Satellite Microwave Assessment of Northern Hemisphere Lake Ice Phenology from 2002 to 2015
Bibliographic record
Abstract
Abstract. Abstract. A new automated method for satellite assessment of seasonal lake ice phenology at 5-km resolution was developed for all lake pixels (water coverage ≥ 90 %) in the Northern Hemisphere using 36.5 GHz, H-polarized brightness temperature (Tb) observations from the Advanced Microwave Scanning Radiometer (AMSR-E/2) sensors. The lake phenology metrics include seasonal timing and duration of annual ice cover. A Moving t-Test (MTT) algorithm allows for automated lake ice retrievals with daily temporal fidelity and 5-km resolution gridding. The resulting ice phenology record shows strong agreement with available ground-based observations from the Global Lake and River Ice Phenology Database (95.4 % temporal agreement), and favourable correlations (R) with alternative ice phenology records from the Interactive Multisensor Snow and Ice Mapping System (R = 0.84 for water clear of ice [WCI] dates; R = 0.41 for complete freeze over [CFO] dates) and Canadian Ice Service (R = 0.86 for WCI dates; R = 0.69 for CFO dates). Analysis of the resulting 12-year (2002-2015) AMSR ice record indicates increasingly shorter ice-cover duration for 43 out of 71 (60.6 %) Northern Hemisphere lakes examined, with significant (p < 0.05) regional trends toward earlier ice melting for only five lakes. Higher latitude lakes reveal more widespread and larger trends toward shorter ice cover duration than lower latitude lakes, consistent with enhanced polar warming. This study documents a new satellite-based approach for rapid assessment and regional monitoring of seasonal ice cover changes over large lakes, with resulting accuracy suitable for global change studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".