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Mountain snowpack provides critical water resources for forest and meadow ecosystems that are experiencing rapid change due to global warming. An accurate characterization of snowpack heterogeneity in these ecosystems requires snow cover observations at high spatial resolutions, yet most existing snow cover datasets have a coarse resolution. To advance our observation capabilities of snow cover in meadows and forests, we developed a machine learning model to generate snow-covered area (SCA) maps from PlanetScope imagery at about 3-m spatial resolution. The model achieves a median F1 score of 0.75 for 103 cloud-free images across four different sites in the Western United States and Switzerland. It is more accurate (F1 score = 0.82) when forest areas are excluded from the evaluation. We further tested the model performance across 7,741 mountain meadows at the two study sites in the Sierra Nevada, California. It achieved a median F1 score of 0.83, with higher accuracy for larger and simpler geometry meadows than for smaller and more complexly shaped meadows. While mapping SCA in regions close to or under forest canopy is still challenging, the model can accurately identify SCA for relatively large forest gaps (i.e., 15m < DCE < 27m), with a median F1 score of 0.87 across the four study sites, and shows promising accuracy for areas very close (>10m) to forest edges. Our study highlights the potential of high-resolution satellite imagery for mapping mountain snow cover in forested areas and meadows, with implications for advancing ecohydrological research in a world expecting significant changes in snow.