Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements

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dc.contributor.authorHu, Yongxiang
dc.contributor.authorLu, Xiaomei
dc.contributor.authorZeng, Xubin
dc.contributor.authorStamnes, Snorre A
dc.contributor.authorZhai, Peng-Wang
dc.contributor.authoret al
dc.date.accessioned2023-03-22T21:56:05Z
dc.date.available2023-03-22T21:56:05Z
dc.date.issued2022-04-08
dc.descriptionAuthors: 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 and Zachary Fairen_US
dc.description.abstractSnow 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.en_US
dc.description.sponsorshipThe authors wish to thank the NASA ICESat-2 program, NASA Remote Sensing Theory program, and NASA ESTO’s IIP program for supporting this research. The ICESat-2 ATL03 data used in this study are available at the National Snow and Ice Data Center: https://nsidc.org/data/ATL03/versions/5.en_US
dc.description.urihttps://www.frontiersin.org/articles/10.3389/frsen.2022.855159/fullen_US
dc.format.extent9 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2mgcg-34wf
dc.identifier.citationHu 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.2022.855159en_US
dc.identifier.urihttps://doi.org/10.3389/frsen.2022.855159
dc.identifier.urihttp://hdl.handle.net/11603/27033
dc.language.isoen_USen_US
dc.publisherFrontiersen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Physics Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis 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.en_US
dc.rightsPublic Domain Mark 1.0*
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.titleDeriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurementsen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0003-4695-5200en_US

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