Improved Water Level Retrieval in Complex Riverine Environments: Sentinel-3 and Sentinel-6 Altimetry Over China's Rivers

The decline in in situ water level measurements since the 1980s has impeded our ability to fully understand hydrological and hydrodynamic processes, particularly in ungauged river reaches, and how global and regional water cycles respond to climate change. Satellite altimetry offers a valuable means of supplementing these gaps in river water level data, both temporally and spatially. However, existing radar waveform retracking techniques often struggle to accommodate rivers with varying morphologies and surrounding environments. This study presents an Improved Multiple Subwaveform Analysis (IMSA) algorithm based on the 50% Threshold and Ice-1 Combined (TIC) algorithm, incorporating noise filtering into the subwave search module and refining the retracking strategy for multiple subwaves, independent of coarse digital elevation models (DEMs). We validated the IMSA algorithm using in situ data from 23 gauging stations and applied it to Sentinel-3 and Sentinel-6 altimetry across 57 virtual stations (VSs) in China, covering rivers with widths ranging from 20 to 1,500 m, generating 79 validation results (each representing an RMSE value comparing altimetry with in situ measurements). The IMSA algorithm demonstrated significant enhancements at over 48 VSs with more than 64 validation results compared to the original TIC, achieving the lowest median RMSE of 0.61 m (0.13–0.50 m lower than the OCOG, Threshold, MWaPP, and TIC algorithms), with strong resilience to environmental noise. Error analysis revealed that the altimetric accuracy is primarily influenced by the underlying surface characteristics of VSs, with built-up areas exerting significant interference. Additional disturbances stem from surrounding waters, large slopes, river channels running parallel to the satellite's ground track, and unique features such as sandbars, braided and ice-covered rivers, and hydroelectric stations. The synthetic aperture radar (SAR) mode was found to mitigate some of these land cover impacts, further improving water level retrieval accuracy. Finally, the results show that river width and topography (whether mountainous or flat) do not inherently affect altimetric accuracy, provided that the on-board tracking system is supported by accurate prior DEMs and minimal slope interference.