Articles | Volume 16, issue 5
https://doi.org/10.5194/tc-16-1765-2022
© Author(s) 2022. 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-16-1765-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Divergence of apparent and intrinsic snow albedo over a season at a sub-alpine site with implications for remote sensing
Earth Research Institute, University of California, Santa Barbara, CA 93106,
USA
Jeff Dozier
Bren School of Environmental Science & Management, University of
California, Santa Barbara, CA 93106, USA
Charles Stern
Lamont-Doherty Earth Observatory, Palisades, NY 10964, USA
Adam LeWinter
Cold Regions Research and Engineering Laboratory, Hanover, NH 03755, USA
Karl Rittger
Institute of Arctic and Alpine Research, University of Colorado,
Boulder, Boulder, CO 80309, USA
Earth Research Institute, University of California, Santa Barbara, CA 93106,
USA
Alexandria Savagian
Bowdoin College, Brunswick, ME 04011, USA
Timbo Stillinger
Earth Research Institute, University of California, Santa Barbara, CA 93106,
USA
Robert E. Davis
Cold Regions Research and Engineering Laboratory, Hanover, NH 03755, USA
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Cited
12 citations as recorded by crossref.
- Bridging the gap between airborne and spaceborne imaging spectroscopy for mountain glacier surface property retrievals C. Donahue et al. 10.1016/j.rse.2023.113849
- How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction? E. Bair et al. 10.5194/tc-17-2629-2023
- Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements B. Wilder et al. 10.5194/tc-18-5015-2024
- Brief communication: Not as dirty as they look, flawed airborne and satellite snow spectra E. Bair et al. 10.5194/tc-19-2315-2025
- Evaluation of E3SM land model snow simulations over the western United States D. Hao et al. 10.5194/tc-17-673-2023
- Error and Uncertainty Degrade Topographic Corrections of Remotely Sensed Data J. Dozier et al. 10.1029/2022JG007147
- Assessment of methods for mapping snow albedo from MODIS R. Palomaki et al. 10.1016/j.rse.2025.114742
- Computationally Efficient Retrieval of Snow Surface Properties From Spaceborne Imaging Spectroscopy Measurements Through Dimensionality Reduction Using K-Means Clustering B. Wilder et al. 10.1109/JSTARS.2024.3386834
- A new approach to net solar radiation in a spatially distributed snow energy balance model to improve snowmelt timing J. Meyer et al. 10.1016/j.jhydrol.2024.131490
- Remote sensing of mountain snow from space: status and recommendations S. Gascoin et al. 10.3389/feart.2024.1381323
- Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets T. Stillinger et al. 10.5194/tc-17-567-2023
- Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau D. Hao et al. 10.5194/gmd-16-75-2023
12 citations as recorded by crossref.
- Bridging the gap between airborne and spaceborne imaging spectroscopy for mountain glacier surface property retrievals C. Donahue et al. 10.1016/j.rse.2023.113849
- How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction? E. Bair et al. 10.5194/tc-17-2629-2023
- Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements B. Wilder et al. 10.5194/tc-18-5015-2024
- Brief communication: Not as dirty as they look, flawed airborne and satellite snow spectra E. Bair et al. 10.5194/tc-19-2315-2025
- Evaluation of E3SM land model snow simulations over the western United States D. Hao et al. 10.5194/tc-17-673-2023
- Error and Uncertainty Degrade Topographic Corrections of Remotely Sensed Data J. Dozier et al. 10.1029/2022JG007147
- Assessment of methods for mapping snow albedo from MODIS R. Palomaki et al. 10.1016/j.rse.2025.114742
- Computationally Efficient Retrieval of Snow Surface Properties From Spaceborne Imaging Spectroscopy Measurements Through Dimensionality Reduction Using K-Means Clustering B. Wilder et al. 10.1109/JSTARS.2024.3386834
- A new approach to net solar radiation in a spatially distributed snow energy balance model to improve snowmelt timing J. Meyer et al. 10.1016/j.jhydrol.2024.131490
- Remote sensing of mountain snow from space: status and recommendations S. Gascoin et al. 10.3389/feart.2024.1381323
- Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets T. Stillinger et al. 10.5194/tc-17-567-2023
- Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau D. Hao et al. 10.5194/gmd-16-75-2023
Latest update: 13 Jul 2025
Short summary
Understanding how snow and ice reflect solar radiation (albedo) is important for global climate. Using high-resolution topography, darkening from surface roughness (apparent albedo) is separated from darkening by the composition of the snow (intrinsic albedo). Intrinsic albedo is usually greater than apparent albedo, especially during melt. Such high-resolution topography is often not available; thus the use of a shade component when modeling mixtures is advised.
Understanding how snow and ice reflect solar radiation (albedo) is important for global climate....