Intercomparison of satellite-derived snow-cover maps
Bibliographic record
Abstract
Abstract In anticipation of the launch of the Earth Observing System (EOS) Terra, and the Aqua spacecraft in 1999 and 2000, respectively, efforts are ongoing to determine errors of satellite-derived snow-cover maps. EOS Moderate Resolution Imaging Spectrora-diometer (MODIS) and Advanced Microwave Scanning Radiometer-E (AMSR-E) snow-cover products will be produced. For this study we compare snow maps covering the same study areas in Canada and the United States, acquired from different sensors using different snow-mapping algorithms. Four locations are studied: (1) Saskatchewan, Canada; (2) New England (New Hampshire, Vermont and Massachusetts) and eastern New York; (3) central Idaho and western Montana; and (4) North and South Dakota. Snow maps were produced using a prototype MODIS snow-mapping algorithm from Landsat Thematic Mapper (TM) scenes of each study area at 30 m and when the TM data were degraded to 1 km resolution. U.S. National Operational Hydrologic Remote Sensing Center (NOHRSC) 1km resolution snow maps were also used, as were snow maps derived from 0.5° × 0.5° resolution Special Sensor Microwave Imager (SSM/I) data. A land-cover map derived from the International Geosphere-Biosphere Program land-cover map of North America was also registered to the scenes. The TM, NOHRSC and SSM/ I snow maps, and land-cover maps were compared digitally. In most cases, TM-derived maps show less snow cover than the NOHRSC and SSM/I maps because areas of incomplete snow cover in forests (e.g. tree canopies, branches and trunks) are seen in the TM data but not in the coarser-resolution maps which may map the areas as completely snow-covered. The snow maps generally agree with respect to the spatial variability of the snow cover. The 30 m resolutionTM data provide the most accurate snow maps, and are thus used as the baseline for comparison with the other maps. Results show that the changes in amount of snow cover, as compared to to the 30 m resolution TM maps, are lowest using the TM 1km resolution maps, at 0–40%. The greatest change (>100%) is found in the New England study area, probably due to the presence of patchy snow cover. A scene with patchy snow cover is more difficult to map accurately than is a scene with a well-defined snowline such as is found on the North and South Dakota scene where the changes were 0–40%. There are also some important differences in the amount of snow mapped using the two different SSM/I algorithms because they utilize different channels.
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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) | 0.011 | 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".