Dual-Polarized Backscatter Features of Surface Currents in the Open Ocean during Typhoon Lan (2017)
Bibliographic record
Abstract
Ocean surface current measurements from satellites have historically been limited. We propose a new approach to detect ocean surface currents as observed by dual-polarized (VV and VH) spaceborne synthetic aperture radar (SAR). This approach is based on the assumptions that the VH-polarized SAR signal is only generated by the effects of ocean winds creating surface waves, whereas the VV-polarization data are due to the effects of both ocean winds and surface currents. Therefore, the surface currents features may be extracted after retrieving the ocean winds from VH-polarized backscatter and inputting signal due to the wind to the VV-polarized backscatter data. To investigate the performance of this approach under extreme wind conditions, we consider a scene of C-band RADARSAT-2 dual-polarized ScanSAR images over Typhoon Lan (2017) in the open ocean, and we verify our results with current estimates from altimeter data. The ocean current features extracted from the backscatter data that were collected from the SAR images are shown to correspond to an area of strong currents and an oceanic front observed by altimeters. We suggest that the proposed method has the potential capacity to provide information about ocean surface currents from high-resolution dual-polarized ScanSAR 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".