Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements

Abstract

Accurately modelling optical snow properties like 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), which degrade accuracy due to errors in derived products such as slope and 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 Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) (collected within 1 h) to validate this method and showed a 25 % increase in performance for the radiance-based method over the static method when estimating SSA. This concept can be implemented in missions such as Surface Biology and Geology (SBG), the Environmental Mapping and Analysis Program (EnMap), and the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME).