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Monitoring snow depth based on the SNR signal of GLONASS satellites

  • role： First author第一作者
  • Email：boy123455@126.com
  • Introduction：周威，1992年生，男，硕士研究生，研究方向为GNSS反射信号的技术及应用。E-mail：boy123455@126.com
Wei ZHOU,  
  • role： Corresponding author通信作者
  • Email：hn_liulilong@163.com
  • Introduction：刘立龙，1974年生，男，教授，研究方向为GNSS技术的理论和应用。E-mail：hn_liulilong@163.com
Lilong LIU*,  
  • No information about the author is available
Liangke HUANG,  
  • No information about the author is available
Junyu LI,  
  • No information about the author is available
Jun CHEN,  
  • No information about the author is available
Fade CHEN,  
  • No information about the author is available
Yin XING,  
  • No information about the author is available
Linbo LIU

resumen

Snow Depth (SD) measurements are important to hydrology, climatology, and agriculture. Global navigation satellite system multipath reflectometry (GNSS-MR) technology is a relatively new and powerful method for sensing SD. Thus far, many algorithms have been proposed to detect SD from the Signal-to-Noise Ratio (SNR) data of different satellites’ reflectometry signals. However, the relevant studies based on GLONASS satellites have less consideration for the development of existing GNSS-MR algorithms. This study aims to verify and analyze the suitability and reliability of applying GNSS-MR model for SD detection from GLONASS data. Theoretical analysis and formula derivation are conducted to systematically and quantitatively determine the SD detection. By analyzing the characteristics of the ground-based GNSS SNR data caused by multipath, this study introduces the detection model and the basic theory of GNSS-R technology based on SNR data to detect SD data. The SNR data of GLONASS L1 reflectometry signals can be obtained from the CORS (Continuously Oporating Reference Stations) network provided by the International GNSS Service (IGS). The residual sequence of this SNR serves as key knowledge for the SD detection in GNSS-R model. The SD is estimated through the spectral analysis using a Lomb-Scargle algorithm. To verify the above theory, the GLONASS-MR model is used to detect SD over the Yellowknife Henderson area in Canada. The observed period is from November 2015 to June 2016, 243 days in total. Then, the GLONASS products are compared with in situ measurements of the National Climate Data Center. Tesults show the following: (1) The accuracy of the detected SD based on GLONASS-MR can reach centimeter level, and the RMSE is 3.3 cm. The detected results likewise reveal good spatial coincidence, with a correlation coefficient of 0.969. (2) The different snow depths can directly affect the amplitude frequency of GLONASS SNR data and the vertical reflection distance. (3) For the same satellite, a linear relation is discovered between the peak values of the amplitude frequency and the detected SD. (4) Under the same conditions, SD detected using more GLONASS satellites can have less bias than that from a GLONASS satellite. This study aims to add new SNR data to the GNSS-MR algorithm to improve SD detection when other satellites’ data cannot be obtained. Validation using both simulated SD data and in situ measurements indicates that the proposed method is effective and can be used for real-time and continuous SD monitoring. Therefore, this study provides references for the future development of GNSS-R technology in China.

palabra clave

GNSS-MR;GLONASS;SNR;snow depth detection;Lomb-Scargle

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