8.2 Lidar Multiple Scattering Pathlength Invariance Theory and Its Application in Measurements of Physical Properties of Dense Scattering Media

Tuesday, 30 January 2024: 4:45 PM
Holiday 1-3 (Hilton Baltimore Inner Harbor)
Yongxiang Hu, NASA, Hampton, VA; NASA Langley Research Center, Hampton, VA; and X. Lu, X. Zeng, C. Gatebe, T. Neumann, C. Weimer, P. W. Zhai, M. Gao, P. Yang, Q. Fu, S. Stamnes, K. H. Stamnes, A. H. Omar, R. Baize, and A. Ashraf

When the receiver footprint size is significantly larger than the laser spot size such that the diffuse optical depth difference along the diameter is larger than 10, multiply-scattered lidar signals from dense scattering media including snow, turbid inland waters, biological tissue, and dense clouds provide rich information about the physical properties of the scattering media. Specifically, the averaged absorption-corrected nadir-pointing lidar multiple scattering depth ( <L>/2 ) equals the equivalent depth of the media ( H = <L>/2 ), and the second moment of the absorption-corrected multiple scattering path ( <L2> ) provides the diffuse scattering optical depth ( diffuse scattering optical depth = 8 <L2> / <L>2 ) (Hu et al., 2022; Lu et al., 2022; Hu et al., 2023).

We introduce the lidar multiple scattering pathlength invariance theory, its proof from analytical radiative transfer theory and Monte Carlo model simulation, and its potential applications.

Reference

Hu, Y., Lu, X., Zeng, X., Stamnes, S. A., Neuman, T. A., Kurtz, N. T., ... & Fair, Z. (2022). Deriving snow depth from ICESat-2 LiDAR multiple scattering measurements. Frontiers in Remote Sensing, 3, 855159.

Lu, X., Hu, Y., Zeng, X., Stamnes, S. A., Neuman, T. A., Kurtz, N. T., ... & Fair, Z. (2022). Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements: Uncertainty Analyses. Frontiers in Remote Sensing, 3, 891481.

Hu, Y. et al. (2023). Linking lidar multiple scattering profiles to snow depth and snow density: an analytical radiative transfer analysis and the implications for remote sensing of snow. Frontiers in Remote Sensing, Volume 4 – 2023, doi: 10.3389/frsen.2023.1202234

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