Global <scp>C</scp>‐<scp>B</scp>and <scp>E</scp>nvisat, <scp>RADARSAT</scp>‐2 and <scp>S</scp>entinel‐1 <scp>SAR</scp> measurements in copolarization and cross‐polarization
Bibliographic record
Abstract
Abstract Using colocated ASCAT and ECMWF winds, a careful global analysis of ENVISAT and Sentinel‐1 synthetic aperture radar (SAR) measurements helps to refine, at medium resolution (tens of kilometers) and especially for HH configuration, a C‐band geophysical model function (GMF, i.e., C‐SARMOD) to analyze wind sensitivity for different incidence and azimuth angles. Results unify major findings from previous global and case studies for polarization ratio (PR, VV/HH), polarization difference (PD, VV‐HH), and cross‐polarization (CP). At lower level than standard two‐scale predictions, PR increases with increasing incidence angle and decreases with increasing wind speed. PR further exhibits a strong azimuthal modulation, with maximum values in downwind configurations. The PD azimuth modulation is found more pronounced for VV than HH (VV being larger than HH), reaching maximum values for wind speed about 10 m/s. CP signals decrease with incidence angle but increase with wind speed, especially beyond 10 m/s, with no evidence of saturation. Remarkably, this also applies to HH crosswind measurements. This comparable high wind sensitivity for both CP and HH crosswind signals, with a clear departure from PD ones, can be related to the onset of vigorous breaking events, large enough to impact in‐plane and out‐of‐plane local tilts. Considering that VV polarization best maximizes the polarized resonant contribution, combined CP and VV wide swath SAR observations can thus have the potential to efficiently map and contrast local directional aspects.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".