A Method to Derive the Multispectral Surface Albedo Consistent with MODIS from Historical AVHRR and VGT Satellite Data
Bibliographic record
Abstract
Abstract Multispectral surface albedo and bidirectional properties are required for accurate determination of the surface and atmosphere solar radiation budget. A method is developed here to obtain time series of these surface characteristics consistent with the Moderate Resolution Imaging Spectroradiometer (MODIS) using historical satellite observations with limited spectral coverage available from NOAA Advanced Very High Resolution Radiometer (AVHRR) and VEGETATION/Satellite pour l’Observation de la Terre (SPOT). A nonlinear regression model was developed that relates retrievals from four spectral channels of VEGETATION/SPOT or three spectral channels of NOAA AVHRR with retrieval from each of the seven MODIS channels designed for land applications. The model also takes into account the surface land cover type, the normalized difference vegetation index, and the seasonal cycle. It was applied to generate surface albedo and bidirectional parameters of the seven MODIS-like spectral channels at a 10-day interval for the 1995–2004 period over the U.S. southern Great Plains. The relative retrieval accuracy for the MODIS channels replicated from AVHRR or VEGETATION/SPOT data was typically better than 5%. Correlation coefficients between replicated and original data varied from 0.92 to 0.98 for all channels except MODIS channel 5, where it was lower (0.77–0.84). The developed method provides valuable information for parameterization of spectral albedo in global climate models and can be extended to generate global multispectral data compatible with MODIS from historical AVHRR and VEGETATION/SPOT observations for the pre-MODIS era.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".