Abstract. Time series mapping of water held as snow in the mountains at global scales is an unsolved challenge to date. In a few locations, lidar-based airborne campaigns have been used to provide valuable data sets that capture snow distribution in near real-time over multiple seasons. Here, an alternative method is presented to map snow depth and quantify snow volume using aerial images and Structure from Motion (SfM) photogrammetry over an alpine watershed (300 km2). The results were compared to the lidar-derived snow depth measurements from the Airborne Snow Observatory, collected simultaneously. Where snow was mapped by both ASO and SfM, the depths compared well, with a mean difference of 0.01 m, NMAD of 0.22 m, and snow volume agreement (difference 1.26 %). ASO though, mapped a larger snow area relative to SfM, with SfM missing ~14 % of total snow volume as a result. Analyzing the SfM reconstruction errors shows that challenges for photogrammetry remain in vegetated areas, over shallow snow (
Mapping snow depth and volume at the alpine watershed scale from
aerial imagery using Structure from Motion
