Use of L- and C-Band SAR Satellites for Sea Ice and Iceberg Monitoring (LC-ICE)
Bibliographic record
Abstract
In support of ESA's Mission Advisory Group for ROSE-L (Radar Observing System for Europe at L-band), a project team consisting of members of operational ice services and the International Ice Charting Working Group, the International Ice Patrol, and groups from universities and research institutes is investigating the benefits of using data from L-band SAR in addition to C-band SAR imagery for separating different sea ice classes and detecting icebergs. The tasks are: (1) a critical assessment of the current state-of-the-art in sea ice monitoring and iceberg detection, (2) matching C- and L-band SAR images acquired with temporal gaps of several hours, (3) tests and assessments of the practical use of L-band images in the operational mapping services, and (4) comparison of classification accuracies that can be achieved at C-band, L-band, and a combination of both, based on the results of automated segmentation and classification algorithms. Based on the suggestions of operational ice centers, data have been collected since April 2019 over six test sites for the Northern Hemisphere: Fram Strait, Belgica Bank, northern and southern parts of Greenland, Baffin Bay and Labrador Sea. The SAR images are acquired by Sentinel-1 at Extra Wide and Interferomeric Wide Swath modes, by RADARSAT-2 at ScanSAR mode, and by ALOS-2 PALSAR-2 at Wide Beam and Fine Beam modes. The PALSAR-2 data are provided through the 2019 to 2022 mutual cooperation project between ESA and JAXA on using SAR data in earth sciences and applications. The presentation - with contributions from project partners - will focus on the conclusions from the literature review, assessments of operational ice services regarding the gain they find in using L-band SAR images supplementary to routinely analyzed C-band imagery, and preliminary results of automated classification.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".