Articles | Volume 17, issue 2
https://doi.org/10.5194/tc-17-567-2023
https://doi.org/10.5194/tc-17-567-2023
Research article
 | 
08 Feb 2023
Research article |  | 08 Feb 2023

Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets

Timbo Stillinger, Karl Rittger, Mark S. Raleigh, Alex Michell, Robert E. Davis, and Edward H. Bair

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Cited articles

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Adams, J. B., Smith, M. O., and Johnson, P. E.: Spectral Mixture Modeling – a New Analysis of Rock and Soil Types at the Viking Lander-1 Site, J. Geophys. Res.-Sol. Ea., 91, 8098–8112, https://doi.org/10.1029/JB091iB08p08098, 1986. 
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Ault, T. W., Czajkowski, K. P., Benko, T., Coss, J., Struble, J., Spongberg, A., Templin, M., and Gross, C.: Validation of the MODIS snow product and cloud mask using student and NWS cooperative station observations in the Lower Great Lakes Region, Remote Sens. Environ., 105, 341–353, https://doi.org/10.1016/j.rse.2006.07.004, 2006. 
Bair, E. and Stillinger, T.: SPIReS: Western USA snow cover and snow surface properties, water years 2001–2021, UCSB [data set], https://doi.org/10.21424/R4H05T, 2022. 
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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.