Effect of microtopography on RADARSAT-1 and PALSAR backscattering from rock alteration products in the Curaçá Valley, Brazil
Bibliographic record
Abstract
This paper addresses the influence of microtopography (root mean square height HRMS and correlation length LC) on RADARSAT-1 and phased array L-band synthetic aperture radar (PALSAR) backscattering coefficient (σ0) values from distinct rock alteration products of the Cu-rich district of Curaçá Valley, northeastern Brazil. The area is characterized by a semiarid environment, flat topography with rock outcrops and residual soils, and low to moderate Caatinga vegetation cover. The lithologies consist of Archean gneisses and granulites interbedded with mafic-ultramafic intrusives and upper Proterozoic marbles, schists, and phyllites. The images were acquired under distinct look azimuth and incidence angles and corresponded to four RADARSAT-1 images (F2, S2, and S7 ascending and S7 descending) and one PALSAR image (fine beam dual (FBD) descending). The research was based on the use of linear regression analyses, which showed a weak to moderate linear correlation between σ0 and HRMS and LC for both SAR data. HRMS was the most important microtopographic parameter influencing σ0, whereas LC played a secondary role. Regarding RADARSAT-1, the highest regression coefficient (R2) values were obtained for shallower incidence angles (S7), and this dependence increased from steeper to shallower incidence, regardless of changes in the look azimuth. For PALSAR, R2 was slightly higher than that for RADARSAT-1 and was related to cross-polarization. The investigation showed that backscattering for synthetic aperture radar (SAR) data from both RADARSAT-1 and PALSAR is not modulated in a predominant manner by the microtopographic variations of the geological surfaces.
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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 | 0.001 |
| 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 | 0.000 | 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".