LAUSR

Abstract Satellite radar altimetry has been widely used in the monitoring of water levels of lakes, rivers and wetlands in the past decades. The conventional pulse-limited radar altimeters have a relatively large ground footprint, which limits their capability to retrieve surface elevation information over small and medium-sized water bodies. A new generation of satellite radar altimeter system, a dual-frequency SAR radar altimeter (SRAL) onboard the Copernicus Sentinel-3 satellite, has produced densely sampled elevation measurements with a smaller footprint for the Earth's surfaces since June 2016, owing to the Delay-Doppler processing technique. Four standard SRAL SAR altimetry waveform retracking algorithms (known as retrackers) have been designed to retrieve elevation measurements for different types of surfaces: Ice-Sheet retracker for polar ice sheets, SAMOSA-3 retracker for open ocean and coastal zones, OCOG retracker for sea-ice margins, and Sea-Ice retracker for sea ice. In this research, we evaluated the performances of the Sentinel-3 SRAL SAR altimetry retrackers over lakes, particularly over seasonally ice-covered lakes in one hydrological cycle. For 15 lakes and reservoirs with different sizes and at varying latitudes in the northern hemisphere, we compared the lake water levels estimated by each of standard SRAL SAR retrackers against in-situ water level measurements for different seasons (a full hydrologic cycle) during 2016–2017. Our evaluation shows that Sea-Ice retracker was unable to provide continuous estimates of lake water levels, as a result of the high rate of missing data. Although the precision and relative accuracy of lake water level estimates from these three standard SRAL SAR retrackers are similar, the SAMOSA-3 retracker has the least bias in comparison with ground-based gauge measurements. When the lakes in the mid- and high-latitude regions were covered by ice in the winter season, these three standard SAR retrackers generated erroneous lake water level measurements, significantly lower than the true lake water levels recorded by in-situ gauge stations. The measurement errors of these three standard retrackers increase with the growth of the lake ice thickness. To address the negative effect of the seasonal ice cover, we developed a new bimodal correction algorithm. We demonstrate that our bimodal correction algorithm can retrieve the ice thickness and reliably estimate water levels for the ice-covered lakes in winter, hence enabling the generation of temporally consistent lake water level measurements throughout all seasons for lake hydrological analysis.