GNSS反射测量在低成本GNSS天线水位监测中的应用——以顺化省谭江泻湖为例

Vu Phuong Lan, Ha Minh Cuong, Nguyen Phuong Bac, Dinh Thi Bao Hoa, Pham Van Manh, Doan Quang Cuong, Nguyen Huu Duy
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引用次数: 1

摘要

由于气候变化和海平面上升,极端水文事件变得越来越难以预测,这凸显了沿海海平面监测的重要性。该研究旨在开发一种利用低成本多频天线测量水位的全球导航卫星系统(GNSS)反射测量技术。2022年4月10日至4月29日,在顺化省潭江潟湖地区安装多频GNSS天线,以1Hz频率收集GPS/GLONASS/伽利略/北斗卫星数据。利用基于信噪比的干涉图技术(IPT)从GNSS反射数据计算水位高程。滤波后的水位结果通过位于同一位置的水位传感器的数据进行验证。结合不同频率,GNSS - Reflectometry (GNSS - R)水位与现场测量水位的均方根误差为0.049 m,相关系数为0.93。研究结果表明,多频GNSS-R站可以作为一种附加的水位测量方法,其精度与标准潮汐计相当。此外,研究结果还显示了GNSS反射信号对天气条件和海面状态的敏感性,这是利用GNSS反射数据预测和预警极端风暴潮的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of GNSS Reflectometry in Water Level Monitoring using Low-cost GNSS Antenna: A Case Study in Tam Giang Lagoon, Thua Thien Hue Province
Extreme hydrological events become increasingly unpredictable due to climate change and sea-level rise, highlighting the importance of coastal sea level monitoring. This study aims to develop a Global Navigation Satellite System (GNSS) reflectometry technology that uses low-cost multi-frequency antennas to measure water levels. A multi-frequency GNSS antenna was installed in the Tam Giang lagoon area, Thua Thien Hue province, to collect data of GPS/GLONASS/Galileo/Beidou satellites at 1Hz from April 10 to April 29, 2022. Water level elevation is calculated from GNSS reflectometry data using Interference Pattern Technical (IPT) based on Signal-to-Noise Ratio (SNR). After filtering, the water level results are validated by data from the water level sensor located in the same location. The Root Mean Square Error between the water level from the GNSS - Reflectometry (GNSS– R) and the in situ measurement is 0,049 m and the correlation coefficient reaches 0,93 when combining different frequencies. The study results demonstrate that the multi-frequency GNSS-R station can be used as an additional method to measure water levels with an accuracy comparable to that of a standard tidal gauge. In addition, the study results also show the sensitivity of the GNSS reflected signal to weather conditions and the state of the sea surface, which is the basis for forecasting and early warning of storm surge extremes from GNSS reflectometry data.  
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