InSAR monitoring of Arctic land fast sea ice deformation using L-band ALOS-2, C-band Radarsat-2 and Sentinel-1
Bibliographic record
Abstract
Abstract. Arctic amplification is accelerating changes in sea ice regimes in the Canadian Arctic with later freeze-up and earlier melt events, adversely affecting Arctic wildlife and communities that depend on the stability of the sea ice conditions. To monitor both the rate and impact of such change, there is a need to accurately measure sea ice deformation, an important component for understanding ice motion and polar climate. This paper presents Interferometric Synthetic Aperture Radar (InSAR) monitoring of Arctic landfast sea ice deformation as a result of thickness changes measured from ice draft and surface height using C-band Radarsat-2, Sentinel-1 and L-band ALOS-2. The small baseline subset (SBAS) approach was explored to process time series observations for retrieval of temporal deformation changes over the winter. Sea ice deformation (subsidence and uplift in the range of −32–57 cm) detected from satellite SAR data in Cambridge Bay, Nunavut, Canada during the winter of 2018–2019 was found to be in a range of values corresponding to the ice draft growth (30–62 cm) measured from an in-situ ice profiler. The trends of InSAR observations from Sentinel-1 were also consistent with ice surface height changes along two ground tracks detected from ICESat-2. SAR backscatter from Sentinel-1 also corresponded to the surface height with strong correlation coefficient (0.49–0.83). High coherence over ice from C-band was maintained over a shorter acquisition interval than L-band due to temporal decorrelation.
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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 | 0.000 | 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".