15 Aug 2022
15 Aug 2022
Status: this preprint is currently under review for the journal TC.

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

Timbo Stillinger1, Karl Rittger1,2, Mark S. Raleigh3, Alex Michell1, Robert E. Davis4, and Edward H. Bair1 Timbo Stillinger et al.
  • 1Earth Research Institute, University of California at Santa Barbara, Santa Barbara, CA 93106, USA
  • 2Institute of Arctic and Alpine Research, University of Colorado at Boulder, Boulder, CO 80309, USA
  • 3College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA
  • 4Cold Regions Research and Engineering Laboratory, Hanover, NH 03755, USA

Abstract. Snow cover mapping algorithms utilizing multispectral satellite data at various spatial resolutions are available, each treating subpixel variation differently. Past evaluations of snow mapping accuracy typically relied on satellite data collected at a higher spatial resolution than the data in question. However, these optical data cannot characterize snow cover mapping performance under forest canopies or at the meter scale. Here, we use 3 m spatial resolution snow depth maps collected on 116 days by an aerial laser scanner to validate band ratio and spectral mixture snow cover mapping algorithms. Such a comprehensive evaluation of sub-canopy snow mapping performance has not been undertaken previously. The following standard (produced operationally by an agency) products are evaluated: NASA gap-filled Moderate-resolution Imaging Spectroradiometer (MODIS) MOD10A1F, NASA gap-filled Visible Infrared Imaging Radiometer Suite (VIIRS) VNP10A1F, and USGS Landsat 8 Level-3 Fractional Snow Covered Area. Two spectral unmixing approaches are also evaluated: Snow Covered Area and Grain size (SCAG) and Snow Property Inversion from Remote Sensing (SPIReS), both of which are gap-filled MODIS products and are also run on Landsat 8. We assess subpixel snow mapping performance while considering the fractional snow covered area (fSCA), canopy cover, sensor zenith angle, and other variables within six global seasonal snow classes. Metrics are calculated at the pixel and basin scales, including the root-mean-square error (RMSE), bias, and F statistic (a detection measure). The newer MOD10A1F Version 61 and VNP10A1F Version 1 product biases (-7.1 %, -9.5 %) improve significantly when linear equations developed for older products are applied (2.8 %, -2.7 %) to convert band ratios to fSCA. The F statistics are unchanged (94.4 %, 93.1 %) and the VNP10A1F RMSE improves (18.6 % to 15.7 %) while the MOD10A1F RMSE worsens (12.7 % to 13.7 %). Consistent with previous studies, spectral mixture approaches (SCAG, SPIReS) show lower biases (-0.1 %, -0.1 %) and RMSE (12.1 %,12.0 %), with higher F statistics (95.6 %, 96.1 %) relative to the band ratio approaches for MODIS. Landsat 8 products are all spectral mixture methods with low biases (-0.4 to 0.3 %), low RMSE (11.4 to 15.8 %), and high F statistics (97.3 to 99.1 %). Spectral unmixing methods can improve snow cover mapping at the global scale.

Timbo Stillinger et al.

Status: open (until 10 Oct 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-159', Roswitha Stolz, 11 Sep 2022 reply
  • CC1: 'Comment on tc-2022-159', Xiongxin Xiao, 21 Sep 2022 reply
  • RC2: 'Comment on tc-2022-159', Anonymous Referee #2, 21 Sep 2022 reply

Timbo Stillinger et al.

Timbo Stillinger et al.


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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 sub pixel 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.