Evaluating the Performance of Sentinel-3A OLCI Products in the Subarctic Northeast Pacific
Bibliographic record
Abstract
The subarctic northeast Pacific (SNEP) is a high-nitrate, low-chlorophyll (HNLC) region in the ocean, where phytoplankton growth and productivity are limited by iron. Moreover, there is a limited application of high spatial (300 m) and temporal resolution (daily) ocean color (OC) satellite imagery in studying the phytoplankton dynamics in this region. To address this issue, we aim to validate the remote sensing reflectance (Rrs; sr−1(λ)) and chlorophyll-a (Chla) concentration derived from the Polymer atmospheric correction algorithm against in situ data for the SNEP obtained during 2019 and 2020. Additionally, we performed qualitative analysis using weekly binned surface Chla maps to determine whether the product reflects the general pattern over a latitudinal and longitudinal domain. We processed the daily Level-1 image using Polymer and binned them weekly using Graphic Processing Tool (GPT). The validation results indicate that Polymer exhibits higher radiometric performance in the blue and green bands and fails to represent in situ Rrs in the red band. Furthermore, the Polymer slightly over- and underestimates reflectance between 0.0012 and 0.0018 sr−1 in the green band. On the other hand, excellent agreement was found between satellite-derived versus in situ Chla, followed by a slight overestimation of in situ Chla in the range from 0.17 to 0.28 mg/m3. The weekly binned Chla spatial map revealed a spatially homogeneous distribution of surface Chla in Central Alaska, but a substantial increase in Chla (≥0.7 mg/m3) was recorded toward Southeast Alaska (SEA) and the British Columbia (BC) shelf. Furthermore, Chla derived from latitudinal and longitudinal transects indicates high Chla toward 57°N and −135°W, respectively. Overall, the results of this study emphasize the need to obtain high-quality matchups from under-sampled oligotrophic waters, which are crucial for satellite validation, in addition to highlighting the importance of using high spatial and temporal resolution satellite imagery to study phytoplankton dynamics in the SNEP.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".