Validation of VEGETATION, MODIS, and GOES + SSM/I snow‐cover products over Canada based on surface snow depth observations
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The ability to map the areal depletion of snow accurately is important for operational decision making (e.g. reservoir management), for correct specification of boundary conditions in numerical weather‐prediction models, and for modelling atmospheric, hydrological and ecological processes. A number of satellite‐derived snow‐cover products are available in real time; however, these can differ considerably due to variations in sensor and platform characteristics, data pre‐processing methods, and the particular snow‐cover classification algorithms employed. This article evaluates the performance of three daily snow‐cover products over Canada: (1) Terra Moderate Resolution Imaging Spectroradiometer (MODIS) snow‐cover maps provided at 500 m spatial resolution for 2001; (2) National Oceanic Atmospheric Administration (NOAA) GOES + SSM/I snow maps provided at 4 km resolution for 2001 (∼30 km resolution SSM/I data were used for cloud‐covered areas); (3) SPOT‐4 VEGETATION (VGT) snow maps derived at 1 km resolution for 2000. An evaluation of the snow‐cover products with daily surface snow depth observations collected from almost 2000 meteorological stations across Canada revealed that the VGT snow product used in this study may not be suitable for snow mapping in Canada because of a significant bias towards mapping snow‐free conditions. The MODIS and NOAA products showed similar reasonable levels of agreement with ground data, ranging from approximately 80% to 100% on a monthly basis. Somewhat lower agreement was found in January, when solar zenith angles are large, suggesting that better correction for tree and surface shadow effects is needed in current snow‐cover mapping algorithms. The lowest agreement was seen during snowmelt, mainly in forest areas. Comparison of MODIS agreement statistics between sparse and dense conifer regions indicated that the effect of non‐representativenes of surface snow depth observations was on the order of 10% disagreement. The NOAA product was found to be the most consistent among land cover types and had the highest percentage of cloud‐free pixels. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 it