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Abstract. Accurately modelling snow albedo and specific surface area (SSA) are essential for monitoring the cryosphere in a changing climate and are parameters that inform hydrologic and climate models. These snow surface properties can be modelled from spaceborne imaging spectroscopy measurements but rely on Digital Elevation Models (DEMs) of relatively coarse spatial scales (e.g. Copernicus at 30 m) degrade accuracy due to errors in derived products – like aspect. In addition, snow deposition and redistribution can change the apparent topography and thereby static DEMs may not be considered coincident with the imaging spectroscopy dataset. Testing in three different snow climates (tundra, maritime, alpine), we established a new method that simultaneously solves snow, atmospheric, and terrain parameters, enabling a solution that is more unified across sensors and introduces fewer sources of uncertainty. We leveraged imaging spectroscopy data from AVIRIS-NG and PRISMA (collected within 1 hour) to validate this method and showed a 15 % increase in performance for the radiance-based method versus using the static DEM (from r=0.52 to r=0.60). This concept can be implemented in future missions such as Surface Biology and Geology (SBG) and Copernicus Hyperspectral Imaging Mission for the Environment (CHIME).
Wilder et al. (Wed,) studied this question.