High-Frequency Centimeter-Accuracy Water Level Estimation in the Yangtze River Using Multi-GNSS Interferometric Reflectometry

Water level measurement is essential for managing and conserving water resources. The Yangtze River has a significant impact on agriculture, transportation, and local ecosystems, while traditional water level measurement still has some limitations, e.g., high cost and low coverage. Recently, global navigation satellite system-interferometric reflectometry (GNSS-IR) has become a new means for water level monitoring. This article integrates the reflected signals from GPS, GLONASS, Galileo, and BDS constellations for real-time high-frequency water level estimation at Yangtze River stations (BADO and DATO). By integrating a sliding window with variational mode decomposition (VMD), iteratively reweighted least-squares (IRLS), and Savitzky–Golay (S–G) filtering, water level measurements at a 5-min temporal resolution are obtained and evaluated. Our individual and combined results show that the VMD method efficiently provides more accurate and stable inversion results using signal-to-noise ratio (SNR) data in each window. The combined and filtered results improved the accuracy by over 45.88% when compared to single-system measurements. The root-mean-square error (RMSE) was 4.86 cm at BADO and 2.28 cm at DATO with coefficient of determination ( $R^{2})~0.99$ at both stations. Our results demonstrated that the combined method enabled precise and continuous water level monitoring, which provides new pathways for hydrological monitoring and real-time water resource management.