Applications of DINEOF to Satellite-Derived Chlorophyll-a from a Productive Coastal Region
Bibliographic record
Abstract
A major limitation for remote sensing analyses of oceanographic variables is loss of spatial data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has demonstrated effectiveness for filling spatial gaps in remote sensing datasets, making them more easily implemented in further applications. However, the spatial and temporal coverage of the input image dataset can heavily impact the outcomes of using this method and, thus, further metrics derived from these datasets, such as phytoplankton bloom phenology. In this study, we used a three-year time series of MODIS-Aqua chlorophyll-a to evaluate the DINEOF reconstruction output accuracy corresponding to variation in the form of the input data used (i.e., daily or week composite scenes) and time series length (annual or three consecutive years) for a dynamic region, the Salish Sea, Canada. The accuracy of the output data was assessed considering the original chla pixels. Daily input time series produced higher accuracy reconstructing chla (95.08–97.08% explained variance, RMSExval 1.49–1.65 mg m−3) than did all week composite counterparts (68.99–76.88% explained variance, RMSExval 1.87–2.07 mg m−3), with longer time series producing better relationships to original chla pixel concentrations. Daily images were assessed relative to extracted in situ chla measurements, with original satellite chla achieving a better relationship to in situ matchups than DINEOF gap-filled chla, and with annual DINEOF-processed data performing better than the multiyear. These results contribute to the ongoing body of work encouraging production of ocean color datasets with consistent processing for global purposes such as climate change studies.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".