How Future Hyperspectral Satellite Spectrometer Systems Can Improve Fractional Snow-covered Area and Grain Size
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The impact of a declining and erratic seasonal mountain snowpack in North America and most of Eurasia may prove a detrimental hydrological, climatological, and environmental hazard as well as put a burden on those who rely on snow storage as their dominant source of water. There is a constant need to refine fractional snow-covered area (fSCA) and snow grain size calculations as they are a significant parameter in regional to global hydrological, ecological, and climate models. While the methods for determining fSCA and grain size are well understood for varying aerial and satellite multispectral imaging spectrometer (IS) systems, the improvements that a hyperspectral satellite IS system might bring to fSCA and grain size calculations is not well understood. As an analog for future IS systems, previously collected Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data serves the unique purpose of parameterizing the effects that future hyperspectral satellite IS systems will have on current fSCA and grain size retrieval algorithms. The following work explores the development of a fSCA and grain size retrieval model in order to understand how the differences in future hyperspectral satellite IS systems compare to current snow products. The open-source model will hopefully expand optimization and use to a variety of different IS systems and spectral unmixing tasks.