Seasonal dynamics of major phytoplankton functional types in the coastal waters of the west coast of Canada derived from OLCI Sentinel 3A
Bibliographic record
Abstract
Monitoring the spatial distribution and seasonal dynamics of phytoplankton functional types (PFTs) in coastal oceans is essential for understanding fisheries production, changes in water quality, and carbon export to the deep ocean. The launch of new generation ocean color sensors such as OLCI (Ocean Land Color Instrument) onboard Sentinel 3A provides an unprecedented opportunity to study the surface dynamics of PFTs at high spatial (300 m) and temporal (daily) resolution. Here we characterize the seasonal dynamics of the major PFTs over the surface waters of the west coast of Canada using OLCI imagery and Chemical Taxonomy (CHEMTAX, v1.95) software. The satellite-based approach was adapted from a previously proven Empirical Orthogonal Function (EOF)-based algorithm by using a local matchup dataset comprising CHEMTAX model output and EOF scores derived from OLCI remote sensing reflectance. The algorithm was developed for the following PFTs: diatoms, dinoflagellates, dictyochophytes, haptophytes, green algae, cryptophytes, cyanobacteria, raphidophytes, and total chlorophyll-a (TChla) concentration. Of these PFTs, first level evaluation of the OLCI-derived retrievals showed reliable performance for diatoms and raphidophytes. The second level of validation showed that TChla had the best performance, and green algae, cryptophytes, and diatoms followed seasonal trends of a high temporal resolution in situ CHEMTAX time-series. Somewhat reduced correspondence was observed for raphidophytes. Due to their low contribution to the phytoplankton community (26%) and low range of variation, weak performance was noted for haptophytes, dictyochophytes, cyanobacteria, and dinoflagellates. The EOF-based PFT maps from daily OLCI imagery showed seasonal spring and fall diatom blooms with succession from spring blooms to high diversity flagellate dominated summer conditions. Furthermore, strong localized summer raphidophyte blooms (Heterosigma akashiwo) were observed, which are a regionally important harmful species. Overall, this study demonstrates the potential of the OLCI in deriving the surface dynamics of major PFTs of the Strait of Georgia (SoG), a critical habitat for the juvenile Pacific Salmon.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".