Soil Moisture Retrieval over Agricultural Fields Using Dual-Polarimetric SAR Data
Bibliographic record
Abstract
The model-based polarimetric decomposition technique can utilize information of different channels to describe the complex interaction processes between soil and canopy, elegantly achieving the decoupling of scattering signals from the surface and vegetation. However, the dependence on fully polarimetric SAR data limits the application of this method. In this study, an advanced soil moisture retrieval method coupling model-based decomposition and surface scattering model is proposed for dual-pol SAR data. The generalized volume scattering model based on fully polarimetric decomposition theory is reconstructed as a projection on Stokes vector to facilitate the simple removal of volume scattering contribution. Soil moisture is subsequently estimated iteratively based on a cost function using an Oh semi-empirical model considering surface roughness. The measurements obtained from the ground campaign in Manitoba, Canada and L-band UAVSAR images collected during the campaign are used for validation. The proposed method achieves accurate soil moisture estimation based on reasonable separation of surface and vegetation scattering signals. The root mean square error (RMSE) of soil moisture inversion for the VV-VH mode reached 0.052 m3/m3with a correlation coefficient of 0.82, while the RMSE for the HH-HV mode was 0.073 m3/m3with a correlation coefficient of 0.68.
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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 | 0.000 |
| 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".