Wind Speed Retrieval From Hybrid-Pol Compact Polarization Synthetic Aperture Radar Images
Bibliographic record
Abstract
This paper presents an attempt to retrieve wind speed from hybrid-pol compact polarization (CP) synthetic aperture radar (SAR) data. Cross-polarization (cross-pol, denoted by HV or VH) facilitates wind speed retrieval and improves the accuracy of the results, especially with respect to high wind speeds. Although the cross-pol data cannot be obtained from CP SAR data directly, these data can be reconstructed from CP SAR data. However, the existing reconstruction algorithms cannot satisfy the quantitative wind-speed retrieval requirements for specifying the critical parameter, denoted “N” in reconstruction algorithms, either as a constant, or as a variable with limited range. Here, N is defined in terms of the ratio between cross- and co-polarization channels and the coherence coefficient between co-polarization (denoted by HH and VV) channels. Thus, we have improved the empirical reconstruction algorithm for the modified N based on a data set of more than 2000 RADASAT-2 (RS-2) quad-polarization images and collocated buoy observations. The algorithm improves the accuracy for the reconstruction of cross-pol data and, ultimately, gives improved wind speed retrievals from the hybrid-pol CP SAR data. With the new algorithm, results show that the wind speed retrievals from reconstructed cross-pol data can approximate the accuracy of VH observations collected by RS-2.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".