Objectives As an effective “remote sensing water level gauge”, the global navigation satellite system (GNSS) -interferometric reflectometry (IR) technique, with its advantages of wide coverage, high precision and strong anti-interference ability, has become an important means for retrieving water level information in oceans, rivers, lakes, and reservoirs against the global reference frame. However, in practical applications, when the observation environment of the station is complicated and the capacity of receiving satellite signals is limited, the number and length of the signal-to-noise ratio (SNR) sequences of the water surface reflections are insufficient, which reduces the inversion accuracy of GNSS-IR water level monitoring. Especially when dealing with special scenarios such as rivers with a narrow reflection area on the water surface, or pumped storage power stations with significant daily fluctuations in water level, a single GNSS station often encounters problems such as insufficient number and limited length of the SNR sequences of the reflected water surface signals, making it difficult to comprehensively reflect the characteristics of water level changes.

Methods Therefore, We propose a GNSS-IR multi-station fusion water level monitoring method based on robust estimation. This method fully utilizes the redundant observational data from multiple measurement stations in the same water area by IGGⅢ equivalent weight function and reduces the influence of outlier rough values of all stations by optimizing the inversion points of satellite arcs of all stations.

Results To verify the effectiveness of the proposed method, experiments are conducted using data from three monitoring stations in the upper reservoir of the Xilongchi pumped storage power station, each receiving reflected signals from different water surface areas. Actual water level gauge measurements at a 1 h sampling rate are collected and converted to the WGS-84 reference frame for comparison. The results show that, for the sudden water level fluctuation process with a daily variation of 18.7 m, the correlation coefficient between the fused inversion results and the gauge measurements reaches as high as 0.983. The standard deviation between the multi-station fusion results and the measured data is 1.13 m, with a relative accuracy of 0.06. Compared with single-station inversion results, the accuracy of the proposed fusion method improves by 30% to 70%, effectively compensating for the insufficient inversion points observed in single-station results during certain periods.

Conclusions It is demonstrated that the proposed method significantly enhances the performance of GNSS-IR water level monitoring in scenarios with insufficient single-station data, while significantly improving the robustness and stability of the water level monitoring model. The research results provide crucial technical support for advancing the application of GNSS-IR technology to a broader spectrum of remote sensing fields, facilitating its practical deployment in complex hydrological monitoring scenarios.