Canola Phenology Mapping Using Optical and Synthetic Aperture Radar (Sar) in Canada
Bibliographic record
Abstract
A plethora of studies have empirically demonstrated and linked canola heat stress susceptibility to substantial yield losses. Such biophysical phenomenon underscores the importance of timely and accurate monitoring of crop phenological events. Optical and Synthetic Aperture Radar (SAR) data, to meet such a requirement, have aided in developing diverse yet complementary information for crop monitoring. In this study, we investigate 47 satellite-based Land Surface Parameters (LSPs) from Sentinel-1 and -2 imagery to monitor canola phenology across arable lands in Saskatchewan, Canada. Daily ground reference phenological data were collected using trail-cameras installed across 28 canola fields. We constructed daily time-series profiles for each LSP by coupling a cubic interpolation algorithm with a Savitzky-Golay filtering. LSPs were correlated with ground reference data to investigate temporal trends and sensitivity of specific patterns to phenological events. Preliminary results indicate that SAR-based LSPs were most sensitive to canola bolting and pod maturity, while flowering and associated stages are mapped efficiently through optical indices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".