基于CryoSat-2数据的海冰厚度估算算法比较

季青, 庞小平, 赵羲, 程子桉

季青, 庞小平, 赵羲, 程子桉. 基于CryoSat-2数据的海冰厚度估算算法比较[J]. 武汉大学学报 ( 信息科学版), 2015, 40(11): 1467-1472. DOI: 10.13203/j.whugis20150279
引用本文: 季青, 庞小平, 赵羲, 程子桉. 基于CryoSat-2数据的海冰厚度估算算法比较[J]. 武汉大学学报 ( 信息科学版), 2015, 40(11): 1467-1472. DOI: 10.13203/j.whugis20150279
JI Qing, PANG Xiaoping, ZHAO Xi, CHENG Zian. Comparison of Sea Ice Thickness Retrieval Algorithms from CryoSat-2 Satellite Altimeter Data[J]. Geomatics and Information Science of Wuhan University, 2015, 40(11): 1467-1472. DOI: 10.13203/j.whugis20150279
Citation: JI Qing, PANG Xiaoping, ZHAO Xi, CHENG Zian. Comparison of Sea Ice Thickness Retrieval Algorithms from CryoSat-2 Satellite Altimeter Data[J]. Geomatics and Information Science of Wuhan University, 2015, 40(11): 1467-1472. DOI: 10.13203/j.whugis20150279

基于CryoSat-2数据的海冰厚度估算算法比较

基金项目: 国家自然科学基金资助项目(41301463);高等学校博士学科点专项科研新教师类基金资助项目(20130141120009);南北极环境综合考察及资源潜力评估专项基金资助项目(CHINARE2014-04-07)。
详细信息
    作者简介:

    季青,博士后,主要从事极地遥感与模型分析的研究。E-mail:jqwhu12@163.com

    通讯作者:

    庞小平,教授。E-mail:pxp@whu.edu.cn

  • 中图分类号: P228.41;P237.9

Comparison of Sea Ice Thickness Retrieval Algorithms from CryoSat-2 Satellite Altimeter Data

Funds: The National Natural Science Foundation of China, No. 41301463;the Specialize Research Fund for the Doctoral Program of Higher Education of China, No.20130141120009;the Polar Environment Comprehensive Investigation and Assessment Programmes of China, No. CHINARE2014-04-07.
  • 摘要: 以北极为研究区,使用CryoSat-2数据,利用现有海冰厚度卫星测高研究中4种主流算法(Laxon03算法、Kurtz09算法、Yi11算法和Laxon13算法)分别对北冰洋海冰厚度进行估算,并将估算结果与研究区IceBridge机载激光测高冰厚数据进行了比较,探索各算法海冰厚度估算的差异,寻找最优的估算算法,为估算长时序海冰厚度提供基础和参考。结果表明,4种算法估算的海冰厚度空间分布较为一致,但不同算法估算的结果差异较大,可达0.476 m;4种算法估算结果大小依次为Laxon03算法、Yi11算法、Laxon13算法、Kurtz09算法;4种算法估算的平均海冰厚度差异,在北极波弗特海海域最大,其次是北极中心海域、格陵兰和挪威海;Laxon13算法估算结果相对于IceBridge观测结果与其他算法相比,具有最小的平均偏差和均方根误差,是卫星测高估算海冰厚度的最优算法。
    Abstract: Sea ice thickness is an important parameter and indicator of climate change, sensitive to ecosystem in Polar Regions. For accurate forecasting of climate change, sea ice mass balance, ocean circulation and sea-atmosphere interactions, it is required to have long term records of sea ice thickness. Satellite altimetry provide useful technology for obtaining time series sea ice thickness information on hemispheric scale. So far, four mainstream algorithms based on satellite altimeter data have been used to estimate sea ice thickness effectively. Different algorithms and parameter values as selected by different researchers will lead to results unsuitable for comparison and large uncertainties. To solve this problem, this study compared sea ice thickness over the Arctic research area retrieved from these four mainstream algorithms based on CryoSat-2 satellite altimeter data. Our results demonstrate that: (1) when compared to each other, the retrieved mean sea ice thickness estimated from these four algorithms have similar spatial distribution, but with larger difference in the value, up to 0.476 m;(2) the sequence of estimated sea ice thickness for the Arctic spatial average based on four algorithms is Laxon03 algorithm, Yi11 algorithm, Laxon13 algorithm and Kurtz09 algorithm;(3) the difference of the mean sea ice thickness from these four algorithms was higher in Beaufort Sea than in Central Arctic or Greenland Sea;(4) The Laxon13 algorithm was the optimal algorithm, with the minimum bias and RMSE when compared to IceBridge sea ice thickness measurements. These results can provide useful reference and basis for further study to improve algorithms, so as to quantify dynamic changes of sea ice thickness more accurately.
  • [1] IPCC. Climate Change 2013: the Physical Science Basis [M/OL]. http://www.ipcc.ch/report/ar5/wgl, 2013
    [2] Bourke R H, Garrett R P. Sea Ice Thickness Distribution in the Arctic Ocean [J]. Cold Regions Science and Technology, 1987, 13: 259-280
    [3] Hudson R. Annual Measurement of Sea-Ice Thickness Using an Upward-Looking Sonar [J]. Nature, 1990, 344(6262): 135-137
    [4] Shimoda H, Endoh T, Muramoto K, et al. Observations of Sea-Ice Conditions in the Antarctic Coastal Application of Remote Sensing to the Estimation of Sea-Ice Thickness Distribution Region Using Ship-Board Video Cameras [J]. Antarctic Record, 1997, 41(1): 355-365
    [5] Haas C. Evaluation of Ship-Based Electromagnetic-Inductive Thickness Measurements of Summer Sea-Ice in the Bellingshausen and Amundsen Seas Antarctica [J]. Cold Regions Science and Technology, 1998, 27: 1-16
    [6] Laxon S, Peacock N, Smith D. High Interannual Variability of Sea Ice in the Arctic Region [J]. Nature, 2003, 425(6961): 947-950
    [7] Kwok R, Zwally H J, Yi D. ICESat Observations of Arctic Sea Ice: A First Look [J]. Geophysical Research Letters, 2004, 31(16): L16401
    [8] Kwok R, Cunningham G F. ICESat over Arctic Sea Ice: Estimation of Snow Depth and Ice Thickness [J]. Journal of Geophysical Research, 2008, 113: C08010
    [9] Kurtz N T, Markus T, Cavalieri D J, et al. Estimation of Sea Ice Thickness Distributions Through the Combination of Snow Depth and Satellite Laser Altimetry Data [J]. Journal of Geophysical Research, 2009, 14(C10): C10007
    [10] Kurtz N T, Markus T, Cavalieri D J, et al. Comparison of ICESat Data with Airborne Laser Altimeter Measurements over Arctic Sea-Ice[J]. IEEE Transations on Geoscience and Remote Sensing,2008,40(7):1 913-1 924
    [11] Yi D, Zwally H J, Robbins J W. ICESat Observations of Seasonal and Interannual Variations of Sea-Ice Freeboard and Estimated Thickness in the Weddell Sea Antarctica (2003-2009) [J]. Annals of Glaciology, 2011, 52(9): 43-51
    [12] Kurtz N T, Markus T. Satellite Observations of Antarctic Sea Ice Thickness and Volume [J]. Journal of Geophysical Research: Oceans (1978-2012), 2012, 117(C8): C08025
    [13] Laxon S W, Giles K A, Ridout A L, et al. CryoSat-2 Estimates of Arctic Sea Ice Thickness and Volume [J]. Geophysical Research Letters, 2013, 40: 732-737
    [14] Kern S, Khvorostovsky K, Skourup H, et al. About Uncertainties in Sea Ice Thickness Retrieval from Satellite Radar Altimetry: Results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise [J]. The Cryosphere Discussions, 2014, 8(2): 1 517-1 561
    [15] Ricker R, Hendricks S, Helm V, et al. Sensitivity of CryoSat-2 Arctic Sea-Ice Volume Trends on Radar-Waveform Interpretation [J]. The Cryosphere, 2014, 8(2): 1 831-1 871
    [16] Kwok R. Satellite Remote Sensing of Sea-Ice Thickness and Kinematics: A Review[J]. Journal of Glaciology, 2010, 56(200): 1 129-1 140
    [17] Warren S G, Rigor R G, Untersteiner N, et al. Snow Depth on Arctic Sea Ice [J]. Journal of Climate, 1999, 12(6): 1 814-1 829
    [18] Farrell S L, Kurtz N, Connor L N, et al. A First Assessment of Icebridge Snow and Ice Thickness Data over Arctic Sea Ice [J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(6): 2 098-2 111
    [19] Spreen G, Kern S, Stammer D, et al. Fram Strait Sea Ice Volume Export Estimated Between 2003 and 2008 from Satellite Data[J]. Geophysical Research Letters, 2009, 36(19):L19502
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出版历程
  • 收稿日期:  2015-05-04
  • 发布日期:  2015-11-04

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