Snow cover is a crucial driver for plant species distributions in cold environments. The primary source of snow cover data used in distribution models is remotely sensed satellite imagery, which is characterized by coarser spatial resolutions than plot-scale observations of plant distributions. This scale-mismatch was hypothesized to limit model accuracy. Here, we used a common modeling framework to assess the contribution of snow melt-out dates derived from four data sources (satellite imagery, numerical snowpack modeling, webcam imagery and in-situ soil temperature measurements) at 1 m and 20 m spatial resolution to the predictive power of distribution models of 74 plant species in an alpine landscape of the Austrian Alps. We found that >80 % of the distribution models of all species were significantly improved by at least one snow melt-out data set when considering Area Under the Curve (AUC). Satellite-based melt-out led to significantly improved models for the highest number of species (>50 % for AUC) and increased True-Skill-Statistic and AUC on average by 16 % and 5 %, respectively. Surprisingly, fine-scale and in-situ measured melt-out data did not improve models more than the coarser scale (20 m) satellite-based melt-out data. Moreover, numerical snowpack modeling delivered results comparable to the other sources, which supports its use for projecting future species distributions. We conclude that the additional effort needed for producing high resolution, in-situ datasets as compared to commonly used satellite imagery might hence be worthwhile for some species but not for plant distribution modeling in cold ecosystems in general.

Imagery acquired by the Moderate-resolution Imaging Spectroradiometer (MODIS) provides a global archive of dailyNormalized Difference Snow Index (NDSI) at 500 m nominal resolution since the year 2000. While Sentinel-2 (S2) NDSI provides an increased spatial resolution of 20 m since the year 2015, the temporal resolution amounts to only 5 days and thus lacks the high temporal resolution of MODIS. Efforts to combine NDSI datasets for an increased temporal and spatial resolution have so far focused on the deriving binary snow cover maps or combining data from other sensors. In contrast, we produce fine scale (20 m) fractional snow cover (FSC) by downscaling MODIS NDSI to S2 resolution. Random forest regression predicts S2 NDSI based on dynamic features (MODIS NDSI, day-of-year) and static, topographic features for an alpine study site. Subsequently, FSC is derived from S2 NDSI. Cross-validation results in R² of 0.795 and RMSE of 0.155 for FSC and outperforms common resampling methods. Multi-annual S2 NDSI metrics are able to slightly improve model accuracy. Our results suggest that combining topographical data and low-resolution NDSI allows to produce daily, high-resolution S2 NDSI and FSC and improve fine scale characterization of snow cover dynamics in mountain landscapes.