Wind speed retrieval from RADARSAT-2 quad-polarization images using a new polarization ratio model
Bibliographic record
Abstract
[1] This paper presents a first attempt to analyze C-band RADARSAT-2 measurements of the normalized radar cross sections (NRCS) in quad-polarization acquisition mode (HH, VV, HV, and VH) over the ocean. NRCS in copolarizations and cross-polarizations are found to be different; the latter is independent of radar incidence angles and wind directions, but is quite linear with respect to wind speeds. We also investigate the properties of the polarization ratio, denoted PR, and show that it is dependent on incidence angle and azimuth angle as well as wind speed. It also correlates well with wave steepness and significant wave height. Moreover, the polarization difference shows a linear relationship with wind speed. Two new analytical models are proposed to estimate PR; one is a function of incidence angle only, while the other has additional dependence on wind speed. Comparisons are presented with theoretical and empirical PR models from the literature; the new PR model which includes wind speed dependence is shown to best compare with observed RADARSAT-2 data. An assessment of this PR model is given using different CMOD algorithms and RADARSAT-2 images. Results show that the wind speeds retrieved from this PR model and CMOD5.N are in good agreement with buoy measurements (standard deviation, 1.37 m/s). This joint GMF-PR approach constitutes a promising hybrid model for wind speed retrievals from HH-polarized RADARSAT-2 images.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".