Quad‐polarization SAR features of ocean currents
Bibliographic record
Abstract
Abstract A methodology is demonstrated to exploit the polarization sensitivity of high‐resolution radar measurements to interpret and quantify upper ocean dynamics. This study particularly illustrates the potential of quad‐polarization synthetic aperture radar (SAR) measurements. The analysis relies on essential characteristics of the electromagnetic scattering mechanisms and hydrodynamical principles. As the relaxation scale of centimeter‐scale ocean surface scatters is typically small, radar signal anomalies associated with surface manifestations of the upper ocean dynamics on spatial scales exceeding 100 m are mostly dominated by nonresonant and nonpolarized scatters. These “scalar” contributions can thus efficiently trace local breaking and near‐breaking areas, caused by surface current variations. Using dual copolarized measurements, the polarized Bragg‐type radar scattering is isolated by considering the difference (PD) between vertically and horizontally polarized radar signals. The nonpolarized (NP) contribution associated with wave breaking is then deduced, using the measured polarization ratio (PR) between polarized signals. Considering SAR scenes depicting various surface manifestations of the upper ocean dynamics (internal waves, mesoscale surface current features, and SST front), the proposed methodology and set of decompositions (PD, PR, and NP) efficiently enable the discrimination between surface manifestation of upper ocean dynamics and wind field variability. Applied to quad‐polarized SAR images, such decompositions further provide unique opportunities to more directly assess the cross‐polarized (CP for HV or VH) signal sensitivity to surface roughness changes. As demonstrated, such an analysis unambiguously demonstrates and quantitatively evaluates the relative impact of breakers on cross‐polarized signals under low to moderate wind conditions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".