北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (6): 1089-1096.doi: 10.13700/j.bh.1001-5965.2019.0396

• 论文 • 上一篇    下一篇

基于GPS多星三频数据融合的GNSS-IR土壤湿度反演方法

孙波1, 梁勇1, 汉牟田2, 杨磊1,2, 荆丽丽1, 洪学宝2   

  1. 1. 山东农业大学 信息科学与工程学院, 泰安 271019;
    2. 北京航空航天大学 电子信息工程学院, 北京 100083
  • 收稿日期:2019-07-19 发布日期:2020-07-02
  • 通讯作者: 梁勇 E-mail:yongl@sdau.edu.cn
  • 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;梁勇 男,博士,教授,博士生导师。主要研究方向:数字农业。
  • 基金资助:
    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)

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

SUN Bo1, LIANG Yong1, HAN Mutian2, YANG Lei1,2, JING Lili1, HONG Xuebao2   

  1. 1. College of Information Science and Engineering, Shandong Agricultural University, Taian 271019, China;
    2. School of Electronic and Information Engineering, Beihang University, Beijing 100083, China
  • Received:2019-07-19 Published:2020-07-02
  • Supported by:
    National Key R & D Program of China (2018YFD1100303); Shandong Agricultural University Funding of First-class Disciplines (XXXY201703); Zhejiang Basic Public Welfare Research Project (LGN19D040001)

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。

关键词: 土壤湿度, 全球卫星导航系统(GNSS), 干涉测量法(IR), 自适应融合, 反演精度

Abstract: Soil moisture monitoring is one of the key applications of Global Navigation Satellite System Interferometry and Reflectometry (GNSS-IR). Traditional GNSS-IR soil moisture inversion methods generally utilize only one frequency of single satellite, which lose the opportunities of taking full advantages of difference and complementarity of satellite signals with different orbits and frequencies. To solve this problem, this paper proposes a joint inversion method with weighting fusions of the L1, L2 and L5 frequency band data of GPS multi-satellite. In this method, the weighting factor is determined by an adaptive fusion algorithm based on the minimum variance. Field experiment is performed for verification. The results show that, compared with traditional Larson method on the test set, the correlation coefficient and the root-mean-square error of the inversion method proposed in this paper are 24.69% higher and 22.28% lower respectively, and meanwhile compared with the fusion method of the mean value method, the correlation coefficient and the root-mean-square error are 26.77% higher and 23.26% lower respectively. Experimental results prove that the proposed method can effectively improve the inversion accuracy.

Key words: soil moisture, Global Navigation Satellite System (GNSS), Interferometry and Reflectometry (IR), adaptive fusion, inversion accuracy

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