Validation of the GOES-R Fractional Snow Cover Product

The global coverage of polar-orbiting satellites and the high temporal frequency of geostationary satellites allow for the robust characterization of the Earth’s snow cover. Unlike binary snow maps that provide only a snow/no-snow labeling, satellite-derived Fractional Snow Cover (FSC) products provide estimates of the area fraction of a sensor field-of-view covered by snow. For U.S. coverage, snow cover products based on a single reflective channel of the Geostationary Operational Environmental Satellite (GOES) Imager have been available for many years. Now, with the enhanced spectral sampling and higher spatial resolution that the GOES-R Advanced Baseline Imager (ABI) provides, we can expect improvements in the level of detail and accuracy of geostationary snow products.

The algorithm used for GOES-R FSC is based on the Moderate Resolution Imaging Spectroradiometer (MODIS) “MODSCAG” method developed by T. Painter. For ABI it is known as the GOES-R Snow Cover And Grain size (GOESRSCAG) algorithm. The product includes not only fractional snow cover but also snow grain size. It is employs an optimized spectral mixture analysis using atmospherically-corrected, surface spectral reflectance in two visible, five near-visible, and one thermal wavelengths. The GOESRSCAG product that is routinely generated using reflectances at the top of atmosphere is giving promising results. The GOES-R ABI surface reflectance product is still under development, and will be available for evaluation. In this presentation, we will provide more detail on the current status of the GOES-R FSC, its validation/evaluation with other snow cover products, and future calibration/validation plans. NOAA’s goal to select a single “enterprise” snow algorithm for use with multiple satellite instruments will also be discussed.