Modeling snow optical properties from single wavelength airborne lidar in steep forested terrain

Airborne lidar is a powerful tool used by water resource managers to map snow depth and aid in producing spatially distributed snow water equivalent (SWE) when combined with modeled density. However, limited research so far has focused on retrieving optical snow properties from lidar. Optical snow surface properties directly impact albedo, which has a major control on snowmelt timing, which is especially useful for water management applications. Airborne lidar instruments typically emit energy at a wavelength of 1,064 nm, which can be informative in mapping optical snow surface properties since grain size modulates reflectance at this wavelength. In this paper we present and validate an approach using airborne lidar for estimating snow reflectance and optical grain size at high spatial resolution. We utilize three lidar flights over the Boise National Forest, United States, during a winter season from December 2022 to March 2023. We discuss sensitivities to beam incidence angles, compare results to in situ measurements snow grain size, and perform spatial analyses to ensure reflectance and optical grain size varies across space and time as anticipated. Modeled optical grain size from lidar performed well (Root mean squared difference = 49 μm; percent mean absolute difference = 31%; n = 28), suggesting that aerial lidar surveys can be useful in mapping snow reflectance and optical grain size for dry snow, and may support development of other remote sensing technologies and aid water resources management.