Evaluation of ICESat-2 Laser Altimetry for Inland Water Level Monitoring: A Case Study of Canadian Lakes

This study evaluates the ICESat-2 ATL13 altimetry product for water level estimation in 182 Canadian lakes by integrating satellite-derived observations with in situ measurements and applying spatial filtering based on the HydroLAKES dataset. Statistical metrics, including root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE), were employed to quantify the discrepancies between datasets. Notably, the application of HydroLAKES filtering reduced the mean RMSE from 1.53 m to 1.40 m, and further exclusion of high-error cases lowered the RMSE to 0.96 m. Larger, deeper lakes exhibited lower error margins, whereas smaller lakes with complex shorelines were more prone to variability. Regression analysis confirmed an excellent correspondence between satellite and gauge measurements (R² = 0.9999; Pearson’s r = 0.9999, p < 0.0001). Temporal analysis revealed water level declines in 134 lakes and increases in 48 lakes, suggesting potential influences of climatic variability and anthropogenic activity. These findings underscore the promise of integrating ICESat-2 altimetry with HydroLAKES-based filtering for robust inland water monitoring, while also highlighting the need for further refinement in data-processing algorithms and site-specific calibration.