RADARSAT-2 Beam Mode Selection for Surface Water and Flooded Vegetation Mapping
Bibliographic record
Abstract
Synthetic Aperture Radar (SAR) is well known for its ability to map surface water. There are a number of SAR satellites providing data for this application including the Canadian RADARSAT-2 system. RADARSAT-2 has a wide range of beam modes and some users may be intimidated by the variety of choices and have a difficult time deciding on the most appropriate beam mode. This technical note addresses some issues behind beam mode and polarization selection for surface water mapping with RADARSAT-2 and the upcoming RADARSAT Constellation Mission (RCM). This includes the impacts of resolution, wind effects, and the best mode for flooded vegetation detection. The results show that high resolution modes like the single polarized Spotlight are best for accurately delineating the surface water edge and small patches of flooded terrain. The addition of the cross-polarization available in other beam modes can provide useful surface water information in windy or rough surface conditions because there is little effect on the RADAR backscatter compared to the HH single polarization. For accurately delineating flooded vegetation, a polarimetric or compact polarimetric mode is best because the phase is maintained, which allows the user to apply polarimetric decompositions models to help separate the RADAR backscatter.
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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 | 0.001 |
| 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) | 0.004 | 0.002 |
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".