In-season crop inventory using multi-angle and multi-pass RADARTSAT-2 SAR data over the Canadian prairies
Bibliographic record
Abstract
In response to the increasing demand on in-season crop inventory, this study presents results of early season crop identification and acreage estimates based on a random forest classifier using RADARSAT-2 fine quad (FQ) SAR data. Thirty RADARSAT-2 FQ SAR scenes acquired over Indian Head, Canada, during the 2009 AgriSAR campaign led by the European Space Agency (ESA) were analyzed. Consistent with results from other researches, this study revealed that the highest classification accuracies are achieved in mid to late season (early July to mid August) when most of the crops experiencing vegetative growth and early reproduction. In addition by incorporating multi-beam images, an increase in classification accuracy of 2% to 12% can be achieved. For images acquired close in time, shallower incidence angles usually give better classification accuracy compared with steeper incidence angles. In order to achieve optimal classification performance, both multi-temporal and multi-beam acquisitions should be combined. For major crops such as canola, spring wheat, lentil, and field peas, over 85% accuracies can be reached early in the growing season (early July) when multi-temporal multi-beam RADARSAT-2 FQ data are used.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".