense forests with very low visible albedos from being classi®ed incorrectly as snow. These two changes increase the amount of snow mapped in forests on snow-covered TM scenes, and decrease the area incorrectly identi®ed as snow on nonsnow-covered TM scenes.