北京航空航天大学学报 ›› 2019, Vol. 45 ›› Issue (6): 1248-1255.doi: 10.13700/j.bh.1001-5965.2018.0555

• 论文 • 上一篇    下一篇

GNSS-IR双频数据融合的土壤湿度反演方法

荆丽丽1, 杨磊1, 汉牟田2, 洪学宝2, 孙波1, 梁勇1   

  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:2018-09-18 出版日期:2019-06-20 发布日期:2019-06-18
  • 通讯作者: 杨磊 E-mail:yanglei_sdau@163.com
  • 作者简介:荆丽丽 女,硕士研究生。主要研究方向:3S技术集成与应用;杨磊 男,博士,副教授。主要研究方向:GNSS-R、集成电路。
  • 基金资助:
    国家自然科学基金(41171351);国家重点研发计划(2016YFC0803104);国家“863”计划(2013AA102301);国家农业信息化工程技术研究中心开放课题(KF2015W003);浙江省基础公益研究计划(LGN19D040001);山东农业大学一流学科资金(xxxy201703)

Soil moisture inversion method based on GNSS-IR dual frequency data fusion

JING Lili1, YANG Lei1, HAN Moutian2, HONG Xuebao2, SUN Bo1, LIANG Yong1   

  1. 1. College of Information Science and Engineering, Shandong Agricultural University, Tai'an 271019, China;
    2. School of Electronic and Information Engineering, Beihang University, Beijing 100083, China
  • Received:2018-09-18 Online:2019-06-20 Published:2019-06-18

摘要: 目前全球导航卫星系统反射信号干涉测量(GNSS-IR)土壤湿度反演研究仅针对单一频点展开,提出用熵值法将2个频点数据进行融合以改进土壤湿度反演精度。首先,利用频谱分析法分别解析出各频点的信噪比(SNR)序列的振荡频率,计算出对应的等效天线高度,并利用最小二乘法求解各频点信噪比序列相位;然后,通过熵值法进行2个频点的相位观测量融合;最后,利用融合结果与实测土壤湿度建立经验模型,实现土壤湿度反演。利用地基观测实验获得的全球定位系统(GPS)L1和L2信噪比数据对该方法进行了验证,结果表明:L1和L2双频融合反演结果平均标准差为0.6%,比L1单频反演结果提高64.73%,比L2单频反演结果提高32.12%;均方根误差为0.37%,比L1频点降低72.8%,比L2频点降低73.4%。

关键词: 全球导航卫星系统反射信号干涉测量(GNSS-IR), 土壤湿度反演, 双频数据融合, 熵值法, 全球定位系统(GPS)

Abstract: At present, the study of soil moisture inversion in the field of global navigation satellite signal-interferometer and reflectometry (GNSS-IR) is only for single frequency deployment. In the paper, we propose a method that uses the entropy method to fuse two frequency to improve the accuracy of soil moisture inversion. First, the spectrum analysis method is used to analyze the oscillation frequency of the signal-to-noise ratio (SNR) sequence of each frequency point, and calculate the corresponding equivalent antenna height. The different frequency phase of SNR sequence can be solved by least square method. Then, the phase observation of two frequencies is fused by the entropy method. Finally, an empirical model was established by using the fusion results and the measured soil moisture to achieve soil moisture inversion. The method was verified by global positioning system (GPS) SNR ratio data obtained in frequency L1 and L2 by ground-based observation experiments. The results show that the average standard deviation of the L1 and L2 inversion results after dual frequency fusion is 0.6%, which is 64.73% higher than the L1 frequency inversion results and 32.12% higher than the L2 frequency inversion results. And the RMSE is 0.37%, 72.8% lower than L1 frequency and 73.4% lower than L2 frequency.

Key words: global navigation satellite signal-interferometer and reflectometry (GNSS-IR), soil moisture inversion, dual frequency data fusion, entropy method, global positioning system (GPS)

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