snowman: an open-source R package for automated 30-m snow and ice cover mapping using the Landsat archive

Abstract. Seasonal snow and ice cover are critical components of the cryosphere yet mapping their dynamics at ecologically relevant spatiotemporal scales remains challenging. Here I present snowman, an open-source R package and algorithm for automated mapping of snow and ice cover dynamics at 30-m resolution using Landsat satellite imagery (1982–present). The algorithm combines globally trained probabilistic Random Forest classifiers with pixel-wise generalised additive models to estimate snow phenology metrics—including snow cover duration, snowmelt timing, and new-snow onset—across any location on Earth, without requiring specialist expertise in remote sensing. Trained on 691,925 manually labelled points from 529 Landsat scenes across 49 globally distributed sites, the classifier achieved an overall accuracy of 96.3 % on an independent 15,000-point test dataset, compared to 80.0 % for traditional normalised difference snow index-based (NDSI) approaches. Critically, snowman retained up to 2.2 times more usable observations than NDSI methods across a cloud-prone mountain landscape, enabling more detailed estimation of the snow dynamics. At two Finnish weather stations, snowman estimated snow cover duration, snowmelt timing, and new-snow onset to within 3–11 days of multi-year station records. Snow phenology maps showed strong spatial correspondence with independent fine-scale satellite-borne snow classifications (Pearson r = 0.79–0.83) and a high-resolution microclimate dataset (r = 0.82). The snowman algorithm is fully automated and scalable from personal computers to high-performance computing environments and offers a reproducible tool for snow and ice monitoring in climate science, hydrology, and ecological research.