Satellite, airborne and ground-based optical remote sensing of snow using is of great importance for understanding the current state of the cryosphere and climate change. There are different algorithms to derive snow properties (snow optical grain size, snow impurity content, snow fractional area, snow spectral and broadband albedo) using optical radiometers, spectrometers, and spectrophotopolarimeters.

This session is aimed at discussions of algorithms to solve the snow inverse problem, namely the snow properties and characteristics of snow impurities (algae, soot, dust, etc.) using optical measurements. The theoretical solution of the corresponding inverse problem is complicated by the fact that the snow grain size is much larger as compared to the wavelength of the incident light, the snow grains are closely packed and are of irregular shapes.

The horizontal and vertical heterogeneity of snow and snow surface morphology (e.g., sastrugi, ice patches and vegetation) add additional problems for the solution of the inverse problem. All these complications lead to the difference in results obtained by different remote sensing algorithms when applied to the same snow surface. An important task of this session is to narrow the gap between retrieval results from different groups by advances in understanding of light scattering, absorption and also radiative transfer in snow as well as advances in cloud screening, atmospheric correction, and solutions of the inverse problem.