Evaluation of ICESat-2 Laser Altimetry for Inland Water Level Monitoring: A Case Study of Canadian Lakes
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".