Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors
Bibliographic record
Abstract
We report the results of a modeling study on the sensitivity of normalized difference vegetation index (NDVI) and surface reflectance to differences in instrument spectral response functions (SRF) for various Advanced Very High Resolution Radiometers (AVHRR) onboard the National Oceanic and Atmospheric Administration's (NOAA) satellites NOAA-6–16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Vegetation sensor (VGT), and the Global Imager (GLI). Modeling results were validated against real satellite observations employing AVHRR/NOAA-14 and -15 and MODIS, with a very good agreement. It is shown that for identical atmospheric state and similar surface spectral reflectance, the NDVI and spectral reflectances are sensitive to the sensor's SRF. Relative to a reference SRF for AVHRR/NOAA-9, the differences in reflectance among the AVHRRs range from −25% to 12% for visible channel (red) and from −2% to 4% for near-infrared (NIR) channel. Absolute change in NDVI among various AVHRRs ranged from −0.02 to 0.06. The most significant difference was observed for the AVHRR/3. Consistent results were obtained with the AVHRR sensors aboard the following afternoon satellites: NOAA-9, -11, and -12, whereas important discrepancies were found for other AVHRRs aboard NOAA-6 and -10 and especially those launched more recently (NOAA-15 and -16). Reflectance and NDVI measured by MODIS channels 1 and 2 also exhibit significant differences (up to 30–40%) relative to AVHRR. GLI and VGT have some specific features that should be taken into account when intercomparing surface or top of the atmosphere (TOA) reflectance as well as NDVI. Sensitivity of the SRF effect to variable atmospheric state (water vapor, aerosol, and ozone) was also investigated. Polynomial approximations are provided for bulk spectral correction with respect to AVHRR/NOAA-9.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".