Monitoring seasonal and diurnal changes in photosynthetic pigments with automated PRI and NDVI sensors
Bibliographic record
Abstract
Abstract. The vegetation indices normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI) provide indicators of pigmentation and photosynthetic activity that can be used to model photosynthesis from remote sensing with the light-use-efficiency model. To help develop and validate this approach, reliable proximal NDVI and PRI sensors have been needed. We tested new NDVI and PRI sensors, "spectral reflectance sensors" (SRS sensors; recently developed by Decagon Devices, during spring activation of photosynthetic activity in evergreen and deciduous stands. We also evaluated two methods of sensor cross-calibration – one that considered sky conditions (cloud cover) at midday only, and another that also considered diurnal sun angle effects. Cross-calibration clearly affected sensor agreement with independent measurements, with the best method dependent upon the study aim and time frame (seasonal vs. diurnal). The seasonal patterns of NDVI and PRI differed for evergreen and deciduous species, demonstrating the complementary nature of these two indices. Over the spring season, PRI was most strongly influenced by changing chlorophyll : carotenoid pool sizes, while over the diurnal timescale, PRI was most affected by the xanthophyll cycle epoxidation state. This finding demonstrates that the SRS PRI sensors can resolve different processes affecting PRI over different timescales. The advent of small, inexpensive, automated PRI and NDVI sensors offers new ways to explore environmental and physiological constraints on photosynthesis, and may be particularly well suited for use at flux tower sites. Wider application of automated sensors could lead to improved integration of flux and remote sensing approaches for studying photosynthetic carbon uptake, and could help define the concept of contrasting vegetation optical types.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".