  Diffuse optical spectroscopy (DOS) techniques aim to characterize scattering
media by examining their optical response to laser illumination. Time-domain
DOS methods involve illuminating the medium with a laser pulse and using a fast
photodetector to measure the time-dependent intensity of light that exits the
medium after multiple scattering events. While DOS research traditionally
focused on characterizing biological tissues, we demonstrate that time-domain
diffuse optical measurements can also be used to characterize snow. We
introduce a model that predicts the time-dependent reflectance of a dry
snowpack as a function of its density, grain size, and black carbon content,
and we develop an algorithm that retrieves these properties from measurements
at two wavelengths. To validate our approach, we use a two-wavelength lidar
system and measure the time-dependent reflectance of snow samples with varying
properties. Rather than measuring direct surface returns, our system captures
photons that enter and exit the snow at different points, separated by a small
distance (4-10cm). We find strong, linear correlations between our retrievals
of density and black carbon concentration, and ground truth measurements.
Although the correlation is not as strong, we also find that our method is
capable of distinguishing between small and large grains.
