Empirical modelling to estimate surface soil moisture at field scale in Sardinia, Italy: Comparison between optical and SAR data
Bibliographic record
Abstract
Surface soil moisture is an important variable in hydrological modeling and is critical in agricultural and other applications. There is thus a strong interest in assessing the potential of remote sensing for providing regular spatio-temporal soil moisture observation. This study analyzes the correlation between 18 ENVISAT ASAR images (C-band, HH and VV polarized, ascendant or descendant mode, incidence angle from 15.0° to 31.4°) and surface soil moisture measurements over six small (2of 85.03% and a variance of 0.001113. When applying a cross validation technique to estimate soil moisture on the same six fields (only those with more than 5% relative humidy) we obtain an R2of 75,87% (measured versus retrieved soil moisture) for ENVISAT imagery and an R2of 51.71% for RADARSAT-2 imagery.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 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".