Validation of MODIS, VEGETATION, and GOES+SSM/I snow cover products over Canada based on surface snow depth observations
Bibliographic record
Abstract
The rate and pattern of snow melt control both hydrological and ecological factors. Snow cover maps derived by different satellite sensors can differ considerably from surface observations due to different spatial resolutions and snow cover classification algorithms. This article addresses issues related to the validation of three daily snow cover products over Canada: MODIS and GOES+SSM/I snow maps derived at 500m and 4km resolution, respectively for 2001, and VEGETATION snow maps derived at 1km resolution for 2000. The validation is based on surface snow depth observations from almost two thousand meteorological stations across Canada. The analysis is performed on a daily basis for the period of six months (January-June). A land cover map of Canada at 1km resolution is used to relate the differences within the validation to land cover types. The SPOT product shows an average agreement of 83% ad considerably high percentage of omission error. The MODIS and GOES+SSM/I products have similar percentage average agreements, 93% and 92%, respectively. Generally, less agreement is seen within the evergreen forest cover types, earlier in the snow season and during snow melt. The MODIS product exhibits a high percentage commission error for evergreen forests. The GOES+SSM/I product shows relatively similar ratios of omission and commission errors for all land cover types except deciduous forest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".