Spectral band difference effects on vegetation indices derived from multiple satellite sensor data
Bibliographic record
Abstract
Vegetation indices based on satellite image data are widely used for change monitoring but, when derived from different satellite sensors, differ as a function of the uncorrectable differences between the analogous spectral bands used to generate them. This is an important issue because multiple satellite sensors in the Landsat class or in the AVHRR and MODIS classes are being used increasingly to monitor vegetation dynamics. This paper reports on an investigation of the impact of spectral band difference effects (SBDEs) on cross-comparisons between vegetation indices (VIs) derived from multiple satellite sensors in the solar-reflective spectral domain. Results from the simulation study, which encompassed three vegetation target types and eight VIs, indicate how large SBDEs can be and for which VI cross-comparisons they are significant. They also indicate that the spectral dependence of atmospheric gas transmittance is the key factor that gives rise to such significant spectral band difference effects. Among the vegetation indices considered, the GEMI proved to be the least sensitive to spectral dissimilarities between sensors, and hence GEMI is worth considering for quantitative monitoring of vegetation using images from multiple sensors. In the context of potential candidates to fill the forthcoming gap in Landsat data continuity, either one of the IRS-P6 sensors or the SPOT-5 HRG is preferable to the CBERS-2 HRCC as a replacement sensor from the standpoint of agreement with Landsat-based vegetation indices.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".