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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- Measurement error in remotely sensed fractional snow cover datasets: implications for ecological research R. Jacques-Hamilton et al. 10.1088/2752-664X/ada8b3
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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
- Morphological indexes to describe snow-cover patterns in a high-alpine area L. Ferrarin et al. 10.1017/aog.2023.62
- Remote sensing of mountain snow from space: status and recommendations S. Gascoin et al. 10.3389/feart.2024.1381323
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- Performance Assessment of Four Data-Driven Machine Learning Models: A Case to Generate Sentinel-2 Albedo at 10 Meters H. Chen et al. 10.3390/rs15102684
- Fusing Climate Data Products Using a Spatially Varying Autoencoder J. Johnson et al. 10.1007/s13253-024-00657-3
- Spatio-temporal patterns and trends in MODIS-retrieved radiative forcing by snow impurities over the Western US from 2001 to 2022 A. Jensen et al. 10.1088/2752-5295/ad285a
- Detection of Winter Heat Wave Impact on Surface Runoff in a Periglacial Environment (Ny-Ålesund, Svalbard) R. Salzano et al. 10.3390/rs15184435
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- Evaluating the performance of the EUMETSAT H SAF H35 fractional snow-covered area product over the Tibetan Plateau S. Kuter et al. 10.53516/ajfr.1565569
- 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
- Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR) J. Tarricone et al. 10.5194/tc-17-1997-2023
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- 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: 21 Feb 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...
