Multiple-scattering scheme useful for geometric optical modeling
Bibliographic record
Abstract
Geometrical optical (GO) models have been widely used in remote sensing applications because of their simplicity and ability to simulate angular variation of remote sensing signals from the Earth's surface. GO models are generally accurate in the visible part of the solar spectrum, but less accurate in near-infrared (NIR) part in which multiple scattering in plant canopies is the strongest. Although turbid-media radiative transfer (RT) methods have been introduced to GO models to cope with the second-order and higher order scattering, the problem of canopy geometrical effects on multiple scattering still remains and becomes the main obstacle in GO model applications. In this paper, we propose and test a multiple scattering scheme to simulate angular variation in multiply scattered radiation in plant canopies. This scheme is based on various view factors between sunlit and shaded components (both foliage and background) in the canopy and allows the geometrical effects to propagate to the second-order and higher order scattering simulations. As the view factors depend on the canopy geometry, the scheme is particularly useful in GO models. This new scheme is implemented in the 4-Scale Model, which previously used band-specific multiple scattering factors. After the use of the scheme, these factors are removed and the multiple scattering at a given wavelength and angle of observation can be automatically computed. Improvements made with this scheme are shown in comparison with the top-of-canopy (i.e., PARABOLA) and airborne (i.e., POLDER) measurements with modeled results with and without the scheme. Examples of canopy-level hyperspectral signatures simulated using the scheme are also shown.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".