A new paradigm for ocean color satellite calibration and validation: accurate measurements of hyperspectral water leaving radiance from autonomous profiling floats (HYPERNAV)
Bibliographic record
Abstract
Ocean color satellites require routine in-orbit verification and vicarious calibration to maintain accuracy over the mission lifetime and between satellites. The majority of vicarious calibration and validation activities for ocean color satellites are carried out in areas of uniform oceanic and atmospheric optical properties using in situ radiometric data collected from fixed mooring installations or oceanographic ships. These methods have limitations in spatial coverage and in the cost of maintenance and operation. A spatially extensive network of vicarious calibration match-up data points would aid in reducing vicarious calibration uncertainty. To meet these needs, we have developed a new approach to ocean color satellite vicarious calibration and validation. Our system (HYPERNAV) combines accurate, reliable and stable hyperspectral radiometric instruments with autonomous profiling float technologies to provide a cost effective, unattended means for vicarious calibration over periods of years in the open ocean. We present data from laboratory and field experiments of the HYPERNAV system used to characterize system performance and to quantify the end-to-end radiance uncertainty budget. We present match-up comparisons of HYPERNAV field data and coincident water leaving radiance measurements from ocean color satellites, demonstrating the capabilities of the system to provide new vicarious calibration paradigm for ocean-color remote-sensing satellites.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".