Evaluation of MODIS-Aqua Atmospheric Correction and Chlorophyll Products of Western North American Coastal Waters Based on 13 Years of Data
Bibliographic record
Abstract
There is an increasing need for satellite-derived accurate chlorophyll-a concentration (chla) products to improve fisheries management in coastal regions. However, the methods used to derive these products have to be evaluated, so the associated uncertainties are known. The performance of three atmospheric correction methods, the near infrared (NIR), the shortwave infrared (SWIR), and the Management Unit of the North Seas Mathematical Models with an additional modification (MUMM + SWIR), and derived chla products based on the Moderate Resolution Imaging Spectroradiometer AQUA (MODIS) images acquired from 2002 to 2014 over the west coast of Canada and the United States were evaluated. The atmospherically corrected products and above-water reflectance were compared with in situ AERONET (N ~ 650) and above-water reflectance (N ~ 34) data, and the Ocean Color 3 MODIS (OC3M)-derived chla were compared with in situ chla measurements (N ~ 82). The statistical analysis indicated that the MUMM + SWIR method was the most appropriate for this region, with relatively good retrievals of the atmospheric products, improved retrieval of remote sensing reflectance with bias lower than 20% for the OC3M bands, and improved retrievals of chla (r = 0.83, slope = 0.89, logRMSE = 0.33 mg m−3 for ±1 h). The poorest chla retrievals were achieved with the SWIR and NIR methods. These results represent the most comprehensive satellite data analysis of MODIS retrievals for this region and provide a framework for the MUMM + SWIR method that can be further tested in other coastal regions of the world.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".