Monitoring Crop Condition at Field Scales and at a Daily Time Step Using Synthetic Aperture Radar (SAR)
Bibliographic record
Abstract
Operations that rely on optically-based Normalized Difference Vegetation Index (NDVI) to track crop productivity, are negatively impacted by cloud cover. In this research, parameters from Synthetic Aperture Radar (SAR) imagery were calibrated to NDVI with the targeted outcome to integrate SAR into Canadian crop monitoring services. Data from fully polarimetric RADARSAT-2 and dual-polarization Sentinel-1B imagery were calibrated to Sentinel-2 NDVI. Random Forest Regression (RFR) and Least Squares Boosting (LSBoost) were tested to calibrate SAR to NDVI (SARcal-NDVI) for six crops (corn, canola, soybeans, wheat, oats and barley). RADARSAT-2 provided the best fit with R2 exceeding 0.84 for canola, 0.87 for wheat and 0.90 for corn and soybeans. Correlations were lower for oats (R2 of 0.72–0.77) and barley (R2 of 0.43–0.64) due to a limited number of fields. Although a global model yielded lower correlations (R2 of 0.80), this model would be easier to implement into operations. Total power, the first and second eigenvalues and VH backscatter were important in reducing model error. The SARcal-NDVI and Growing Degree Days were then integrated into a Crop Structure Dynamic Model to produce daily estimates of crop condition, at field scales. The next step is to test if these daily estimates of crop condition can improve crop yield forecasting for the Canadian agriculture sector.
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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".