<p>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 km<sup>2</sup>). The results were compared at multiple resolutions to the lidar-derived snow depth measurements from the Airborne Snow Observatory (ASO), collected simultaneously. Where snow was mapped by both ASO and SfM, the depths compared well, with a mean difference between −0.02 m and 0.03 m, NMAD of 0.22 m, and close snow volume agreement (+/−5 %). ASO mapped a larger snow area relative to SfM, with SfM missing ~14 % of total snow volume as a result. Analyzing the differences shows that challenges for SfM photogrammetry remain in vegetated areas, over shallow snow (< 1 m), and slope angles over 50 degrees. Our results indicate that capturing large scale snow depth and volume with airborne images and photogrammetry could be an additional viable resource for understanding and monitoring snow water resources in certain environments.</p>