Potential of mapping soil moisture by combining radar backscatter modeling and PolSAR decomposition
Bibliographic record
Abstract
The purpose of this study is to evaluate the capability of the Oh backscattering model in combination with the Freeman Durden decomposition to estimate soil moisture over agricultural fields from fully polarimetric RADARSAT-2 C-band SAR responses. Initially, soil moisture multi-polarization retrieval was accomplished by using a look-up table (LUT) approach applied to the Oh model. Two methods were considered: the multi-polarization method and the one-unknown configuration. Of the two methods, results showed that the HH-HV inversion provided the best estimates. In the second phase, the Freeman Durden decomposition was applied to the polarimetric data. The conceptual approach for retrieving soil moisture using the surface scattering component of the total power was implemented in a LUT inversion. The algorithm attempts to minimize the difference between measured single scattering power obtained by applying the Freeman Durden decomposition and simulated total power using Oh model. When compared with the multi-polarization approach, this polarimetry-based method improves the accuracy of soil moisture estimates.
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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".