AUTHOR=Hu Yongxiang , Lu Xiaomei , Zeng Xubin , Stamnes Snorre A , Neuman Thomas A. , Kurtz Nathan T. , Zhai Pengwang , Gao Meng , Sun Wenbo , Xu Kuanman , Liu Zhaoyan , Omar Ali H. , Baize Rosemary R. , Rogers Laura J. , Mitchell Brandon O. , Stamnes Knut , Huang Yuping , Chen Nan , Weimer Carl , Lee Jennifer , Fair Zachary 

TITLE=Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements

JOURNAL=Frontiers in Remote Sensing

VOLUME=Volume 3 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2022.855159

DOI=10.3389/frsen.2022.855159

ISSN=2673-6187

ABSTRACT=Snow is a crucial element in the Earth system, but snow depth and mass are very challenging to measure globally. Here we provide the theoretical foundation for deriving snow depth directly from spaceborne lidar (ICESat-2) snow multiple scattering measurements for the first time. First, based on Monte Carlo lidar radiative transfer simulations of ICESat-2 measurements of 532 nm laser light propagation in snow, we find that the lidar backscattering pathlength follows the Gamma distribution. Next, we derive three simple analytical equations to compute snow depth from the average, second- and third-order moments of the distribution. The robustness of our theory is demonstrated by the agreement among the three derived relations and the convergence of the relations using a different radiative transfer calculation. As a preliminary application, these relations are then used to retrieve snow depth over the Antarctic ice sheet and the Arctic sea ice from the ICESat-2 lidar multiple scattering measurements.