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

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

doi:https://doi.org/10.5194/tc-17-567-2023

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.

https://doi.org/10.5194/tc-17-567-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 2

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

Supplement

https://doi.org/10.5194/tc-17-567-2023-supplement

Data sets

SPIReS: Western USA snow cover and snow surface properties, water years 2001-2021 Edward Bair and Timbo Stillinger https://doi.org/10.21424/R4H05T

SPIReS: Landsat 8 snow cover and snow surface properties co-incident with 3 m LiDAR from the Airborne Snow Observatory Timbo Stillinger and Edward Bair https://doi.org/10.21424/R4C62H

Snow cover from spectral mixture analysis algorithm SCAG: OLI and MODIS Karl Rittger https://doi.org/10.5281/zenodo.7510861

The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo (https://nsidc.org/data/aso) T. H. Painter, D. F. Berisford, J. W. Boardman, K. J. Bormann, J. S. Deems, F. Gehrke, A. Hedrick, M. Joyce, R. Laidlaw, D. Marks, C. Mattmann, B. McGurk, P. Ramirez, M. Richardson, S. M. Skiles, F. C. Seidel, and A. Winstral https://doi.org/10.1016/j.rse.2016.06.018

The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo (https://data.airbornesnowobservatories.com/) T. H. Painter, D. F. Berisford, J. W. Boardman, K. J. Bormann, J. S. Deems, F. Gehrke, A. Hedrick, M. Joyce, R. Laidlaw, D. Marks, C. Mattmann, B. McGurk, P. Ramirez, M. Richardson, S. M. Skiles, F. C. Seidel, and A. Winstral https://doi.org/10.1016/j.rse.2016.06.018

VIIRS/NPP CGF Snow Cover Daily L3 Global 375m SIN Grid, Version 1 G. Riggs, D. K. Hall, and M. O. Román https://doi.org/10.5067/VIIRS/VNP10A1F.001

MODIS/Terra CGF Snow Cover Daily L3 Global 500m SIN Grid, Version 61 D. K. Hall and G. A. Riggs https://doi.org/10.5067/MODIS/MOD10A1F.061

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.