A case study comparing approaches to mask satellite-derived bathymetry
Bibliographic record
Abstract
Abstract Satellite-derived bathymetry (SDB) is a cost-effective method for estimating water depth in inland and coastal waters, but is only applicable to optically shallow water (OSW). Determining the appropriate extent of SDB maps and the depth threshold for accurate SDB model predictions has therefore been a challenge for practical applications of SDB. Previous studies have used either a numeric cut-off value or manually delineated OSW to determine where to apply, and where not to apply, SDB models. We compared the use of a threshold applied to the predicted depth, automated delineation of OSW using a published model, and manual delineation of OSW, to determine which method of masking unsuitable pixels for SDB performs best. We used a water-leaving reflectance Sentinel-2 image of the St. Lawrence River, and a Random Forest model using neighbouring pixel information to predict SDB. We then compared the different approaches to masking unsuitable pixels in terms of the mean absolute error (MAE) of the retained predictions and the total mapped area. The application of a model-predicted depth threshold is easy to implement and achieved an MAE of 0.54 m, outperforming automated and manual OSW delineation methods, which had MAEs of 1.39 m and 1.64 m respectively over an approximately 100 km2 study area. Future studies should further investigate these and other methods for masking pixels unsuitable for SDB under a wider range of environmental conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".