Incidence Angle Dependencies for C-Band Backscatter From Sea Ice During Both the Winter and Melt Season
Bibliographic record
Abstract
Incidence angle normalization is used to reduce the radiometric ambiguity within or between synthetic aperture radar (SAR) images. For sea ice, incidence angle normalization is typically constrained to winter months because of to the difficulty of capturing the rapidly changing backscatter values during the melt season. Here, we make use of high-temporal-resolution RADASAT Constellation Mission (RCM) SAR images to quantify incidence angle dependencies (slopes) for first-year ice (FYI), second-year ice (SYI), and multi-year ice (MYI) during several stages of melt. We apply a new successive image differencing method to mitigate the rapid changes in backscatter during the melt season. Slopes for SYI are shown, for the first time, for winter, and for most melt season periods. Time series of slopes are shown, also for the first time, at intervals as short as thirty minutes. Slopes for the early melt period (FYI -0.230, SYI -0.191, MYI -0.175 dB/1°) are similar to those for winter (FYI -0.235, SYI -0.208, MYI -0.167 dB/1°). During the snow melt period, slopes remain similar to winter for FYI (-0.235 dB/1°), but become steeper for SYI (-0.241 dB/1°) and MYI (-0.240 dB/1°). All ice types reach their maximum slope steepness during the ponding period (FYI -0.308, SYI -0.283, MYI -0.289 dB/1°), and then become shallower again during the drainage period (FYI -0.198, SYI -0.207, MYI -0.240 dB/1°). We show that the melt-season-specific slopes provide important improvements for visual interpretation of SAR imagery and in backscatter consistency for automated classification algorithms.
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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 | 0.001 | 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".