The fine scale distribution of snow is important for avalanche forecasting and biological refugia. Here, we test how historical context provided by 3 m snow cover observations derived from PlanetScope commercial satellite imagery could be used to downscale fractional snow covered area (fSCA) observed by the MODerate resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Harmonized Landsat and Sentinel-2 (HLS) product using previously published approaches. We evaluate this over Colorado and California montane meadows using 1) probabilistic snow cover maps, and 2) random forest machine learning models. We then compare versus a downscaling benchmark that relies only on terrain characteristics, eliminating the need for PlanetScope observations. Provided these three approaches, downscaling snow cover using random forest models performed best on average, largely because these models corrected annually consistent snow cover biases between PlanetScope and coarser resolution fSCA observations. The approach used to downscale influenced 3 m snow cover most, followed closely by the accuracy of the fSCA estimate that snow cover was downscaled from. Snow cover downscaled from HLS using only terrain indices was often similar or better than snow cover downscaled from MODIS and VIIRS using context from PlanetScope. This demonstrates how a limited historical record of commercial satellite observations can be used to estimate the fine-scale pattern of snow cover in many regions, but also when publicly accessible remote sensing retrievals and information about the terrain may obviate the need for commercial observations.