Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping
Bibliographic record
Abstract
Land-cover monitoring is one of the core applications of remote sensing. Monitoring and mapping changes in the distribution of agricultural land covers provide a reliable source of information that helps environmental sustainability and supports agricultural policies. Synthetic Aperture Radar (SAR) can contribute considerably to this monitoring effort. The first objective of this research is to extend the use of time series of polarimetric data for land-cover classification using a decision tree classification algorithm. With this aim, RADARSAT-2 (quad-pol) and Sentinel-1 (dual-pol) data were acquired over an area of 600 km2 in central Spain. Ten polarimetric observables were derived from both datasets and seven scenarios were created with different sets of observables to evaluate a multitemporal parcel-based approach for classifying eleven land-cover types, most of which were agricultural crops. The study demonstrates that good overall accuracies, greater than 83%, were achieved for all of the different proposed scenarios and the scenario with all RADARSAT-2 polarimetric observables was the best option (89.1%). Very high accuracies were also obtained when dual-pol data from RADARSAT-2 or Sentinel-1 were used to classify the data, with overall accuracies of 87.1% and 86%, respectively. In terms of individual crop accuracy, rapeseed achieved at least 95% of a producer’s accuracy for all scenarios and that was followed by the spring cereals (wheat and barley), which achieved high producer’s accuracies (79.9%-95.3%) and user’s accuracies (85.5% and 93.7%).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".