A phenology-weighted cross-correlation method for long-term monitoring of non-grain cultivation expansion in the Guanzhong Plain, China
Bibliographic record
Abstract
Mapping spatio-temporal patterns of non-grain cultivation is critical for understanding agricultural land-use transitions and their implications for regional food security. This study developed a phenology-enhanced classification method integrating the Cross-Correlogram Spectral Matching (CCSM) algorithm with a weighted absolute value distance to distinguish grain and non-grain crops in China’s Guanzhong Plain from 2000 to 2023. Using MODIS EVI time series, we identified six key phenological stages to improve classification accuracy. The method achieved an overall accuracy of 93% and a Kappa coefficient of 0.90. Results revealed that non-grain cultivation expanded significantly, covering 9,142 km² (54% of cropland) by 2023. Spatiotemporal analysis identified clear core agglomeration zones and a stable spatial structure, with limited directional expansion but intensified local clustering. This study provides a robust tool for monitoring non-grain dynamics and highlights the need for spatially targeted land-use governance to ensure sustainable agricultural development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 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".