The sensitivity of RADARSAT-2 quad-polarization SAR data to crop LAI
Bibliographic record
Abstract
The object of this paper is to investigate the relationship between polarimetric SAR information and LAI. RADARSAT- 2 Fine Quad-pol SLC data with shallower and steeper incidence angles were programmed throughout the 2008 growing season. Optical data were acquired using a hyperspectral CASI airborne sensor as well as the SPOT-4 multi-spectral satellite. The optical data were used to generate LAI map for the entire study site. Backscatter coefficients, ratios of backscatter intensity, three polarimeric variables and three Cloude-pottier Decomposition parameters were extracted from the polarimetric data set. Temporal variations of the backscatter coefficient were analyzed. The results show an increase in backscatter with corn and soybean growth. The statistical analysis quantified the relationship between the radar parameters and LAI revealing a strong sensitivity for some radar configurations. For both corn and soybean, RADARSAT-2 cross-polarization (HV) backscatter at either shallow or steep incidence angles was well correlated with LAI. To avoid sensitivity to sensor calibration and changing target moisture conditions, ratios of backscatter intensity, polarimetric variables and Cloude-pottier Decomposition parameters were investigated. For corn, the ratio of HV/HH and HV/VV as well as pedestal height, total power, correlation coefficient, Entropy and alpha angle were highly correlation with LAI at steeper incidence angle. For soybean, the higher correlations were found with the ratio of HV/HH as well as pedestal height, total power, Entropy and alpha angle at shallow incidence angle. In general, the best results were observed for corn using the FQ6 acquisition. For soybean, the FQ20 data provided the most promising results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".