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基于GPS多星三频数据融合的GNSS-IR土壤湿度反演方法

孙波 梁勇 汉牟田 杨磊 荆丽丽 洪学宝

孙波, 梁勇, 汉牟田, 等 . 基于GPS多星三频数据融合的GNSS-IR土壤湿度反演方法[J]. 北京航空航天大学学报, 2020, 46(6): 1089-1096. doi: 10.13700/j.bh.1001-5965.2019.0396
引用本文: 孙波, 梁勇, 汉牟田, 等 . 基于GPS多星三频数据融合的GNSS-IR土壤湿度反演方法[J]. 北京航空航天大学学报, 2020, 46(6): 1089-1096. doi: 10.13700/j.bh.1001-5965.2019.0396
SUN Bo, LIANG Yong, HAN Mutian, et al. A method for GNSS-IR soil moisture inversion based on GPS multi-satellite and triple-frequency data fusion[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(6): 1089-1096. doi: 10.13700/j.bh.1001-5965.2019.0396(in Chinese)
Citation: SUN Bo, LIANG Yong, HAN Mutian, et al. A method for GNSS-IR soil moisture inversion based on GPS multi-satellite and triple-frequency data fusion[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(6): 1089-1096. doi: 10.13700/j.bh.1001-5965.2019.0396(in Chinese)

基于GPS多星三频数据融合的GNSS-IR土壤湿度反演方法

doi: 10.13700/j.bh.1001-5965.2019.0396
基金项目: 

国家重点研发计划 2018YFD1100303

山东农业大学一流学科资金 XXXY201703

浙江省基础公益研究计划 LGN19D040001

详细信息
    作者简介:

    孙波  男, 博士研究生。主要研究方向:卫星导航及其应用

    汉牟田:梁勇  男, 博士, 教授, 博士生导师。主要研究方向:数字农业

    通讯作者:

    梁勇, E-mail:yongl@sdau.edu.cn

  • 中图分类号: P237;TB553

A method for GNSS-IR soil moisture inversion based on GPS multi-satellite and triple-frequency data fusion

Funds: 

National Key R & D Program of China 2018YFD1100303

Shandong Agricultural University Funding of First-class Disciplines XXXY201703

Zhejiang Basic Public Welfare Research Project LGN19D040001

More Information
  • 摘要:

    土壤湿度的监测是全球卫星导航系统干涉测量法(GNSS-IR)的关键应用之一。传统的GNSS-IR土壤湿度反演方法一般只针对单颗卫星的单一频段,未充分利用不同轨道、不同频率卫星信号的差异性与互补性。针对此问题,提出了一种将GPS多星的L1、L2和L5频段数据加权融合进行联合反演的方法,该方法利用基于最小方差的自适应融合算法得到加权因子,并通过现场实验进行了方法验证。结果表明:在测试集上所提出的反演方法相比于传统的Larson方法,相关系数提高了24.69%,均方根误差下降了22.28%,与均值融合法相比,相关系数提高了26.77%,均方根误差下降了23.26%,证明了所提方法能有效提高反演精度。

     

  • 图 1  干涉场景

    Figure 1.  Scenario of interference

    图 2  数据处理流程

    Figure 2.  Flowchart of data processing

    图 3  实验场地示意图

    Figure 3.  Schematic diagram of experimental site

    图 4  单星反演模型及结果

    Figure 4.  Inversion model and results of single satellite

    图 5  融合反演模型及结果对比

    Figure 5.  Fusion inversion model and results comparison

    表  1  土壤湿度反演结果评价比较

    Table  1.   Evaluation and comparison of soil moisture inversion results %

    评价指标 计算公式 自适应融合算法 Larson方法 均值融合法
    MAE 1.46 1.919 2.088
    MRE 19.427 24.995 22.938
    RMSE 2.075 2.67 2.704
    MAPE 4.836 6.372 6.887
    下载: 导出CSV
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  • 被引次数: 0
出版历程
  • 收稿日期:  2019-07-19
  • 录用日期:  2019-09-12
  • 网络出版日期:  2020-06-20

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