سابِق|تال

Multi-mode and multi-frequency GNSS-MR snow depth inversion based on signal strength

  • role： First author第一作者
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

  • Email：335179752@qq.com
  • Introduction：陈国庆，研究方向为GNSS遥感。E-mail： 335179752@qq.com
CHEN Guoqing,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

  • Email：xfhe@hhu.edu.cn
  • Introduction：何秀凤，研究方向为卫星定位导航、海洋监测、卫星遥感技术。E-mail： xfhe@hhu.edu.cn
HE Xiufeng*,  
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

WANG Xiaolei,  
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

TU Jinsheng

ملخص

In recent years, with the development of Global Navigation Satellite Systems (GNSS), a GNSS-multipath reflectometry (GNSS-MR) technique based on Signal-to-Noise Ratio (SNR) has been developed. This technology can obtain the information of the reflector by using a GNSS receiver and has the advantages of abundant signal sources and high sampling rate in snow depth inversion. However, many GNSS receivers do not record SNR observations. Thus, a multimode and multifrequency GNSS-MR snow depth inversion fusion method based on Signal Strength Indicator (SSI) is proposed in this study to make these receivers capable of snow depth monitoring. At the same time, aiming at the two main problems existing in GNSS-MR inversion of snow depth, that is, low precision and low time resolution, this method can also be effectively solved. This approach mainly benefits from the strategy of performing a robust estimation. The specific steps are as follows: first, by using SSI and SNR data of GPS, GLONASS, Galileo and Beidou, and using Lomb-Scargle Periodogram (LSP) method in classical snow depth retrieval principle, the snow depth retrieval values of each frequency band are obtained from four constellations. Then, a specific time window is established, and the state transition equation set is established in each time window considering the snow surface dynamic change and tropospheric delay. Finally, the snow depth time series is solved by a robust estimation model. In essence, it is a method of optimal valuation for GNSS-MR that is theoretically suitable for different geographical environments. In addition, this study selected a suitable station for snow depth retrieval experiments to prove the feasibility and effectiveness of the method. The experimental station is SG27 in Alaska, United States.Results show that the multifrequency SSI data of four global satellite systems can retrieve snow depth. Before multimode and multifrequency GNSS-MR snow depth inversion fusion, the results of SSI inversion at each frequency band have good correlation with the measured snow depth (except for Beidou frequency band, the other correlation coefficients is greater than 0.92). Considering the standard deviation and root mean square error of the retrieval results of different satellite systems, the retrieval results of GPS satellite system are the best, followed by GLONASS, then Galileo. However, the retrieval results of these three satellite systems are similar. The Beidou satellite system has the worst retrieval result. Among the four satellite systems, root mean square error of the frequency band with the best inversion result is 6.34 cm. After multimode and multifrequency GNSS-MR snow depth inversion fusion, the root mean square error between the SSI inversion results and the measured snow depth series is 2.36 cm, and the correlation coefficient is 0.98. At the same time, the multimode and multifrequency GNSS-MR snow depth inversion based on SNR data is also performed in the calculation example; the results of SSI inversion are consistent with those of SNR inversion, and the feasibility and effectiveness of multimode and multifrequency GNSS-MR snow depth inversion fusion based on SSI are verified by experiments.

مفهوم

GNSS-MR;multi-mode and multi-frequency;snow depth;robust estimation;signal strength

References

  1. 1.
    Chen L Y, An J C, Wang Z M, Liu J B and Qu X C. 2022. Assessment of GNSS-IR-based snow-depth retrievals using observations from different receivers with the same antenna. Geomatics and Information Science of Wuhan University, 48(8): 1312-1321
  2. 2.
    Gurtner W and Estey L. 2007. RINEX-the Receiver Independent Exchange Format-Version 3.00[EB/OL]. (2007-11-28).
  3. 3.
    Hu Y F, Wang J, Li Z H and Peng J B. 2022. Ground surface elevation changes over permafrost areas revealed by multiple GNSS interferometric reflectometry. Journal of Geodesy, 96(8): 56
  4. 4.
    Huang L K, Zhou W, Liu L L, Chen J and Wang H Y. 2019. Research on surface snow depth retrieval of new L5 signals from GPS. Bulletin of Surveying and Mapping, 7: 1-5, 11
  5. 5.
    Larson K M. 2016. GPS interferometric reflectometry: applications to surface soil moisture, snow depth, and vegetation water content in the western United States. Wiley Interdisciplinary Reviews: Water, 3(6): 775-787
  6. 6.
    Larson K M, Gutmann E D, Zavorotny V U, Braun J J, Williams M K and Nievinski F G. 2009. Can we measure snow depth with GPS receivers? Geophysical Research Letters, 36(17): L17502
  7. 7.
    Larson K M, Ray R D, Nievinski F G and Freymueller J T. 2013. The accidental tide gauge: a GPS reflection case study from Kachemak Bay, Alaska. IEEE Geoscience and Remote Sensing Letters, 10(5): 1200-1204
  8. 8.
    Ozeki M and Heki K. 2012. GPS snow depth meter with geometry-free linear combinations of carrier phases. Journal of Geodesy, 86(3): 209-219
  9. 9.
    Roesler C and Larson K M. 2018. Software tools for GNSS interferometric reflectometry (GNSS-IR). GPS Solutions, 22(3): 80
  10. 10.
    Tu J S, Wei H H, Zhang R, Yang L, Lv J C, Li X M, Nie S H, Li P, Wang Y X and Li N. 2021. GNSS-IR snow depth retrieval from multi-GNSS and multi-frequency data. Remote Sensing, 13(21): 4311
  11. 11.
    Wang N Z, Xu T H, Gao F, He Y Q, Meng X Y, Jing L L and Ning B J. 2022. Sea-level monitoring and ocean tide analysis based on multipath reflectometry using received strength indicator data from multi-GNSS signals. IEEE Transactions on Geoscience and Remote Sensing, 60: 4211513
  12. 12.
    Wang X L, He X F and Zhang Q. 2020. Coherent superposition of multi-GNSS wavelet analysis periodogram for sea-level retrieval in GNSS multipath reflectometry. Advances in Space Research, 65(7): 1781-1788
  13. 13.
    Wang Z M, Liu Z K, An J C and Lin G B. 2018. Snow depth detection and error analysis derived from SNR of GPS and BDS. Acta Geodaetica et Cartographica Sinica, 47(1): 8-16
  14. 14.
    Williams S D P and Nievinski F G. 2017. Tropospheric delays in ground-based GNSS multipath reflectometry—Experimental evidence from coastal sites. Journal of Geophysical Research: Solid Earth, 122(3): 2310-2327
  15. 15.
    Yan S H, Zhang N, Chen N C and Gong J Y. 2018. Feasibility of using signal strength indicator data to estimate soil moisture based on GNSS interference signal analysis. Remote Sensing Letters, 9(1): 61-70
  16. 16.
    Yang Y, Song L and Xu T. 2002. Robust estimator for correlated observations based on bifactor equivalent weights. Journal of Geodesy, 76(6/7): 353-358
  17. 17.
    Yu K G, Ban W, Zhang X H and Yu X W. 2015. Snow depth estimation based on multipath phase combination of GPS triple-frequency signals. IEEE Transactions on Geoscience and Remote Sensing, 53(9): 5100-5109
  18. 18.
    Zhou W, Liu L L, Huang L K, Li J Y, Chen J, Chen F D, Xing Y and Liu L B. 2018. Monitoring snow depth based on the SNR signal of GLONASS satellites. Journal of Remote Sensing (in Chinese), 22(5): 889-899

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