Analysis of MODIS-Aqua imagery to determine phytoplankton phenology in the Strait of Georgia, Canada
Bibliographic record
Abstract
Accurate chlorophyll-a concentration timeseries was created for the Strait of Georgia (SoG), Canada, using an improved atmospheric correction scheme and workflow for the Moderate Resolution Imaging Spectroradiometer AQUA (MODIS) satellite instrument. Useful Chla dynamics and spring bloom phenology in the SoG were derived. In situ radiometric samples were acquired via Aerosol Robotic Network (AERONET), and hyperspectral data collected from a Hyperspectral Surface Acquisition System (HyperSAS) to assess three potential atmospheric correction schemes. Water property samples including total suspended material (TSM), chromophoric dissolved organic matter (CDOM), and chlorophyll concentrations (chla) were collected to further assess atmospheric corrections and the applied ‘Ocean Color 3 Modis’ (OC3M) standard chlorophyll algorithm. Regression, Absolute percentage difference (APD), Relative Percentage difference (RPD), and Root mean squared error (RMSE) analysis revealed the most appropriate method to be the ‘Management Unit of the North Seas Mathematical Models’ (MUMM) using the shortwave infrared spectrum (SWIR) to determine NIR-derived aerosol model. This method was used to construct a time series (July 2002-June 2014) of daily chlorophyll maps for all available imagery. Files were spatially binned into 8-day composites for the North and Central SoG where a modified threshold-based definition was used to determine the start of the spring phytoplankton bloom period, as well as timing of maxima and duration of the largest spring bloom. Results indicate Central SoG start dates range from late February to late April, with an average start date at the last week of March. These results compare favorably to Hindcast predictive modelling of bloom start dates. The Northern SoG bloom phenology starts on average 9 days earlier, and experiences lower chlorophyll-a magnitudes. Hierarchical clustering with correlation similarity of spring seasons indicate 2008 and 2007 were anomalous, while 2009 and 2012 were the most correlated for blooms occurring in the spring season.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".