RADARSAT-2 Polarimetric Radar Imaging for Lake Ice Mapping
Bibliographic record
Abstract
Changes in lake ice dates and duration are useful indicators for assessing long-term climate trends and variability in northern countries. Lake ice cover observations are also a valuable data source for predictions with numerical ice and weather forecasting models. In recent years, satellite remote sensing has assumed a greater role in providing observations of lake ice cover extent for both modeling and climate monitoring purposes. Polarimetric radar imaging has become a promising tool for lake ice mapping at high latitudes where cloud cover and polar darkness severely limit observations from optical sensors. In this study, we assessed and characterized the physical scattering mechanisms of lake ice from fully polarimetric RADARSAT-2 datasets obtained over Great Bear Lake, Canada, with the intent of classifying open water and ice cover during the freeze-up and break-up periods. Model-based and eigen-based decompositions were employed to construct the coherency matrix into deterministic scattering mechanisms, and secondary physical parameters were generated following the polarimetric decompositions. This study presents an application of the Markov Random Field by introducing radar signals and polarimetric parameters as features. These features were labeled using the entropy-alpha Wishart classifier. We show that the selected polarimetric parameters can help with interpretation of radar-ice/water interactions and can be used successfully for water-ice segmentation. As more satellite SAR sensors are being launched or planned, such as the Sentinel-1a/b series and the upcoming RADARSAT Constellation Mission, the rapid volume growth of data and their analysis require the development of robust automated algorithms. The approach developed in this study was therefore designed with the intent of moving towards fully automated mapping of lake ice for consideration by ice services.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".