GNSS-IR Sea level Retrieval with Near Real-Time Potential using Shared-Frequency Signals from GPS/Galileo/BDS3

Sea level monitoring is of great significance for studying global climate change, disaster monitoring, and water resource management. GNSS Interferometric Reflectometry (GNSS-IR) technology is considered an effective complement to traditional sea level monitoring methods and has gained significant attention in recent years. However, in the two main GNSS-IR methods for sea level retrieval (spectral analysis and inverse modeling), real-time performance is limited because each low-elevation angle trajectory corresponds to a single retrieval value, and post-processing is required to eliminate outliers. In this study, we propose a sea-level retrieval method with near real-time potential based on shared-frequency signals from GPS, Galileo, and BDS3. In the data preprocessing stage, a sliding time window was used to extract the dSNR data fragments from the sea azimuths within the window. Subsequently, different strategies were employed to combine dSNR fragments from various satellites within the time window using both spectral analysis and inverse modeling processing methods to accurately extract the reflector height, which was further converted into sea level. Through experiments conducted at the AT01 and SC02 stations, we verified that the proposed spectral analysis and inverse modeling methods can stably output sea-level retrievals within 20-minute time windows at AT01 and 30-minute time windows at SC02. Subsequently, a comparison with the traditional method revealed that the proposed near real-time approach promises sea-level monitoring with higher accuracy and uniform time resolution. This study shows that GNSS-IR technology has the potential to achieve near real-time, high-precision sea-level monitoring in multi-system scenarios and further promotes its application in sea-level monitoring.