Evaluation of MODIS and VIIRS cloud-gap-filled snow-cover products for production of an Earth science data record
Bibliographic record
Abstract
Abstract. MODerate resolution Imaging Spectroradiometer (MODIS) cryosphere products that have been available since the launch of the Terra MODIS in 2000 and the Aqua MODIS in 2002 include snow-cover extent (swath, daily and eight-day composites) and daily snow albedo. These products are used in hydrological modeling and studies of local and regional climate, and are increasingly being used to study regional hydrological and climatological changes over time. Reprocessing of the complete snow-cover data record, from Collection 5 (C5) to Collection 6 (C6) and Collection 6.1 (C6.1), has led to improvements in the MODIS product suite. Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products have been available since 2011, and are currently being reprocessed for Collection 2 (C2). To address the need for a cloud-reduced or cloud-free daily snow product for both MODIS and VIIRS, a new daily cloud-gap filled snow-cover product was developed for MODIS C6.1 and VIIRS C2 processing. MOD10A1F (Terra) and MYD10A1F (Aqua) are daily, 500-m resolution cloud-gap filled (CGF) snow-cover map products from MODIS. VNP10A1F is the 375-m resolution CGF snow map from VIIRS. The CGF maps provide daily cloud-free snow maps, along with cloud-persistence maps showing the age of the snow or non-snow observation in each pixel. Work is ongoing to evaluate and document uncertainties in the MODIS and VIIRS standard daily CGF snow-cover products. Analysis of the MOD/MYD10A1F products for study areas in the western United States shows excellent results in terms of accuracy of snow-cover mapping. When there are frequent clear-sky episodes, MODIS is able to capture enough clear views of the surface to produce accurate snow-cover information and snow maps. Even in the extensively-cloud-covered northeastern United States during winter months, snow maps from MODIS CGF products are useful, though the snow maps are likely to miss some snow, particularly during the spring snowmelt period when snow may fall and melt within a day or two, before the clouds clear from the storm that deposited the snow. Comparisons between the Terra and Aqua CGF snow maps have revealed differences that are related to cloud masking in the two algorithms. We conclude that the MODIS Terra CGF is the more accurate MODIS snow-cover product, and should therefore be the basis of an Environmental Science Data Record that will extend the CGF data record from the Terra MODIS beginning in 2000 through the VIIRS era, at least through the early 2030s.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".