On the Effect of Polarization and Incidence Angle on the Estimation of Significant Wave Height From SAR Data
Bibliographic record
Abstract
Significant wave height is an extremely important descriptor of the ocean wave field. We have implemented the CWAVE algorithm using linear regression, with elastic net term selection, and single-layer feed-forward neural network using buoy observations and RADARSAT-2 Fine Quad image data as model inputs. We used a number of standard performance metrics and found that the neural network models comprehensively outperformed the regression models. We explored the effect of incidence angle and polarization on model performance and found that the most accurate models were implemented within incidence angle bins between 1° and 2°, rather than including incidence angle as an independent variable. We found that the performance of copol (horizontal-horizontal, vertical-vertical, and RL) and hybrid-pol (right-circular-horizontal and right-circular-vertical) channels was comparable, and that these channels outperformed cross-pol channels (horizontal-vertical and right-circular-right-circular). The accuracy of our H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> estimates was significantly higher than other published linear regression and neural network results. We demonstrate that a major factor in improving the accuracy of H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> estimation is to use buoy observations rather that operation wave model hindcasts as training data. We demonstrate an application of our model by creating two high-resolution H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> maps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".