Fine-scale seasonal snow depth observations can improve estimates of snow water equivalent at critical times of year. Airborne lidar is the current gold standard for snow depth measurement, but it involves high costs and relatively limited coverage. Using very-high-resolution satellite stereo images from WorldView-2, WorldView-3, and Pléiades-HR 1A/1B, we produced a six-year time series (2017–2022) of spatially continuous digital elevation models for an 874 km² study area over Grand Mesa, Colorado. We generated high-resolution stereo snow depth maps that capture intra- and interannual variability and span multiple anomalous years (58%–158% of median peak SNOTEL snow depth). Comparisons with near-contemporaneous airborne lidar snow depth measurements showed good agreement, with median offset of −0.13 m, precision of 0.19 m and accuracy of 0.31 m. Our results suggest that satellite stereo can provide snow depth observations with the spatiotemporal coverage needed to improve operational forecast models and inform adaptive management strategies.