Integration of Multi-Polarized SAR Data and High Spatial Optical Imagery For Precision Farming
Bibliographic record
Abstract
Monitoring the condition of agricultural crops requires that soils and crop information is readily available throughout the growing season. Visible-infrared wavelengths are sensitive to variations in crop and soil conditions. Although optical imagery can be used to map crop characteristics, cloud cover can impede the use of these data for operational monitoring. RADARSAT-1 can provide crop information, but because imagery is acquired in only one transmit-receive polarization, multi-temporal data sets are required. Radars that acquire imagery in multiple polarizations, like RADARSAT-2, are likely to provide much more information on both crop and soil characteristics. In 1998 and 1999, airborne C-band polarimetric synthetic aperture radar (SAR) imagery was acquired over two sites in Ontario (Canada). These data are currently being analyzed to assess what crop information polarimetric sensors, like RADARSAT-2, will provide for site specific crop monitoring. In addition to airborne SAR, satellite and airborne optical images were acquired over these test sites. Soil moisture measurements and crop information were collected on corn, soybean and wheat fields during the airborne acquisitions, to support interpretation of the remotely sensed images. Preliminary results indicate that although backscatter from corn fields saturates once crop growth is significant, multi-polarized linear and circular radar configurations do provide some information on grain and soybean crop condition.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".