Lidar measurements of green laser light traveling inside snow can be modeled using Monte Carlo simulations. These simulations generate databases that link snow properties (such as snow depth and scattering mean free path) with lidar backscatter vertical profile measurements. In this study, these simulated datasets are used to train neural networks to explore the potential for estimating snow properties from ICESat-2 lidar measurements. The networks use simulated snow backscatter profiles as inputs and corresponding snow properties as outputs. Our results indicate that the near-surface portion of the snow backscatter signal contains information relevant to snow depth and scattering mean free path, demonstrating the feasibility of using machine learning frameworks for efficient analysis of spaceborne lidar observations. These findings are presented as a proof-of-concept, with comprehensive external validation and uncertainty quantification identified as future work.
Zhou et al. (Fri,) studied this question.