U.S. flag An official website of the United States government.
Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

i

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

Supporting Files


Details

  • Journal Title:
    The Cryosphere
  • Personal Author:
  • NOAA Program & Office:
    OAR (Oceanic and Atmospheric Research)
  • Description:
    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.
  • Keywords:
  • Source:
    The Cryosphere, 17(2), 567-590
  • DOI:
  • ISSN:
    1994-0424
  • Format:
    PDF
  • Publisher:
    Copernicus GmbH
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA18OAR4590367
  • License:
  • Rights Information:
    CC BY
  • Compliance:
    Library
  • Main Document Checksum:
    urn:sha256:7b41c535ef2fb5079eeba124210d48f4ffee22807a2766d9607fb2fa89baff40
  • Download URL:
  • File Type:
    Filetype[PDF - 3.35 MB ]
PREVIOUS
ON THIS PAGE
Details Supporting Files
The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.
BACK TO TOP