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星载GNSS-R融冰期海冰密集度反演研究

王玥 谢涛 李建 张雪红 白淑英 王明华

王玥,谢涛,李建,等. 星载GNSS-R融冰期海冰密集度反演研究[J]. 海洋学报,2024,46(x):1–10
引用本文: 王玥,谢涛,李建,等. 星载GNSS-R融冰期海冰密集度反演研究[J]. 海洋学报,2024,46(x):1–10
Wang Yue,Xie Tao,Li Jian, et al. Sea ice concentration retrieval using spaceborne GNSS-R during the melting period[J]. Haiyang Xuebao,2024, 46(x):1–10
Citation: Wang Yue,Xie Tao,Li Jian, et al. Sea ice concentration retrieval using spaceborne GNSS-R during the melting period[J]. Haiyang Xuebao,2024, 46(x):1–10

星载GNSS-R融冰期海冰密集度反演研究

基金项目: 国家自然科学基金项目(42176180);国家重点研发计划项目(2021YFC2803302)。
详细信息
    作者简介:

    王玥(1998—),女,江苏省南通市人,主要从事GNSS-R海洋遥感研究。E-mail:wy2805@outlook.com

    通讯作者:

    谢涛(1973—),男,湖南省张家界市人,教授,主要从事海洋微波遥感研究。E-mail: xietao@nuist.edu.cn

Sea ice concentration retrieval using spaceborne GNSS-R during the melting period

  • 摘要: 针对北极融冰期的海冰密集度反演,并改善全球导航卫星系统反射测量(GNSS-R)对海水的海冰密集度高估问题,本文提出一种利用机器学习算法生成高时空分辨率的融冰期海冰密集度估算方法,提取GNSS-R时延多普勒图(DDM)的特征参数,并结合海表温度数据建立LightGBM模型,将反演结果与参考海冰密集度值进行相关性分析和评估。本文的模型结果与OSI SAF的海冰密集度产品显示出较好的一致性,相关系数、平均绝对误差和均方根误差分别为0.965、0.061和0.090,该方法能够实现对北极海冰边缘区的海冰密集度高精度估计。
  • 图  1  0%、50%和90% SIC DDM和对应的归一化积分延迟波形

    Fig.  1  DDM for 0%, 50% and 90% SIC and corresponding normalized Integrated Delay Waveforms

    图  2  LightGBM模型架构

    Fig.  2  Framework of the LightGBM model

    图  3  测试集基于DDM特征参数的反演结果与OSISAF海冰密集度对比散点图(a)和基于DDM特征参数的LightGBM特征重要性排序结果(b)

    Fig.  3  Scatter plot of retrieval results based on DDM observables against OSISAF sea ice concentration from the test samples (a) and LightGBM feature importance ranking results based on DDM observables (b)

    图  4  测试集海冰密集度反演结果与OSISAF海冰密集度对比散点图

    a. 基于SST;b. 基于DDM特征参数、SST

    Fig.  4  Scatter plot of retrieval results against OSISAF sea ice concentration from the test samples

    a. Based on SST; b. based on DDM observables and SST

    图  5  整个验证集不同海冰密集度区间与反演误差箱线图

    a. 基于DDM特征参数的模型;b. 基于DDM特征参数、SST的模型

    Fig.  5  Box plots of different sea ice concentration intervals and retrieval error using the entire validation set

    a. Based on DDM observables; b based on DDM observables and SST

    图  6  基于DDM特征参数和SST的反演结果与OSISAF海冰密集度对比散点图

    a. 2018年7月20-22日;b. 2018年8月9-10日;c. 2018年8月15-16日

    Fig.  6  Scatter plot of retrieval results based on DDM observable against OSISAF sea ice concentration

    a. July 20-22, 2018; b. August 9-10, 2018; c. August 15-16, 2018

    图  7  2018年8月9日模型反演结果与OSISAF海冰密集度地图比较

    Fig.  7  Comparison of model retrieval results with OSISAF sea ice concentration map on August 9th, 2018

    表  1  不同特征参数组合的反演误差统计表

    Tab.  1  Error Statistics for Different DDM observables Combinations

    序号特征参数组合RMAERMSE
    1DDMA + SNR + PP + TES + LES + K + SK (无Γ)0.8670.1240.176
    2Γ + SNR + PP + TES + LES + K + SK (无DDMA)0.8590.1280.181
    3Γ + DDMA + PP + TES + LES + K + SK (无SNR)0.8170.1460.204
    4Γ + DDMA + SNR + TES + LES + K + SK (无PP)0.8510.1320.186
    5Γ + DDMA + SNR + PP + LES + K + SK (无TES)0.8710.1220.174
    6Γ + DDMA + SNR + PP + TES + K + SK (无LES)0.8710.1220.174
    7Γ + DDMA + SNR + PP + TES + LES + SK (无K)0.8660.1240.177
    8Γ + DDMA + SNR + PP + TES + LES + K (无SK)0.8660.1250.177
    9Γ + DDMA + SNR + PP + TES + LES + K + SK0.8750.1200.171
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-30
  • 修回日期:  2023-11-21
  • 网络出版日期:  2024-03-14

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