Caractérisation géostatistique de la variabilité spatiale de l’humidité du sol à l’aide des cartes dérivées des données radar à synthèse d’ouverture de RADARSAT-1
Bibliographic record
Abstract
This paper examines the potential of RADARSAT-1 C-band synthetic aperture radar (SAR) data for quantifying the spatial variability of soil moisture in the Roseau River basin, in Manitoba, Canada. To compensate for the lack of accuracy in the measurement of roughness, we applied a semi-empirical calibration technique. The procedure improved the performance of the integral equation model (IEM). An inversion of the calibrated version of IEM was implemented to extract soil moisture maps. Adjustments to the semivariograms using an exponential model showed the existence of an effect of spatial organization. The correlation lengths obtained are in good agreement with those reported in the literature using in situ measurements (~100 m). However, we highlighted the influence of semivariogram spatial extents on such estimations. Yet, it has been shown that this effect can be modeled using exponential type functions (R2 > 0.90). During the analysis of the apparent variances, we confirmed the predominance of microstructure errors over the effect of radar speckle. We also outlined an increasing pattern in the apparent variances in relation to the scale of the geostatistical analyses. This pattern was less pronounced during relatively dry periods. This derives from the predominance of evaporation mechanisms, which create a uniform spatial distribution of moisture in the soil superficial layers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".