Cross and Col-Pol Phase Difference Related to Crop Structures in the Quad-Pol SAR Data and its Potential for Crop Monitoring
Bibliographic record
Abstract
The amplitude and phase of synthetic aperture radar (SAR) backscatter coefficients are sensitive to surface dielectric constant, canopy structure, and surface roughness. Particularly, quad-pol SAR observations which provide HH, VV, and HV/VV polarimetry information are more beneficial for crop monitoring compared with single and dual-pol data. Considering the difference of penetrability of horizontal and vertical signals over oriented canopy structure, we explores the potential of co-pol and cross-pol phase difference, like ∆ϕHH−VVand ∆ϕHH−HVand ∆ϕVV−VH, for crop phenology and structure monitoring. Besides, time-series variation of phase difference is supposed to be able to track crop growth. This paper introduces the form of the co-pol and cross-pol phase difference derived from Sinclare scattering matrix (S2×2) and the covariance matrix (C3×3). Subsequently, the mechanism of phase difference relating with the scattering phase center, the oriented crop structure, and the penetration depths of different polarization are provided. Time-series L-band quad-pol UAVSAR data which are collected from June 22 to July 17, 2012 in Winnipeg, Canada are used for the analysis of temporal and spacial phase difference characteristics over canola, soybean, and wheat fields. Results indicate significant potential of co-pol and cross-pol phase differences in crop structure and phenology monitoring. Especially, the evolution of time-series ∆ϕHH−VVis more consistent with taht of LAI measurements compared with the cross-pol phase difference.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".