Extraction of Winter Wheat Planting Area Based on Data of MODIS EVI Time-series
Bibliographic record
Abstract
Using the MODIS EVI series in integrated with the growth status of winter wheat,the growing area of winter wheat in Henan province was extracted.The results showed that in EVI′ s feature space,the winter wheat had its unique spectrum series trait.After green-up,the EVI of the winter wheat had an overall gradual increasing trend and then followed a decreasing trend after flowering.The decreasing rate became higher after grouting.The decision tree classifier(CART) was used to extract the winter wheat growing area.There was a minor 482 000 hm2 of difference between extracted number and the number officially publicized.The accuracy of extracted winter wheat growing area reached 90.88%.The EVI time series spectrum can clearly reflect the physical meanings of crop growth.Using the remote sensing classification method of the MODIS EVI time series spectrum can accurately extract the winter wheat growing area,and meet the needs of monitoring winter wheat growth and yield estimation by remote sensing.
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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) | 0.001 | 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 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".