Simulation and analysis on the influence of different types of soil background on the remote sensing information of wheat NDVI of farmland
Bibliographic record
Abstract
It is quite confusing to effectively monitor and precisely evaluate growing conditions of wheat by using normalized differential vegetation index based on pixel size (NDVIp) as they are significantly different when acquired by the wheat of same growth status with different types of soil background. The wheat canopy normalized differential vegetation index(NDVIc) acquired by one scene of multi-spectral remote sensing image with no soil disturbance, pure black background are similar while soil backgrounds are often different. In view of this situation, based on the fixed wheat canopy spectrum which means the NDVIc is a constant value, this paper selects 9 typical soil types in our country as background in order to study the influence of different soil background types on NDVIp of wheat and analyze the sensitivity of NDVIp of wheat to the vegetation coverage simulated by diverse liner mixed ratio of wheat canopy and soil background on the remote-sensing's pixel scale. The results show that: (1)wheat NDVIp of farmland increases along with the increment of vegetation coverage under the same type of soil background, and vice versa; (2)wheat NDVIp of farmland varies greatly with different soil background types, and the difference decreases while the vegetation coverage exceeds 25%; (3) NDVIp sensitivity also shows a quite difference to vegetation coverage under the diverse soil background types. The influence of soil background on NDVIp sensitivity is the lowest when the vegetation coverage ranges from 25% to 35%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".