Remote Sensing of Phytoplankton Size Class in Northwest Atlantic from 1998 to 2016: Bio-Optical Algorithms Comparison and Application
Bibliographic record
Abstract
Phytoplankton community structure and phytoplankton size class (PSC) are linked to ecological and biogeochemical changes in the oceanic environment. Many models developed to obtain the fraction of PSCs from satellite remote sensing have only been evaluated in open oceans, and very limited effort has been carried out to report on the performance of these PSC models in productive continental shelf waters. In this study, we evaluated the performance of nine PSC models in the coastal Northwest Atlantic (NWA) by comparison of in situ phytoplankton pigment measurements with coincidental satellite data from the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate-resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). Our results show that no PSC model retrieved all three phytoplankton size classes (pico-, nano-, and micro-phytoplankton) with reliable accuracy in the region of interest. In particular, these PSC models showed poor performance for retrieval of the picophytoplankton fraction of total phytoplankton in our study region, which could be related to the under-representation of pico-dominated samples in the productive waters of the NWA. For the accuracy of retrieved microphytoplankton and combined nano–pico phytoplankton fraction, the regional model developed by Devred et al. (2011) yielded the best result, followed by the model of Brewin et al. (2011). The model of Devred et al. (2011) was applied to satellite-derived chlorophyll-a concentration from the Ocean Color Climate Change Initiative (OC-CCI) archive in the NWA from 1998 to 2016. We report solely on the microphytoplankton biomass and fraction given the inverse relationship that exists with the nano–pico class. The multi-decadal trend along with the deseasonalized trend of microphytoplankton fraction was computed and analyzed for six biogeochemical provinces located in the NWA. Over the 19-year time series, there were significant, positive trends for four of the six provinces, with a slope of 0.36%·yr−1 in the Northwest Continental Shelf (NWCS), 0.25%·yr−1 in the Arctic Waters (ARCT), 0.12%·yr−1 in the Slope Waters (SW) and 0.06%·yr−1 in the Gulf Stream (GFST). Strong positive anomalies of microphytoplankton fraction were found in winter months in NWCS between 2009 and 2014, which could be associated with changes in environmental factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".