New algorithms for MODIS sun-induced chlorophyll fluorescence and a comparison with present data products
Bibliographic record
Abstract
We discuss important sources of variability in sun-induced chlorophyll fluorescence and examine difficulties in deriving fluorescence data products from satellite imagery, with a focus on the MODerate-resolution Imaging Spectroradiometer (MODIS) sensor. Our results indicate that there are limitations in the present MODIS algorithms that could lead to biases in the interpretation of the fluorescence products across gradients of chlorophyll concentration. To avoid some of these limitations, we suggest replacing the calculation of absorbed radiation by phytoplankton (ARP) over a finite depth with integration over the entire water column, and including a term accounting for cellular reabsorption of fluoresced light. These suggestions are incorporated into two new algorithms, based on established bio-optical models for case 1 waters (most open ocean waters), to retrieve chlorophyll concentration and the quantum yield of fluorescence. We compare our results to the results using MODIS algorithms for two regions: one located off the coast of Central America, including the Costa Rica Dome, and the other in the Arabian Sea. The new algorithms provide a similar field for the quantum yield of fluorescence in the first region, while they provide a different and more uniform field in the second region. We suggest that this discrepancy originates from the use of the water leaving radiance at 412 nm in the MODIS standard algorithm, which is not used in our algorithm and can be problematic under certain environmental conditions (e.g., absorbing aerosols or highly scattering waters).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".