Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements

• Published in: Frontiers in Remote Sensing
• Main Authors: Yongxiang Hu, Xiaomei Lu, Xubin Zeng, Snorre A Stamnes, Thomas A. Neuman, Nathan T. Kurtz, Pengwang Zhai, Meng Gao, Wenbo Sun, Kuanman Xu, Zhaoyan Liu, Ali H. Omar, Rosemary R. Baize, Laura J. Rogers, Brandon O. Mitchell, Knut Stamnes, Yuping Huang, Nan Chen, Carl Weimer, Jennifer Lee, Zachary Fair
• Format: Article in Journal/Newspaper
• Language: English
• Published: Frontiers Media S.A. 2022
• Subjects: snow depth; lidar; average path length; path length distribution; multiple scattering; ICESat-2; Geophysics. Cosmic physics; QC801-809; Meteorology. Climatology; QC851-999; Antarctic; Arctic; The Antarctic; Antarc*; Ice Sheet; Sea ice
• Online Access: https://doi.org/10.3389/frsen.2022.855159; https://doaj.org/article/7a0dcd363ef74e4d97f1c83170af348f

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.