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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- Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas M. Lalande et al. 10.5194/tc-17-5095-2023
- Intercomparison of snow water equivalent products in the Sierra Nevada California using airborne snow observatory data and ground observations K. Yang et al. 10.3389/feart.2023.1106621
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- Detection of Winter Heat Wave Impact on Surface Runoff in a Periglacial Environment (Ny-Ålesund, Svalbard) R. Salzano et al. 10.3390/rs15184435
- Mathematically Improved XGBoost Algorithm for Truck Hoisting Detection in Container Unloading N. Wu et al. 10.3390/s24030839
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- Combining Daily Sensor Observations and Spatial LiDAR Data for Mapping Snow Water Equivalent in a Sub‐Alpine Forest J. Geissler et al. 10.1029/2023WR034460
- MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022) F. Pan et al. 10.5194/essd-16-2501-2024
- Computationally Efficient Retrieval of Snow Surface Properties From Spaceborne Imaging Spectroscopy Measurements Through Dimensionality Reduction Using k-Means Spectral 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
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- Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR) J. Tarricone et al. 10.5194/tc-17-1997-2023
3 citations as recorded by crossref.
- 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
- An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales L. Keuris et al. 10.3390/rs15051231
- High-resolution mapping of snow cover in montane meadows and forests using Planet imagery and machine learning K. Yang et al. 10.3389/frwa.2023.1128758
Latest update: 20 Nov 2024
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...
