Crop-type identification potential of Radarsat-2 and MODIS images for the Canadian prairies
Bibliographic record
Abstract
Owing to their high-frequency revisit and weather independence with high image resolution, Moderate Resolution Imaging Spectroradiometer (MODIS) and Radarsat-2 SAR (ScanSAR (synthetic aperture radar)), respectively, provide data suitable for regional-level crop-type identification in the Canadian prairies. The challenge remains in optimally combining data from the two sources, to identify crop types in individual fields. This study investigated an approach based on image fusion and a specially designed classification to obtain a result with the high spatial detail of ScanSAR and the spectral information from MODIS. The methodology employs a wavelet-IHS (intensity, hue, and saturation) combined image fusion method to enhance the spatial resolution of the MODIS data using ScanSAR data, followed by a multiresolution segmentation process supported by a road network database to generate the final classification. The fusion-classification approach yielded a result suitable for both visual and digital analysis. The overall classification accuracy of the fused data set was about 72%, higher than accuracies achieved for ScanSAR images (transformed as principal components), the MODIS data alone, or a combination of the ScanSAR principal components and MODIS data. While further investigation is warranted, this approach appears to have the attributes required for operational crop-type identification in situations where such information is required frequently and over large areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".