Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements: Uncertainty Analyses ...

• Main Authors: Lu, Xiaomei, Hu, Yongxiang, Zeng, Xubin, Stamnes, Snorre A., Zhai, Peng-Wang, Et Al
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Frontiers 2022
• Subjects: Arctic; Stamnes; Sea ice
• Online Access: https://dx.doi.org/10.13016/m2bw7j-rmnl; https://mdsoar.org/handle/11603/27032

Authors: Xiaomei Lu, Yongxiang Hu, Xubin Zeng, Snorre A. Stamnes, Thomas A. Neuman, Nathan T. Kurtz, Yuekui Yang, Peng-Wang 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 and Zachary Fair ... : The application of diffusion theory and Monte Carlo lidar radiative transfer simulations presented in Part I of this series of study suggests that snow depth can be derived from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. These methods are now applied to the satellite ICESat-2 lidar measurements over the Arctic sea ice and land surfaces of Northern Hemisphere. Over the Arctic sea ice, the ICESat-2 retrieved snow depths agree well with co-located IceBridge snow radar measured values with a root-mean-square (RMS) difference of 7.8 cm or 29.2% of the mean snow depth. The terrestrial snow depths derived from ICESat-2 show drastic spatial variation of the snowpack along ICESat-2 ground tracks over the Northern Hemisphere, which are consistent with the University of Arizona (UA) and Canadian Meteorological Centre (CMC) gridded daily snow products. The RMS difference in snow depths between ICESat-2 and UA gridded daily snow products is 14 cm, or 28% of the mean ...