Iterative retrieval method for ocean attenuation profiles measured by airborne lidar
Bibliographic record
Abstract
Lidar remote sensing for ocean optical properties has been increasingly applied because of its ability to provide vertical structure information, which cannot be directly obtained by ocean color remote sensing. However, the application of this technology demands an inversion method to infer two quantities, i.e., attenuation and backscatter, from a single measurement. Here, a new iterative retrieval method is demonstrated to deduce the attenuation coefficient from ocean lidar return signals. One calculates the logarithmic backscatter-to-attenuation ratio k by an iterative solution based on a bio-optical model. Procedural examples of lidar-processing results—from raw data to attenuation—are presented, and the inversion results are compared with in situ measurements. The correlation coefficient R between the lidar-retrieval and in situ measurements is 0.8, and the root mean square error (RMSE) is 0.032. We then map the vertical structure of the lidar-retrieved attenuation along airborne lidar flight tracks and discuss the influences of k , the reference depth z m , and the reference value α m . Consequently, the reference value has little influence on the results for high-optical-thickness water, and k is the main error source in lidar return inversion. Primary results indicate that this method provides a more accurate k and improves the inversion accuracy of the lidar attenuation coefficient.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".