Snow water equivalent (SWE) measurements are crucial for calculating water balances and streamflow runoff, and estimating infiltration into soils. However, traditional snow survey measurements of SWE do not capture basin-wide variability, especially in mountain environments subject to snow redistribution by wind and vegetation. Aerial lidar data allows for the generation of high-resolution snow depth maps, but estimating SWE requires in situ density measurements, which are spatially limited and difficult to collect. This study evaluates three methods for interpolating seasonal snow density at a basin-wide scale using snow surveys and aerial lidar scans collected in the Canadian Rockies. High-resolution snow depth maps were generated from aerial lidar by subtracting bare ground elevation from snow-on elevation. Manual snow depth and density measurements were collected in coordination with drone flights from five transects corresponding to five land cover types: forest clearing, alpine herbaceous vegetation, dense needleleaf forest, and north and south-facing sparse needleleaf forest. Methods of interpolating seasonal snow density include a simple method of calculating the average basin-wide density, a stratified method that calculates the average density by land cover type assuming vegetation and wind exposure impact densification, and a method assuming densification to be a function of snow depth, which applies statistical associations between depth and density. Results show the impact of varying interpolations of snow density on the volumetric water content of a basin-scale snowpack. This research provides valuable insights for improving high-resolution SWE estimation from aerial snow depth observations in mountain environments.