Nonlinear Response in a Field Portable Spectroradiometer: Characterization and Effects on Output Reflectance
Bibliographic record
Abstract
We report the characterization and correction of nonlinear responses of a commercial field portable spectroradiometer intended to be used to monitor vegetation physiology. Calibration of photoresponse allowed the successful correction of spectral data and the modeling of biases in reflectance at different levels of the dynamic range. Finally, the impact of nonlinearities on a spectral estimator of photosynthetic status, the photochemical reflectance index (PRI) is discussed. Significance of the biases proved that, although nonlinearity can potentially affect reflectance along most of the dynamic range of the instrument, experimental uncertainties can limit its impact. Nonlinearity biased PRI by affecting the reference band of the index and suggested unreal changes on plant physiology. Results show that nonlinearity could be a significant problem in field spectroscopy, especially in the case of spectroradiometers integrated in unattended systems to monitor vegetation responses to radiation. An automatic adjustment of integration time to reach only a certain level of the dynamic range may reduce nonlinearity effects, though may not always avoid them. We conclude that linearity characterization is necessary to understand impacts and correct potential biases.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".