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 (<;5 hectares) bare fields in Sardinia (Italy) taken over the period from 2005 to 2009. When comparing the backscatter coeffficient of variations and corresponding field data, it was found that images taken with a VV polarisation in a descendant mode were more correlated to soil moisture variations, with an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 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 R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 75,87% (measured versus retrieved soil moisture) for ENVISAT imagery and an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 51.71% for RADARSAT-2 imagery.
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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 | 0.001 |
| 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".