Harder, PhillipPomeroy, JohnHelgason, Warren D.2023-11-012023-11-012019https://hdl.handle.net/10388/15195© Author(s) 2019. CC BY 4.0 License.Vegetation has a tremendous influence on snow processes and snowpack dynamics yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are lacking. Unmanned Aerial Vehicles (UAV) have had recent widespread application to capture high resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV Structure from Motion (SfM) and airborne lidar have focussed on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds, measure returns from a wide range of scan angles, and so have a greater likelihood of successfully sensing the sub-canopy snow depth. The effectiveness of UAV-lidar and UAV-SfM in mapping snow depth in both open and forested terrain was tested in a 2019 field campaign in the Canadian Rockies Hydrological Observatory, Alberta and at Canadian Prairie sites near Saskatoon, Saskatchewan, Canada. Only UAV-lidar could successfully measure the sub-canopy snow surface with reliable sub-canopy point coverage, and consistent error metrics (RMSE <0.17m and bias -0.03m to -0.13m). Relative to UAV-lidar, UAV-SfM did not consistently sense the sub-canopy snow surface, the interpolation needed to account for point cloud gaps introduced interpolation artefacts, and error metrics demonstrate relatively large variability (RMSE <0.33m and bias 0.08 m to -0.14m). With the demonstration of sub-canopy snow depth mapping capabilities a number of early applications are presented to showcase the ability of UAV-lidar to effectively quantify the many multiscale snow processes defining snowpack dynamics in mountain and prairie environments.enAttribution 2.5 Canadasub-canopy snow depthUnmanned Aerial Vehicles (UAV)UAV-lidarUAV Structure from Motion (SfM)Preprint