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

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 WeimerJennifer Lee, Zachary Fair

Research output: Contribution to journalArticlepeer-review


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 UA snow depth. To better understand these results, we also discuss the possible sources of errors in ICESat-2 derived snow depths, including surface roughness within the laser footprint, atmospheric forward scattering, solar background noise, and detector dark current. Simulation results indicate that the snow depth errors would be less than 5 cm if the standard deviation of pulse spreading due to surface roughness is within 50 cm. Our results demonstrate that the ICESat-2 lidar measurements can be used to reliably derive snow depth, which is a critical geophysical parameter for cryosphere studies including sea ice thickness estimation and also provides important constraints in the modeling of terrestrial hydrological processes.

Original languageEnglish (US)
Article number891481
JournalFrontiers in Remote Sensing
StatePublished - 2022
Externally publishedYes


  • ICESat-2 lidar
  • multiple scattering
  • pathlength distribution
  • snow depth
  • snow water equivalent

ASJC Scopus subject areas

  • Physics and Astronomy (miscellaneous)
  • Biophysics


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