With passive microwave snow data the most prominent error feature is the underestimation of snow mass during the winter, especially for North America. The reason why the microwave data underestimates snow mass primarily has to do with the effects of vegetation above snow fields. With the microwave data the emissivity of trees, especially dense conifers, can overwhelm the scattering signal which results when upwelling microwave energy is redistributed by snow crystals. The boreal forests which stretch across the northern tier of North America are perhaps the physiographic region where most of the difference occurs between the snow depth measurements based on climatological data and those based on microwave observations. Forests not only absorb some of the radiation scattered by snow crystals, but trees are also emitters of microwave radiation. So in forested areas the signal received by a radiometer on-board a satellite is produced by a combination of media. Generally, the denser the forest, the higher the microwave brightness temperature despite the type and condition of the media underlying the forest canopy. Furthermore, because the canopy shields the snow from direct solar radiation the deepest snow accumulates in the densest forests. However, if the fractional forest cover of a given microwave pixel can be accounted for in some way then microwave algorithms can be modified by including a forest cover parameter and estimates of snow depth will be improved. In this study we have used a vegetation index, derived from satellite brightness data, as an indicator of forest cover, and preliminary results show that this refined algorithm compares more favorably with climatological snow depths.