GNSS-IR near real-time sea level retrieval from multi-constellation shared-frequency signals

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 near-real-time sea-level retrieval method based on shared-frequency signals. A sliding time window was used, and different strategies were employed to combine dSNRs 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 two PBO stations, we verified that near-real-time spectral analysis and inverse modeling methods can stably output sea-level retrievals within 20/30-minute time windows. Subsequently, a comparison with the traditional method revealed that the near-real-time approach enables sea-level monitoring with higher accuracy and much more uniform time resolution. This study shows that GNSS-IR technology can achieve near real-time, high-precision sea-level monitoring in multi-system scenarios and further promotes its application in sea-level monitoring.