Sensitivity of SAR Speckle Filtering on the Assessment of Surface Roughness and Soil Moisture Content
Bibliographic record
Abstract
ABSTRACT: The assessment of soil moisture content and surface roughness from remotely sensed data is of primary importance for improving agricultural techniques of conservation farming such as yield forecasts, scheduling irrigations, fertilization. SAR (Synthetic Aperture Radar) remotely sensed data may provide a powerful tool for indirectly retrieving these agricultural surface parameters over large areas with frequent coverage. Nevertheless, one major source of error in the quantitative estimate of such geophysical parameters is the presence of the speckle within the scene. To overcome this difficulty many speckle filtering techniques have been developed for reducing this multiplicative noise. However, up to now, few works analyze the performance of these filters on the retrieval of spatially and physically accurate information useful for the estimates of these soil properties. In this context, this paper outlines the sensitivity of several speckle filtering methods on the assessment of these two agricultural surface parameters. Results stressed that depending on the speckle filtering method used, significant deviations were obtained on the estimates of soil properties.
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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 | 0.003 | 0.008 |
| 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 | 0.001 | 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".