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 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 | 0.000 | 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 | 0.000 |
| 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 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".