An Improved Antarctic Sea Ice Thickness Dataset Derived from CryoSat-2 Using LightGBM

Sea ice is crucial for modulating Antarctic air–sea fluxes, and its thickness (SIT) is the primary factor controlling the exchange of heat, moisture, and momentum. Although CryoSat-2 is commonly used for SIT retrieval, conventional algorithms rely on empirical parameters and auxiliary data that introduce substantial uncertainties. In this study, we developed a novel SIT dataset for 2010–24, derived directly from radar parameters using the Light Gradient Boosting Machine (LightGBM) machine learning method. Intercomparisons show that the LightGBM-derived SIT shows better consistency with the ICESat-2 product than conventional algorithm results. Validation against shipborne observations indicates that LightGBM-based monthly gridded SIT achieves a mean absolute error of 0.558 m, which is lower than with conventional methods (0.823 m). Temporal comparisons reveal that the LightGBM-derived sea ice volume (SIV) exhibits a more realistic seasonal cycle, with the maximum value occurring in September, compared to the conventional method, which shows a peak in August. This new SIT dataset provides a robust basis for estimating SIV with reduced uncertainty, investigating sea ice variability mechanisms, and assessing the impact of sea ice changes.