snowMapper v1.0: a model for daily mountain snow cover reconstruction in high resolution

snowMapper is a physics-informed model developed to generate daily reconstructions of snow cover across complex mountain terrain. The system integrates in situ measurements with gridded meteorological inputs and incorporates binary snow presence/absence observations derived from high-resolution satellite imagery. Its modular design enables users to tailor configurations to specific study sites, producing daily snow-cover maps at spatial resolutions typically ranging from 20 to 100 meters. The workflow includes a preprocessing pipeline compatible with imagery from Landsat 4–9 and Sentinel-2; multiple terrain- and land-cover–based masking options (i.e., forest, glaciers, surface water, elevation, urban areas); five configurable schemes for converting satellite reflectance to binary snow cover; and a quasi–physically based downscaling of climate variables. Snow-cover reconstruction is accomplished through two sequential, configurable gap-filling procedures: an initial decision-tree step followed by a machine-learning classifier. The classifier can be trained either with local field observations or with in situ data originating from other regions, allowing the model to operate in a fully physics-informed mode in the absence of a local monitoring network. A built-in evaluation module compares model outputs with satellite-derived snow cover, providing accuracy metrics directly within the final product. Optional aggregation routines allow fractional snow-cover metrics to be generated across temporal and spatial scales. The system operates entirely on Google Earth Engine via its Python API, reducing dependence on local data storage and eliminating local computational demands. We applied snowMapper to generate a 41-year snow-cover climatology for Greek mountains exceeding 2,000 m a.s.l. The resulting daily 100 m climatology consisted of over 90 % modeled values, and achieved a mean overall accuracy of 93 % when assessed against 1.1 billion clear-sky, pixel-level satellite observations. The model code and example data are available as an open-source project on GitHub (https://github.com/snowMapper/snowMapper, last access: 25 November 2025).