We combined field-based snow depth and snow water equivalent (SWE) measurements, remote sensing data, and machine learning to estimate snow depth and SWE over a 10 km² local scale area in Sodankylä, Finland. Associations were found for snow depth and SWE with carbon- and mineral-based forest surface soils, alongside dry and wet peatbogs. This approach to upscale field-based snow depth and SWE measurements to a local scale can be used in regions that regularly experience snowfall.