Large-scale sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission
Bibliographic record
Abstract
Abstract. As Arctic sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of sea ice. Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014, Sentinel-1B in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent 5 spaceborne SAR sensors, that together routinely cover the pan-Arctic sea ice domain. Here, we utilized over 60,000 SAR images from Sentinel-1AB (S1) and RCM to generate large-scale sea ice motion (SIM) estimates over the pan-Arctic domain from March to December, 2020. On average, 4.5 million SIM vectors from S1 and RCM were automatically detected per week for 2020 and when combined (S1+RCM) they facilitated the generation of 7-day, 25 km SIM products across the pan-Arctic domain. S1+RCM SIM provided more coverage in Hudson Bay, Davis Strait, Beaufort Sea, Bering Sea, and over the North Pole compared to SIM from S1 alone. S1+RCM SIM was able to be resolved within the narrow channels and inlets across the pan-Arctic alleviating the main limitation of coarser resolution sensors. S1+RCM SIM provided larger ice speeds with a mean difference (MD) of 1.3 km/day compared to the National Snow and Ice Data Center (NSIDC) SIM product and a MD of 0.76 km/day compared to Ocean and Sea Ice-Satellite Application Facility (OSI-SAF) SIM product. S1+RCM was also able to better resolve SIM in the marginal ice zone compared to the NSIDC and OSA-SAF SIM products. Overall, our results demonstrate that combining SIM from multiple spaceborne SAR satellites allows for large-scale SIM to be routinely generated across the pan-Arctic domain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".