C- and L-band SAR signatures of Arctic sea ice during freeze-up
Bibliographic record
Abstract
Identifying sea ice types in the early stages of development from L-band SAR imagery remains an active research area during the Arctic freeze-up period. We used ScanSAR C- and L-band imagery from RADARSAT-2, ALOS PALSAR and ALOS-2 PALSAR-2, to identify ice types in the North Water Polynya (NOW) and Victoria Strait (VS) region of the Canadian Arctic. We investigated the HH-polarized microwave backscatter coefficient (σHH0) and its GLCM texture parameters for six ice classes and open water. We found very low σHH0 for nilas at both C- and L-band. Although similar σHH0 found for grey ice at both frequencies, σHH0 decrease with increasing ice thickness at L-band from grey ice, whereas, at C-band, σHH0 increases from grey to grey-white ice and then decreases as the ice grows. GLCM texture parameters show lower values for L-band than C-band; however, separability among classes was found only for a few selected parameters. We used the support vector machine (SVM) algorithm for ice type classification from SAR scenes using σHH0 and GLCM texture statistics. Due to overlapping σHH0 signatures at C-band, early-stage ice classes were substantially misclassified. L-band identified early-stage ice classes with higher accuracy compared to C-band but misclassified thicker ice types and open water. L-band alone provided very good classification results (~80% accuracy) and combining L- and C-band (i.e., dual-frequency approach) further increased accuracy to >90%. C-band alone resulted in the lowest accuracy of <60%. We acknowledge that developing a universal ice classification is still a challenge and requires some manual supervision to adopt variable ice conditions into the classification method. However, a dual-frequency approach can achieve higher classification accuracy than conventionally used single-frequency approaches. This research highlights the value of upcoming L-Band SAR missions to improve sea ice classification in regions where a variety of ice types exist, including many thinner types, which are now dominating an increasingly warming Arctic.
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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 | 0.000 |
| 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) | 0.000 | 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 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".