Snow-covered surface albedo varies depending on many factors, including snow grain size, snow cover thickness, snow age, forest shading factor, etc., and its parameterization is still under great uncertainty. For the snow-covered surface condition, albedo of forest is typically lower than that of short vegetation; thus snow albedo is dependent on the spatial distributions of characteristic land cover and on the canopy density and structure. In the Noah land surface model with multiple physics options (Noah-MP), almost all vegetation types in East Asia during winter have the minimum values of leaf area index (LAI) and stem area index (SAI), which are too low and do not consider the vegetation types. Because LAI and SAI are represented in terms of photosynthetic activeness, stem and trunk in winter are not well represented with only these parameters. We found that such inadequate representation of the vegetation effect is mainly responsible for the large positive bias in calculating the winter surface albedo in the Noah-MP. In this study, we investigated the vegetation effect on the snow-covered surface albedo from observations and improved the model performance by implementing a new parameterization scheme. We developed new parameters, called leaf index (LI) and stem index (SI), which properly manage the effect of vegetation structure on the snow-covered surface albedo. As a result, the Noah-MP's performance in the winter surface albedo has significantly improved - the root mean square error is reduced by approximately 69 %.