利用IGGIII模型的多模多频GNSS-MR潮位反演
Sea level combined retrievals using multi-GNSS multipath reflectometry based on the IGGIII scheme
- 2024年28卷第2期 页码：426-436
纸质出版日期： 2024-02-07
DOI： 10.11834/jrs.20211227
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摘要
潮位是保证沿海安全、监测海洋气候、维持高程基准的重要参数。近年来基于地基Global Navigation Satellite Systems（GNSS）反射信号的遥感方法被证实可以用于潮位监测。相较于传统的潮位测量方法，GNSS-multipath reflectometry（GNSS-MR）技术有成本低、连续跟踪、全天候等优势；但是目前技术的精度不高、时间分辨率较低。通过获取更多GNSS卫星系统的观测数据可以提高潮位监测结果的时间分辨率，本文利用GPS、GLONASS、Galileo和BeiDou的观测数据，采用基于IGGIII模型的稳健回归方法对四系统的潮位反演数据进行融合研究。测站选取BRST站和HKQT站，这两个测站均可接收四系统数据；实验结果表明，利用多模多频GNSS-MR进行潮位反演，二个测站的反演精度分别优于13 cm和8 cm，相比于单系统单频精度有40%—70%的提升，而且能够大大提高时间分辨率。
Abstract
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.
关键词
遥感GNSS-MR潮位反演多模多频稳健回归IGGIII
Keywords
remote sensingGNSS-MRsea level estimationmultiple systemrobust regressionIGGIII
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相关作者
陈殊 河海大学 地球科学与工程学院
何秀凤 河海大学 地球科学与工程学院
王笑蕾 河海大学 地球科学与工程学院
宋敏峰 河海大学 地球科学与工程学院
范亚雄 中国林业科学研究院资源信息研究所;国家林业和草原局林业遥感与信息技术重点实验室
赵磊 中国林业科学研究院资源信息研究所;国家林业和草原局林业遥感与信息技术重点实验室
陈尔学 中国林业科学研究院资源信息研究所;国家林业和草原局林业遥感与信息技术重点实验室
徐昆鹏 中国林业科学研究院资源信息研究所;国家林业和草原局林业遥感与信息技术重点实验室
相关机构
中国林业科学研究院资源信息研究所
国家林业和草原局林业遥感与信息技术重点实验室
西南林业大学 林学院
河南理工大学 测绘与国土信息工程学院
中国科学院南京地理与湖泊研究所 流域地理学重点实验室
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