Mapping winter wheat crop traits dynamic change and growth performance for variable rate application using Sentinel-1 and Sentinel-2
Bibliographic record
Abstract
Site specific crop management for variable rate application is extensively recognized as a method for distributing agricultural input unevenly across a field, tailored to the diverse requirement of different areas. From the previous study, this approach proven to reduce agricultural input expenses by 10 % without impacting yield and ensure environmental sustainability. This study presents a new approach to delineate management zones for precision agriculture using crop biophysical property variability assessment within winter wheat fields. A multivariate random forest framework was developed to estimate winter wheat’s biophysical properties within fields from surface reflectance and backscatters of Sentinel-1 and Sentinel-2. Combining Sentinel-1 and Sentinel-2 data resulted in more precise estimation of the green area index (R²=0.98), aboveground dry biomass (R²=0.90), plant height (R²=0.94), and leaf nitrogen content (R²=0.78). Sentinel-2 alone was particularly effective in estimating shoot density (R²=0.94). These estimates were then used to create management zones for precision agriculture, classified based on agronomic performance benchmarks. The fuzzy c-mean clustering algorithm helped generate homogeneous management zones, considering the biophysical variations within fields.The ultimate goal is to integrate these biophysical property maps and management zones into crop management workflows. This integration will assist farmers in recognizing field variability and understanding its causes. Moreover, the spatial distribution of these zones supports variable rate application, guiding farmers towards more efficient, profitable, and sustainable crop management practices. • Winter wheat crop trait dynamic change in IE and the UK over 2 cycles. • Best specific window to monitor SD, GAI, PH, AGDB and LNC. • Pixel-level fusion of Sentinel-1 and Sentinel-2 data. • Mapping crop management zone using agronomic performance benchmark. • Framework to integrate crop property with crop management workflow to support VRA.
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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".