Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements
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Date
2022-04-08
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Citation of Original Publication
Hu Y, Lu X, Zeng X, Stamnes SA, Neuman TA, Kurtz NT, Zhai P, Gao M, Sun W, Xu K, Liu Z, Omar AH, Baize RR, Rogers LJ, Mitchell BO, Stamnes K, Huang Y, Chen N, Weimer C, Lee J and Fair Z (2022) Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements. Front. Remote Sens. 3:855159. doi: 10.3389/frsen.2022.855159
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Public Domain Mark 1.0
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Abstract
Snow is a crucial element in the Earth’s system, but snow depth and mass are very challenging to be measured globally. Here, we provide the theoretical foundation for deriving snow depth directly from space-borne lidar (ICESat-2) snow multiple scattering measurements for the first time. First, based on the Monte Carlo lidar radiative transfer simulations of ICESat-2 measurements of 532-nm laser light propagation in snow, we find that the lidar backscattering path length follows Gamma distribution. Next, we derive three simple analytical equations to compute snow depth from the average, second-, and third-order moments of the distribution. As a preliminary application, these relations are then used to retrieve snow depth over the Antarctic ice sheet and the Arctic sea ice using the ICESat-2 lidar multiple scattering measurements. The robustness of this snow depth technique is demonstrated by the agreement of snow depth computed from the three derived relations using both modeled data and ICESat-2 observations.