Characterizing the spatial distribution of field-scale snowpack using unpiloted aerial system (UAS) lidar and structure-from-motion (SfM) photogrammetry

Cho, Eunsang; Verfaillie, Megan; Jacobs, Jennifer M.; Hunsaker, Adam G.; Sullivan, Franklin B.; Palace, Michael; Wagner, Cameron

doi:https://doi.org/10.5194/hess-29-4539-2025

Articles | Volume 29, issue 18

https://doi.org/10.5194/hess-29-4539-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-29-4539-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 29, issue 18

Research article

|

22 Sep 2025

Research article |

| 22 Sep 2025

Characterizing the spatial distribution of field-scale snowpack using unpiloted aerial system (UAS) lidar and structure-from-motion (SfM) photogrammetry

Eunsang Cho, Megan Verfaillie, Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner

Supplement

https://doi.org/10.5194/hess-29-4539-2025-supplement

Data sets

Data for: Characterizing spatial distribution of field-scale snowpack using unpiloted aerial system (UAS) lidar and structure-from-motion (SfM) photogrammetry E. Cho et al. https://doi.org/10.5281/zenodo.17129072

U.S. Climate Reference Network Processed Data from USCRN Database Version 2 [stationId=1040] M. A. Palecki et al. https://doi.org/10.7289/V5MS3QR9

Short summary

Unpiloted aerial system (UAS) lidar and structure-from-motion (SfM) photogrammetry effectively map high-resolution snow depths. Our study found that UAS lidar outperformed SfM, particularly in capturing stable snow distribution patterns. Vegetation type was the primary factor influencing snow depth across forest and field areas, reflecting soil variables, such as organic matter. When analyzed separately, slope and forest canopy shadowing played key roles.