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.
Satellite image data is being researched and applied effectively in the survey and establishment of bathymetry mapping in shallow water areas in both time and human terms. Remote sensing techniques contribute to rapid updating of topography, timely assurance of civil and military operations such as maritime safety, environmental security and rescue, Warfare in the military, especially the ability to remotely monitor disputed areas. The article experiment with the Stumpf et al algorithm to estimate the shallow water depths on the Spratly Island by Landsat 8 image. The correlation coefficient of the model R2 is 0.924; RMSE is 0.99m. In addition, the results are compared with the map data of C-map and use 12 actual test points scores to evaluate the accuracy of the model.
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