Utilizing both Radarsat-2 And TerraSAR-X polarimetrie data for crop growth stages estimation
Bibliographic record
Abstract
This paper uses RADARSAT-2 quad Polarimetrie Synthetic Aperture Radar and TerraSAR-X dual polarimetrie SAR data to estimate agriculture crop growth stages. Ten RADARSAT-2 Fine Quad Wide beam modes data and 13 sets of Stripmap TerraSAR-X data with duel HH and VV polarizations were used in this study. Polarimetrie features such as differential reflectivity bands ratio, entropy, anisotropy, alpha angle, lambda, scattering diversity and polarization index were extracted and evaluated for wheat and canola crop types. The results from both RADARSAT-2 and TerraSAR-X data were compared; they demonstrated clear correlations between crop growth stages obtained from the ground truth data and polarimetric parameters. A stepwise regression feature selection technique was employed for determine the most suitable parameters for different crop types by utilizing both RADARSAT-2 and TerraSAR-X data. By integrating of the selected polarimetrie parameters from both Radarsat-2 and TerraSAR-X data, the more aeeurate estimate of the erop growth stages was aehieved and validated using erop growth stage ground truth data. The estimation of erop growth stages was performed on the elassified erop image subsets aequired at different dates. The results indieates that the mean square errors of the estimated erop growth stages are redueed using integrated/combined polarimetrie parameters from both RADARSAT-2 and TerraSAR-X data, as opposed to using a single sensor.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".