Articles | Volume 20, issue 7
https://doi.org/10.5194/tc-20-3759-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-3759-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Jul 2026
Research article |
| 03 Jul 2026
| 03 Jul 2026
Mapping daily snow depth with machine learning and airborne lidar across two contrasting snowpacks
Caleb G. Pan
CORRESPONDING AUTHOR
Geospatial Research Laboratory, Engineer Research and Development Center, US Army Corps of Engineers, Alexandria, VA, 22315, USA
Jeremy Johnston
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire,Durham, NH, USA
Jennifer M. Jacobs
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire,Durham, NH, USA
Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, USA
Shad O'Neel
Cold Regions Research Engineering Laboratory, Engineer Research and Development Center, US Army Corps of Engineers, Boise, ID, USA
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Manuscript not accepted for further review
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Short summary
We developed a simple method to turn a few airborne snow-mapping flights and one daily snow record into continuous maps showing how snow depth changes each day. Tested in Idaho and New Hampshire, the approach works well in both deep and shallow snow regions and helps plan when and how often to fly lidar surveys for the best results.
We developed a simple method to turn a few airborne snow-mapping flights and one daily snow...
