An improved sea ice detection algorithm using MODIS: application as a new European sea ice extent indicator
Bibliographic record
Abstract
The continued loss of sea ice in the Northern Hemisphere due to global warming poses a threat to biota and human activities, evidencing the necessity of efficient sea ice monitoring tools. Aiming at the creation of an improved sea ice extent indicator covering the European regional seas, the new IceMap500 algorithm has been developed to classify sea ice and water at a resolution of 500 m at nadir. IceMap500 features a classification strategy built upon previous MODIS sea ice extent algorithms and a new method to reclassify areas affected by resolution-breaking features inherited from the MODIS cloud mask. This approach results in an enlargement of mapped area, a reduction of potential error sources and a better delineation of the sea ice edge, while still systematically achieving accuracies above 90 %, as obtained by manual validation. Swath maps have been aggregated at a monthly scale to obtain sea ice extent with a method that is sensitive to spatio-temporal variations in the sea ice cover and that can be used as an additional error filter. The resulting dataset, covering the months of maximum and minimum sea ice extent (i.e. March and September) over 2 decades (from 2000 to 2019), demonstrates the algorithm's applicability as a monitoring tool and as an indicator, illustrating the sea ice decline at a regional scale. The European sea regions located in the Arctic, NE Atlantic and Barents seas display clear negative trends in both March (−27.98 ± 6.01 × 10 3 km 2 yr −1 ) and September (−16.47 ± 5.66 × 10 3 km 2 yr −1 ). Such trends indicate that the sea ice cover is shrinking at a rate of ∼ 9 % and ∼ 13 % per decade, respectively, even though the sea ice extent loss is comparatively ∼ 70 % greater in March.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".