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Sea level combined retrievals using multi-GNSS multipath reflectometry based on the IGGIII scheme

  • role： First author第一作者
  • Affiliation：

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

  • Email：cshu@hhu.edu.cn
  • Introduction：陈殊，研究方向为GNSS空间环境分析。E-mail：cshu@hhu.edu.cn
CHEN Shu,  
  • role： Corresponding author通信作者
  • Affiliation：

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

WANG Xiaolei,  
  • Affiliation：

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

SONG Minfeng

Resümee

Sea level is an important parameter to ensure coastal safety, monitor marine climate, and maintain elevation data. In recent years, the remote-sensing method using ground-based GNSS reflection signal can be used for sea-level monitoring. Compared with the traditional sea-level measurement method, GNSS multipath reflectometry (GNSS-MR) technology has the advantages of low cost and continuous tracking and can make all-weather, all-day observations. However, GNSS-MR technology is limited by two problems: low accuracy and low time resolution. The time resolution of retrievals can be improved by acquiring more observation data from more satellite systems. In this work, a robust regression strategy based on the IGGIII scheme is proposed to address the two limitations. This method uses the SNR data of GPS, GLONASS, Galileo, and Beidou. The Lomb-Scargle periodogram method in the classical tide level-inversion principle is used to obtain the sea-level estimates of each frequency band from quad-constellation. Then, a specific time window is established. The state-transition equation set is established in each time window considering the sea-surface dynamic change and tropospheric delay. Finally, the sea-level time series is solved by a robust estimation model. To prove the feasibility and effectiveness of this method, BRST station in France and HKQT station in Hong Kong are selected to validate the performance of the proposed method. The Root-Mean-Square Errors (RMSEs) between sea-level combined retrievals of multi-GNSS signals and the tide gauge records are calculated. The RMSE of BRST station is 12.43 cm, which is about 40%—60% higher than the single-signal results of each system. The RMSE of HKQT station is 7.09 cm, which is about 72% higher than the results of the four systems. BRST and HKQT stations can formulate a 10-min sea-level time series, which greatly improves the time resolution of sea-level retrievals compared with single-signal retrievals. Comparing the inversion results of the two stations, we conclude that using robust regression strategy based on the IGGIII scheme can lead to a clear increase in precision and thus achieve a higher temporal sampling because of the more frequent GNSS retrievals and better retrieval combination strategy. The estimated value of sea level well agrees agreement with the data of tide-gauge records and can clearly describe the sea-level fluctuation. In essence, it is a method of quality control and optimal valuation for GNSS-MR that is theoretically suitable for different geographical environments.

Schlüsselwort

remote sensing;GNSS-MR;sea level estimation;multiple system;robust regression;IGGIII

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