Atmospheric Correction of Sentinel-2 Satellite Images for Improvement of Vegetation Indices
Bibliographic record
Abstract
As global warming becomes serious with the increase of greenhouse gases, interest in the impact of climate change on the global environment and human life is increasing. In addition, local abnormal climate phenomena frequently appear according to climate change, and changes in the ecosystem are starting to be detected. Indicators that reflect climate change are diverse, such as agriculture, plant and animal distribution, biological seasons and ecology, and health. Here, the impact on agricultural production in particular seems to be very important Satellite products such as NDVI (Normalized Difference Vegetation Index), LAI (Leaf Area Index), FPAR (Fraction of Photosynthetically Active Radiation), and PET (Potential EvapoTranspiration) that reflect the growth and vitality of vegetation exist. Through this, changes in agricultural land or forests can be identified. At this time, strict atmospheric correction for high-resolution satellite data is essential in order to use accurate product.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".