Polarimetric Decomposition for Monitoring Crop Growth Status
Bibliographic record
Abstract
This letter investigates the polarimetric decomposition for monitoring the crop growth status over agricultural fields. Based on an existing polarimetric decomposition, the vegetation volume scattering component is removed from the full polarimetric synthetic aperture radar (SAR). Then, the estimated crop orientation is combined with the dominant scattering mechanism in the remaining ground coherency matrix to define the vegetation growth indicators for the crop growth monitoring from SAR. The proposed method is evaluated on the time series of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data and the extensive ground truth measurements collected in the framework of the Soil Moisture Active Passive (SMAP) Validation Experiment in 2012. The results show that the scattering characteristics vary with the crop types and the phenological development stage. The random orientation is the most important case during the crop development period considered in this letter. For canola, corn, and wheat, the dihedral scattering is significant in the remaining ground coherency matrix at the early growth stage, and then, the surface scattering becomes important in the further growth. For soybean and pasture, the surface scattering dominates the remaining ground coherency matrix during the considered crop development stage. The vegetation growth indicators derived from UAVSAR data are well correlated with the ground measurements of crop height and biomass. This letter demonstrates, for the first time, the crop growth monitoring by using polarimetric decomposition via the vegetation orientation and scattering mechanisms.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".