Optimal estimation of snow and ice surface parameters from imaging spectroscopy measurements

Snow and ice melt processes are key indicators of climate change, and may be assessed with remote sensing instruments. These processes decrease the surface reflectance with unique spectral patterns due to the accumulation of liquid water and light absorbing particles (LAP), making imaging spectroscopy a powerful tool to measure and map this phenomenon. Here we present a new method to retrieve snow grain size, liquid water fraction, and LAP mass mixing ratio from airborne and space borne imaging spectroscopy acquisitions. This methodology is based on a simultaneous retrieval of atmospheric and surface parameters using optimal estimation (OE), a retrieval technique which leverages prior knowledge and measurement noise in the inversion and models estimation uncertainties. The retrieval extends the surface model by exploiting statistical relationships between surface reflectance spectra and the snow properties to estimate their most probable quantities given the reflectance. To test this new algorithm we conducted a sensitivity analysis based on simulated EnMAP spectra, demonstrating accurate estimation performance of snow surface properties for orbital observations. An additional validation experiment of the extended surface model approach was conducted using in-situ measurements of algae mass mixing ratios and surface reflectance from the Greenland Ice Sheet yielding promising results. Finally, we evaluated the retrieval capacity for all snow properties with an AVIRIS-NG acquisition from the Greenland Ice Sheet demonstrating this approach’s potential and suitability for upcoming orbital imaging spectroscopy missions.