Investigating coincident L- and S-band ASAR imagery over Arctic sea ice
Bibliographic record
Abstract
Our investigation into coincident L- and S-band ASAR (airborne synthetic aperture radar) imagery explored Arctic sea ice separability in the Beaufort Sea, particularly in light of the imminent launch of the NASA-ISRO Synthetic Aperture Radar (NISAR) mission. Our research has revealed an improved capability to separate i) multi-year and first-year sea ice at the S-band imagery, as well as ii) a higher separability within first-year sea ice classes at L-band imagery. We have also reported that wind-roughened melt ponds show a distinct signature in the S-band. Importantly, our machine learning algorithm has achieved higher accuracy in sea ice classification at the S-band than the L-band. These findings have significant implications for the future of sea ice research and operations using SAR imagery from the NISAR mission. • Airborne L- and S-band SAR imagery are investigated over sea ice in the Beaufort Sea. • Improved multi-year ice detection is found in S-band imagery. • L-band detects thinner sea ice classes with improved accuracy. • The S-band imagery shows a higher capability to detect wind-roughened newly formed lead. • The overall accuracy of sea ice mapping is higher at the S-band than at the L-band.
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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 | 0.001 |
| 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) | 0.000 | 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".