Characterization of canola canopies using optical and SAR imagery
Bibliographic record
Abstract
Normalized Difference Vegetation Index (NDVI) values extracted from remotely sensed optical imagery are used ubiquitously to monitor crop condition. However, challenges in the operational use of optical imagery are well documented making it difficult to capture measures of crop condition during critical phenology stages when clouds obscure. This study investigates the integration of Synthetic Aperture Radar (SAR) and optical imagery to characterize the condition of crop canopies in order to deliver daily measures of NDVI during the entire growing season. Multitemporal C-band polarimetric RADARSAT-2 SAR data and RapidEye images were acquired in 2012 for a study site in western Canada. SAR polarimetric parameters and NDVI were extracted. The temporal variations in SAR polarimetric parameters and NDVI were interpreted with respect to the development of the canola canopy. Optical NDVI was statistically related with SAR polarimetric parameters over test canola fields. Significant correlations were documented between a number of SAR polarimetric parameters and optical NDVI, in particular with respect to HV backscatter, span, volume scattering of the Freeman Durden decomposition and the radar vegetation index, with R-values of 0.83, 0.72, 0.81 and 0.71 respectively. Based on the statistical analysis, SAR polarimetric parameters were calibrated to optical NDVI, creating a SAR-calibrated NDVI (SARc-NDVI)). A canopy structure dynamics model (CSDM) was fitted to the SARc-NDVI, providing a seasonal temporal vegetation index curve. The coupling of NDVI from optical and SAR imagery with a CSDM demonstrates the potential to derive daily measures of crop condition over the entire growing season.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".