Ice thickness and water level estimation for ice-covered lakes with satellite altimetry waveforms and backscattering coefficients

Abstract. Lake ice, serving as a sensitive indicator of climate change, is an important regulator of regional hydroclimate and lake ecosystems. For ice-covered lakes, traditional satellite altimetry-based water level estimation is often subject to winter anomalies that are closely related to the thickening of lake ice. Despite recent efforts made to exploit altimetry data to resolve the two interrelated variables, i.e., lake ice thickness (LIT) and the water level of ice-covered lakes, several important issues remain unsolved, including the inability to estimate LIT with altimetric backscattering coefficients in ungauged lakes due to the dependence on in situ LIT data. It is still unclear what role lake surface snow plays in the retrieval of LIT and water levels in ice-covered lakes with altimetry data. Here we developed a novel method to estimate lake ice thickness by combining altimetric waveforms and backscattering coefficients without using in situ LIT data. To overcome complicated initial LIT conditions and better represent thick ice conditions, a logarithmic regression model was developed to transform backscattering coefficients into LIT. We investigated differential impact of lake surface snow on estimating water levels for ice-covered lakes when different threshold retracking methods are used. The developed LIT estimation method, validated against in situ data and cross-validated against modeled LIT, shows an accuracy of ∼ 0.2 m and is effective at detecting thin ice that cannot be retrieved by altimetric waveforms. We also improved the estimation of water levels for ice-covered lakes with a strategy of merging lake water levels derived from different threshold methods. This study facilitates a better interpretation of satellite altimetry signals from ice-covered lakes and provides opportunities for a wider application of altimetry data to the cryosphere.