• 论文
主办单位:煤炭科学研究总院有限公司、中国煤炭学会学术期刊工作委员会
基于GNSS-R技术的矿区复垦地土壤湿度反演方法研究
  • Title

    Study on soil moisture inversion method of reclamation land in mining area based on GNSS-R technology

  • 作者

    徐良骥刘悦谌芳张坤

  • Author

    XU Liangji,LIU Yue,CHEN Fang,ZHANG Kun

  • 单位

    深部煤矿采动响应与灾害防控国家重点实验室安徽理工大学测绘学院

  • Organization
    1.National Key Experiment of Mining Response and Disaster Prevention and Control in Deep Coal Mine,Huainan ,China;2.School of Surveying and Mapping,Anhui University of Science and Technology,Huainan ,China
  • 摘要

    全球导航卫星系统反射测量(GNSS-R)技术通常以单卫星单频反射信号为数据源进行土壤湿度反演,但其反演精度有限且易忽略不同卫星双频反射信号之间的差异性与互补性。为进一步提高矿区复垦地土壤湿度的反演精度,以安徽省淮南市潘集矿区东辰生态园为研究区,通过HD-V8接收机与土壤湿度监测系统(HZR80型)采集GPS卫星信号和土壤湿度等数据,利用GNSS-R土壤湿度反演方法处理得到GPS PRN1和PRN22卫星的L1、L2波段反射信号的干涉特征参量(振幅、频率、相位),并将其与土壤湿度实测值进行相关性分析,根据相关性分析结果选择相关性较强的干涉特征参量作为最优反演参数;采用自适应加权算法确定最优加权因子对最优反演参数进行数据融合;再利用PRN1和PRN22卫星L1、L2波段的最优反演参数、最优反演参数的算数平均值及其融合值分别与土壤湿度实测值建立基于单频法、均值法与融合法的土壤湿度反演模型,并验证模型的可靠性。结果表明,干涉特征参量中振幅与土壤湿度实测值的相关性较强,其相关系数的绝对值介于0.556 8~0.748 ,表明选择振幅作为最优反演参数比较合理;相比于单频法与均值法,融合法的土壤湿度反演模型更可靠,其决定系数R2为0.763 8,模型验证R2为0.936 9,均方根误差为1.907 8%,平均绝对误差为1.380 6%,最大相对误差为18.482 8%,表明基于双频数据融合的GNSS-R矿区复垦地土壤湿度反演方法可提高反演精度。


  • Abstract
    Global Navigation Satellite System Interference and Reflectometry (GNSS-IR) usually uses single-satellite single-frequency reflection signals as data sources for soil moisture inversion,but its inversion accuracy is limited and it is easy to ignore the differences and complementarity between different satellites dual frequency reflection signals.In order to further improve the retrieval accuracy of soil moisture in the mining area,taking Dongchen Ecological Park in Panji Mining Area,Huainan City,Anhui Province as the research area,GPS satellite signals and soil moisture data were collected through HD-V8 receiver and soil moisture monitoring system (HZR80 type).The GNSS-IR soil moisture retrieval method was used to obtain the interference characteristic parameters (amplitude,frequency,and phase) of the reflection signals of the L1 and L2 bands of the GPS PRN1 and PRN22 satellites,and the correlation analysis was performed with the measured soil moisture values.According to the correlation analysis results,the interference characteristic parameters with strong correlation are selected as the optimal inversion parameters.The adaptive weighting algorithm is used to determine the optimal weighting factor for data fusion of the optimal inversion parameters;and then the optimal inversion parameters of the L1 and L2 bands of the PRN1 and PRN22 sat-ellites are used.Three soil moisture inversion models based on the single frequency method,the mean method and the fusion method was established using the optimal inversion parameters of L1,L2 bands,the arithmetic mean of the optimal inversion parameters,and the fusion values and confirmed the model accuracy.The results show that the correlation between the amplitude and the measured value of soil moisture is stronger,and the absolute value of the correlation coefficient is between 0.556 8 to 0.748 3.It is reasonable to choose the amplitude as the optimal inversion parameter.Compared with the single-frequency method and the mean method,the fusion method has higher accuracy of soil moisture inversion model.Its R2is 0.763 8,the model verification R2 is 0.936 9,the RMSE is 1.907 8%,the Mean Absolute Error is 1.380 6%,and Maximum Relative Error is 18.848 28%,which indicates that the GNSS-R soil moisture retrieval method based on dualfrequency data fusion can improve the retrieval accuracy.
  • 关键词

    矿区复垦地复垦地土壤土壤湿度全球导航卫星系统反射测量双频数据融合

  • KeyWords

    mining area reclamation; reclamation soil; soil moisture; Global Navigation Satellite System-Reflectometry; dual-frequency data fusion

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主办单位:煤炭科学研究总院有限公司 中国煤炭学会学术期刊工作委员会

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