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GNSS-MR soil moisture retrieval considering the multipath environments differences and gross error

  • role： First author第一作者
  • Affiliation：

    College of Mining, Guizhou University, Guiyang 550025, China

  • Email：1506087124@qq.com
  • Introduction：李婷，1993年生，女，硕士研究生，研究方向为GNSS-MR土壤湿度反演。E-mail： 1506087124@qq.com
LI Ting,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Mining, Guizhou University, Guiyang 550025, China

  • Email：mec.xyzhang@gzul.edu.cn
  • Introduction：张显云，1974年生，男，副教授，研究方向为GNSS理论及应用研究。E-mail： mec.xyzhang@gzul.edu.cn
ZHANG Xianyun*,  
  • Affiliation：

    College of Mining, Guizhou University, Guiyang 550025, China

DENG Xiaodong,  
  • Affiliation：

    College of Mining, Guizhou University, Guiyang 550025, China

LI Hongda,  
  • Affiliation：

    College of Mining, Guizhou University, Guiyang 550025, China

NIE Shihai

résumé

In order to obtain better phase delay estimation, improve the reliability and the practical operability of GNSS-MR(GNSS Multipath Reflectometry) soil moisture inversion, and also to simplify the complex process of the satellite selection, a multi-system multi-satellite GNSS-MR soil moisture inversion algorithm based on the robust estimation was proposed in view of the poor reliability and operability of the single system single satellite GNSS-MR soil moisture inversion and the least squares estimation of no robustness. In this algorithm, the spatial difference of multipath environment and the periodic characteristics of multipath were taken into account to screen the SNR (Signal to Noise Ratio) observations. Then, the phase delay combination representing the change trend of soil moisture was obtained by using the robust estimation based on IGGIII (Weight Function III Developed by Institute of Geodesy and Geophysics) weight function. Compared with multi-system multi- satellite combination (scheme 1) and the single-satellite combination (scheme 3), the experimental results showed that the multi-system multi-satellite combination (scheme 2) and the single-satellite combination (scheme 4) based on the robust estimation achieved higher modeling accuracy, which were benefited from the positive performance of the robust estimation. The correlation coefficients between the estimated phase delays and the measured soil moisture were 0.97 and 0.95, respectively, and the root mean square error of the soil moisture fitting residual were 0.010 and 0.012, respectively. At the same time, scheme 2 and scheme 4 also achieved higher soil moisture prediction accuracy, with the correlation coefficient between the predicted soil moisture and the measured soil moisture being 0.92 and 0.91, respectively, and the root mean square error of the soil moisture forecast residuals being 0.016 and 0.023, respectively. In addition, compared with scheme 4, scheme 2 not only adopted the robust estimation, but also adopted the multi-system multi-satellite combination, which contributed to better modeling effect and higher modeling accuracy. Moreover, because it could avoid the complex process of the satellite selection, scheme 2 owned better performance in GNSS-MR soil moisture inversion.

mots-clés

multi-system multi-satellite GNSS-MR;soil moisture inversion;signal to noise ratio;delayed phase;robust estimation

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