The Potential of RADARSAT-2 for Crop Mapping and Assessing Crop Condition
Bibliographic record
Abstract
Synthetic aperture radar (SAR) research over the last two decades has confirmed that microwaves are sensitive to both soils and crop characteristics. Results using multi-temporal RADARSAT-1 imagery have confirmed that C-HH backscatter can detect differences in crop type, crop growth stage and crop indicators like crop height, biomass and leaf area index. Active microwave systems have a significant advantage over optical systems, particularly for crop monitoring, since SAR acquisitions are not impeded by cloud cover. The multi-beam modes associated with RADARSAT-1 also provide significant flexibility related to the timing, spatial resolution and incidence angle of the acquired imagery. Building on the success of RADARSAT-1, the announcement of the launch of RADARSAT-2 opens up new opportunities for agriculture and land cover mapping. The multi-polarized configuration of RADARSAT-2 is likely to provide more information related to crop structure and crop condition. In preparation for the availability of RADARSAT-2 data, the Canada Centre of Remote Sensing (CCRS) has been gathering airborne multi-polarized imagery to assess the added information content of this multi-dimensional data. CCRS flew the airborne CV-580 SAR over two sites in Ontario during the 1998 and 1999 field seasons. These data are being used to address the sensitivity of multi-polarized SAR data to characteristics of corn, wheat and soybean crops. This paper provides preliminary results of this analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".