Articles | Volume 17, issue 2
https://doi.org/10.5194/tc-17-567-2023
© Author(s) 2023. 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-17-567-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Feb 2023
Research article |
| 08 Feb 2023
| 08 Feb 2023
Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets
Earth Research Institute, University of California at Santa Barbara,
Santa Barbara, CA 93106, USA
Karl Rittger
Earth Research Institute, University of California at Santa Barbara,
Santa Barbara, CA 93106, USA
Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO 80309, USA
Mark S. Raleigh
College of Earth, Ocean, and Atmospheric Sciences, Oregon State
University, Corvallis, OR 97331, USA
Alex Michell
Earth Research Institute, University of California at Santa Barbara,
Santa Barbara, CA 93106, USA
Robert E. Davis
Cold Regions Research and Engineering Laboratory, Hanover, NH 03755, USA
Edward H. Bair
Earth Research Institute, University of California at Santa Barbara,
Santa Barbara, CA 93106, USA
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- Which global reanalysis dataset has better representativeness in snow cover on the Tibetan Plateau? S. Yan et al. 10.5194/tc-18-4089-2024
- Advances in image-based estimation of snow variable: A systematic literature review on recent studies G. Demil et al. 10.1016/j.jhydrol.2025.132855
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- Remote sensing of mountain snow from space: status and recommendations S. Gascoin et al. 10.3389/feart.2024.1381323
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Latest update: 27 May 2025
Short summary
Understanding global snow cover is critical for comprehending climate change and its impacts on the lives of billions of people. Satellites are the best way to monitor global snow cover, yet snow varies at a finer spatial resolution than most satellite images. We assessed subpixel snow mapping methods across a spectrum of conditions using airborne lidar. Spectral-unmixing methods outperformed older operational methods and are ready to to advance snow cover mapping at the global scale.
Understanding global snow cover is critical for comprehending climate change and its impacts on...
