Monitoring Wheat Crop Growth Using a New Vegetation Index from Sentinel-1 GRD SAR Data
Bibliographic record
Abstract
Accurate and high-resolution spatio-temporal information on wheat growth is an essential factor for agronomic management and grain yield estimation. In this study, we propose a new vegetation descriptor from the Sentinel-1 Synthetic Aperture Radar (SAR) GRD data for monitoring the growth stages of wheat. We also assess the performance of the proposed vegetation descriptor for estimating wheat biophysical parameters: Plant Area Index (PAI), Dry Biomass (DB), and Vegetation Water Content (VWC) over the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) test site in Manitoba, Canada. The proposed vegetation descriptor produced good correlation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(R^{2})$</tex> with the biophysical parameters of wheat: 0.63 (PAI), 0.64 (DB), and 0.57 (VWC) compared to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sigma_{\text{VH}}^{\mathrm{o}}/\sigma_{\text{VV}}^{\mathrm{o}}$</tex> and the dual-pol Radar Vegetation Index (RVI).
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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".