Assessment of satellite altimetry accuracy (Sentinel-3A/B) with in-situ sea level data at Livingston Island, Antarctica

• Sea ice greatly reduces satellite data quality in autumn and winter seasons. • Adjusting processing settings improves satellite data accuracy in polar regions. • Waveform anomalies associated with sea ice conditions could reduce satellite altimetry reliability. Sea level records in Antarctica remain limited and challenging to obtain, particularly during winter months due to extreme weather and sea ice formation. This study presents one of the longest in-situ sea level time series available in Antarctica (2015–2024), recorded at Livingston Island, South Shetland Archipelago. The in-situ data were compared with satellite altimetry derived sea level anomalies from Sentinel-3A and Sentinel-3B. The optimal agreement with tide gauge data was achieved after adjusting the processing parameters, particularly the sea state bias correction and the number of observations averaged around the crossover point. Under these optimized settings, differences between in-situ and satellite measurements were typically within a few centimetres, showing statistically significant correlations. Seasonal analysis revealed that data quality decreases markedly during autumn and winter, when most missing or anomalous measurements coincide with sea-ice presence. In contrast, agreement improves notably during spring and summer. Sentinel-1 SAR imagery and satellite altimetry waveform analysis confirmed sea ice presence in most anomalous cases, with characteristic bimodal shapes and peak shifts observed over ice-covered surfaces. These findings underscore parameter adjustment and seasonal filtering to improve the reliability of satellite altimetry in polar regions, while highlighting the limitations imposed by ice conditions on radar signal retrieval. This study provides the first long-term validation of Sentinel-3A/B altimetry in Antarctica using nearly a decade of in-situ sea level data, extending previous assessments and demonstrating how combined SAR imagery and waveform analysis can effectively detect sea-ice contamination.