[A-CG41] Satellite Earth Environment Observation

[ACG41-11] A Roadmap to Global High Spatial/Temporal Resolution Snow Depth Survey Through Synergistic Active/Passive Optical Spectral Measurements

*Yongxiang Hu¹ (1.NASA Langley Research Center, Hampton, Virginia 23681, USA)

Keywords:snow depth , snow density, lidar , spectrometer, machine learning

In our previous articles (Hu et al., 2022; Lu et al., 2022, Hu et al., 2023), we introduced a theoretical snow depth and snow density measurements concept using lidar measurements. The key findings of these studies are: (1) snow depth and snow density are linked to the probability distribution of diffused photon scattering inside snow; (2) when absorption can be ignored, the averaged photon pathlength of laser light or sunlight traveling inside snow is twice of the snow depth; (3) snow density also affect spectral absorptions and the higher order statistics of the diffused photon pathlength distribution.

Spectral reflectance, R(k), of sunlight is a Laplace transform of the diffuse photon pathlength probability, p(L), from pathlength domain to the absorption coefficient, k. The averaged pathlength, <L>, can be derived from the derivatives of R(k). Thus, snow depth can be derived from spectral reflectance of sunlight. Snow depths can be derived with machine learning that uses lidar measurements of snow depth to train the collocated spectral solar reflectance measurements, and the algorithm can be applied to broad swath, high spatial resolution spectral imaging measurements from space.

This presentation describes the theory behind the measurements and demonstrates the concept with collocated PACE and ICESat-2 observations.