Inter-comparison of Terra and Aqua MODIS Feflective Solar Bands using Suomi NPP VIIRS
Bibliographic record
Abstract
VIIRS (Visible Infrared Imager Radiometer Suite) onboard the Suomi NPP (National Polar-orbiting Partnership) satellite has been acquiring Earth observations for more than a year. During that time, SNO (Simultaneous Nadir Overpass) events have provided many opportunities for inter-comparisons between VIIRS and the MODIS (Moderate Resolution Imaging Spectroradiometer) instruments from the Aqua and Terra satellites. The SNOs have occurred over snow-covered Antarctica, which provided bright surfaces in the visible and near-infrared bands, as well as over northern Alaska, Canada, Greenland, Scandinavia, Siberia, and ocean, with both dark and bright scenes that frequently include clouds. Top-of-atmosphere reflectance values measured by VIIRS during the SNO events were found to be highly correlated with the MODIS data for the corresponding spectral bands. The SNO comparisons have helped improve VIIRS radiometric performance and accuracy of its data products. Comparing VIIRS with two MODIS instruments has not only reduced uncertainty of the SNO measurements, but also allowed for inter-comparisons between the MODIS instruments from two spacecraft. For most of the reflective solar bands, biases between VIIRS and MODIS are small and are similar for both MODIS instruments. Radiative transfer modeling has shown that the observed discrepancies can be attributed to differences between spectral responses of VIIRS and MODIS. With some exceptions, inferred biases between MODIS measurements are within uncertainty of the radiometric calibrations. The largest bias occurs in the case of the 488-nm band from MODIS on Terra, despite improved radiometric calibration of the MODIS Level 1B Collection 6 data products. For other bands, biases between Aqua and Terra MODIS Collection 6 data are smaller than in Collection 5.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".