Testing of Automated Photochemical Reflectance Index Sensors as Proxy Measurements of Light Use Efficiency in an Aspen Forest
Bibliographic record
Abstract
Commercially available autonomous photochemical reflectance index (PRI) sensors are a new development in the remote sensing field that offer novel opportunities for a deeper exploration of vegetation physiology dynamics. In this study, we evaluated the reliability of autonomous PRI sensors (SRS-PRI) developed by METER Group Inc. as proxies of light use efficiency (LUE) in an aspen (Populus tremuloides) forest stand. Before comparisons between PRI and LUE measurements were made, the optical SRS-PRI sensor pairs required calibrations to resolve diurnal and seasonal patterns properly. An offline diurnal calibration procedure was shown to account for variable sky conditions and diurnal illumination changes affecting sensor response. Eddy covariance measurements provided seasonal gross primary productivity (GPP) measures as well as apparent canopy quantum yield dynamics (α). LUE was derived from the ratio of GPP to absorbed photosynthetically active radiation (APAR). Corrected PRI values were derived after diurnal and midday cross-calibration of the sensor’s 532 nm and 570 nm fore-optics, and closely related to both LUE (R2 = 0.62, p < 0.05) and α (R2 = 0.72, p < 0.05). A LUE model derived from corrected PRI values showed good correlation to measured GPP (R2 = 0.77, p < 0.05), with an accuracy comparable to results obtained from an α driven LUE model (R2 = 0.79, p < 0.05). The automated PRI sensors proved to be suitable proxies of light use efficiency. The onset of continuous PRI sensors signifies new opportunities for explicitly examining the cause of changing PRI, LUE, and productivity over time and space. As such, this technology represents great value for the flux, remote sensing and modeling community.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".