Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Remote Sensing |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2022.855159/full |
| _version_ | 1828082645867692032 |
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| author | Yongxiang Hu Xiaomei Lu Xubin Zeng Snorre A Stamnes Thomas A. Neuman Nathan T. Kurtz Pengwang Zhai Meng Gao 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 |
| author_facet | Yongxiang Hu Xiaomei Lu Xubin Zeng Snorre A Stamnes Thomas A. Neuman Nathan T. Kurtz Pengwang Zhai Meng Gao 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 |
| author_sort | Yongxiang Hu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T03:55:15Z |
| format | Article |
| id | doaj.art-7a0dcd363ef74e4d97f1c83170af348f |
| institution | Directory Open Access Journal |
| issn | 2673-6187 |
| language | English |
| last_indexed | 2024-04-11T03:55:15Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Remote Sensing |
| spelling | doaj.art-7a0dcd363ef74e4d97f1c83170af348f2023-01-02T00:41:28ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872022-04-01310.3389/frsen.2022.855159855159Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering MeasurementsYongxiang Hu0Xiaomei Lu1Xubin Zeng2Snorre A Stamnes3Thomas A. Neuman4Nathan T. Kurtz5Pengwang Zhai6Meng Gao7Meng Gao8Wenbo Sun9Kuanman Xu10Zhaoyan Liu11Ali H. Omar12Rosemary R. Baize13Laura J. Rogers14Brandon O. Mitchell15Knut Stamnes16Yuping Huang17Nan Chen18Carl Weimer19Jennifer Lee20Zachary Fair21Science Directorate, NASA Langley Research Center, Hampton, VA, United StatesScience Directorate, NASA Langley Research Center, Hampton, VA, United StatesDepartment of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, United StatesScience Directorate, NASA Langley Research Center, Hampton, VA, United StatesNASA/GSFC Cryospheric Sciences Lab, Greenbelt, MD, United StatesNASA/GSFC Cryospheric Sciences Lab, Greenbelt, MD, United StatesDepartment of Physics, UMBC, Baltimore, MD, United StatesOcean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United StatesScience Systems and Applications, Inc., Lanham, MD, United StatesScience Directorate, NASA Langley Research Center, Hampton, VA, United StatesScience Directorate, NASA Langley Research Center, Hampton, VA, United StatesScience Directorate, NASA Langley Research Center, Hampton, VA, United StatesScience Directorate, NASA Langley Research Center, Hampton, VA, United StatesScience Directorate, NASA Langley Research Center, Hampton, VA, United StatesScience Directorate, NASA Langley Research Center, Hampton, VA, United StatesDepartment of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, United StatesDepartment of Physics, Stevens Institute of Technology, Hoboken, NJ, United StatesDepartment of Physics, Stevens Institute of Technology, Hoboken, NJ, United StatesDepartment of Physics, Stevens Institute of Technology, Hoboken, NJ, United StatesBall Aerospace & Technologies Corp., Boulder, CO, United StatesBall Aerospace & Technologies Corp., Boulder, CO, United StatesNASA/GSFC Cryospheric Sciences Lab, Greenbelt, MD, United StatesSnow 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.https://www.frontiersin.org/articles/10.3389/frsen.2022.855159/fullsnow depthlidaraverage path lengthpath length distributionmultiple scatteringICESat-2 |
| spellingShingle | Yongxiang Hu Xiaomei Lu Xubin Zeng Snorre A Stamnes Thomas A. Neuman Nathan T. Kurtz Pengwang Zhai Meng Gao 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 Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements Frontiers in Remote Sensing snow depth lidar average path length path length distribution multiple scattering ICESat-2 |
| title | Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements |
| title_full | Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements |
| title_fullStr | Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements |
| title_full_unstemmed | Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements |
| title_short | Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements |
| title_sort | deriving snow depth from icesat 2 lidar multiple scattering measurements |
| topic | snow depth lidar average path length path length distribution multiple scattering ICESat-2 |
| url | https://www.frontiersin.org/articles/10.3389/frsen.2022.855159/full |
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