Remote Sensing of Ice Phenology and Dynamics of Europe’s Largest Coastal Lagoon (The Curonian Lagoon)
Bibliographic record
Abstract
A first-ever spatially detailed record of ice cover conditions in the Curonian Lagoon (CL), Europe’s largest coastal lagoon located in the southeastern Baltic Sea, is presented. The multi-mission synthetic aperture radar (SAR) measurements acquired in 2002–2017 by Envisat ASAR, RADARSAT-2, Sentinel-1 A/B, and supplemented by the cloud-free moderate imaging spectroradiometer (MODIS) data, are used to document the ice cover properties in the CL. As shown, satellite observations reveal a better performance over in situ records in defining the key stages of ice formation and decay in the CL. Using advantages of both data sources, an updated ice season duration (ISD) record is obtained to adequately describe the ice cover season in the CL. High-resolution ISD maps provide important spatial details of ice growth and decay in the CL. As found, ice cover resides longest in the south-eastern CL and along the eastern coast, including the Nemunas Delta, while the shortest ice season is observed in the northern CL. During the melting season, the ice melt pattern is clearly shaped by the direction of prevailing winds, and ice drift velocities obtained from a limited number of observations range within 0.03–0.14 m/s. The pronounced shortening of the ice season duration in the CL is observed at a rate of 1.6–2.3 days year‒1 during 2002–2017, which is much higher than reported for the nearby Baltic Sea regions. While the timing of the freeze onset and full freezing has not changed much, the dates of the final melt onset and last observation of ice have a clear decreasing pattern toward an earlier ice break-up and complete melt-off due to an increase of air temperature strongly linked to the North Atlantic Oscillation (NAO). Notably, the correlation between the ISD, air temperature, and winter NAO index is substantially higher when considering the lagoon-averaged ISD values derived from satellite observations compared to those derived from coastal records. The latter clearly demonstrated the richness of the satellite observations that should definitely be exploited in regional ice monitoring programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".