Variations of Annual Minimum Snow and Ice Extent over Canada and Neighboring Landmass Derived from MODIS 250-m Imagery for 2000–2014
Bibliographic record
Abstract
. Snow and ice are important hydrological resources. Their minimum spatial extent over land, here referred to as annual minimum snow/ice (MSI) cover, plays a very important role as an indicator of long-term changes and baseline capacity for surface water storage. Data from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra satellite for the period of 2000–2014 were utilized in this study. The level-2 MODIS swath imagery for bands B1 to B7 was employed and the 500-m bands B3–B7 were spatially downscaled to a 250-m swath grid. The imagery is available daily with multiple overpasses. This allows for more accurate identification of annual minimum in comparison to high-resolution imagery (e.g., Landsat, ASTER, etc.) available at much coarser temporal rates. Atmospherically corrected 10-day clear-sky composites converted into normalized surface reflectance over the warm season (April 1 to September 20) were employed to identify persistent snow and ice presence. Results were compared with our previous results derived from the MODIS Circumpolar Arctic clear-sky composites, generated for the end of melting season, and showed smaller MSI extent by 24%, on average. Produced MSI distributions were also compared with the permanent snow and ice maps available from 6 global land cover datasets: (i) Global Land Cover GLC-2000, (ii & iii) European Space Agency's (ESA) Globcover circa 2005 and 2009, (iv–vi) land cover maps derived under the ESA Climate Change Initiative (CCI) for 2000, 2005, and 2010. Significant biases were discovered between various land cover datasets and our results. For example, GLC-2000 overestimated snow/ice extent by 194% (325,400 km2) for the Canadian Arctic. The biases over the entire landmass (excluding Greenland) are 135% (3.7 × 105 km2), 113% (3.0 × 105 km2), 89% (2.2 × 105 km2), and 28% (0.8 × 105 km2) between our results and GLC-2000, ESA Globcover 2005, ESA Globcover 2009, and ESA CCI datasets, correspondingly. The derived MSI extent was compared with Randolph Glacier Inventory (RGI) 4.0 and showed much better consistency (ranging from 1% to 15%).
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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".