The effect of dew on the use of RADARSAT-1 for crop monitoring: Choosing between ascending and descending orbits
Bibliographic record
Abstract
Radar sensors, like RADARSAT-1, can be a valuable tool for monitoring agricultural crops. RADARSAT-1 imagery can be acquired regardless of cloud cover, and the satellite can be programmed to collect imagery in a wide range of beam modes and incidence angles. This flexibility significantly increases the revisit schedule, thereby ensuring that images can be acquired during key crop growth stages. Users also have the flexibility of choosing acquisitions during either ascending or descending orbits. However, the condition of agricultural targets can change diurnally, and consequently, care must be taken in choosing between RADARSAT-1's dawn and dusk orbits. In temperate regions, early morning dew is often present on the crop canopy at the time of the satellite overpass. Consequently, this study used fine mode dawn/dusk image pairs acquired over western Canada to examine the potential effect of dew on operational crop mapping. The data consistently demonstrated that backscatter increased when dew was present on the canopy. However, overall crop separability did not appear to be affected by the presence of dew. These results indicate that although choice of orbit is less important for crop classification, the probability of dew on the canopy must be carefully considered when users are extracting quantitative crop information from radar imagery.
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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 | 0.001 | 0.007 |
| 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) | 0.001 | 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".