Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets
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Published:2023-02-08
Issue:2
Volume:17
Page:567-590
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ISSN:1994-0424
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Container-title:The Cryosphere
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language:en
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Short-container-title:The Cryosphere
Author:
Stillinger TimboORCID, Rittger KarlORCID, Raleigh Mark S.ORCID, Michell Alex, Davis Robert E., Bair Edward H.ORCID
Abstract
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 d 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 United States Geological Survey (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-unmixing 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.
Funder
Cold Regions Research and Engineering Laboratory National Aeronautics and Space Administration National Oceanic and Atmospheric Administration
Publisher
Copernicus GmbH
Subject
Earth-Surface Processes,Water Science and Technology
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