Providing crop information using RADARSAT-1 and satellite optical imagery
Bibliographic record
Abstract
In 1997, the Canada Centre for Remote Sensing acquired RADARSAT-1, SPOT and IRS-1C imagery over an agricultural site in western Canada. These data were used to address the information content of RADARSAT-1 imagery for mapping crop type and for providing information on crop condition, and to explore the implications of crop growth stage on crop monitoring with radar imagery. The use of radar for crop mapping is particularly attractive because of its all weather capability and the sensitivity of microwaves to canopy structure and moisture. Results from this study indicated that multi-date RADARSAT-1 imagery, with or without satellite optical imagery, can provide accurate information about crop types, although timing of image acquisition was important. Regression analysis established that some indicators of crop vigour - in particular leaf area index and crop height - were correlated with backscatter. The highest correlations were for wheat and potatoes. However, backscatter was insensitive to variations in corn growth and only moderately sensitive to differences in indicators of canola crop condition. Nevertheless, this study clearly demonstrates that multi-temporal RADARSAT-1 imagery can be used to provide useful crop information.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".