SCS+C: a modified Sun-canopy-sensor topographic correction in forested terrain
Bibliographic record
Abstract
Topographic correction based on sun-canopy-sensor (SCS) geometry is more appropriate than terrain-based corrections in forested areas since SCS preserves the geotropic nature of trees (vertical growth) regardless of terrain, view, and illumination angles. However, in some terrain orientations, SCS experiences an overcorrection problem similar to other simple photometric functions. To address this problem, we propose a new SCS+C correction that accounts for diffuse atmospheric irradiance based on the C-correction. A rigorous, comprehensive, and flexible method for independent validation based on canopy geometric optical reflectance models is also introduced as an improvement over previous validation approaches, and forms a secondary contribution of this paper. Results for a full range of slopes, aspects, and crown closures showed SCS+C provided improved corrections compared to the SCS and four other photometric approaches (cosine, C, Minnaert, statistical-empirical) for a Rocky Mountain forest setting in western Canada. It was concluded that SCS+C should be considered for topographic correction of remote sensing imagery in forested terrain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".