The Response of Spectral Vegetation Indices and Solar‐Induced Fluorescence to Changes in Illumination Intensity and Geometry in the Days Surrounding the 2017 North American Solar Eclipse
Bibliographic record
Abstract
Abstract Remote sensing is a key method for advancing our understanding of global photosynthesis and is thus critical to understanding terrestrial carbon uptake and climate change. Increasingly sophisticated spectral indices including solar‐induced florescence (SIF) and the photochemical reflectance index (PRI) are considered good proxies of canopy structure, biochemistry, and physiology. However, the relative influences of illumination intensity and angle on these measures are difficult to unravel, particularly at the scale of whole forest canopies. We exploit the solar dimming during the 2017 North American solar eclipse as well as a clear day before and cloudy day after the day of the eclipse. This novel approach allows us to assess changes in spectral vegetation indices due to illumination intensity independent of changes in illumination angle. Physiologically relevant spectral indices were most affected by dimming, with illumination level explaining 97% of variability in SIF and 99% of variability in PRI during the eclipse. The spectral change in reflectance through the eclipse period revealed changes in PRI were driven by reflectance differences at the 570 nm reference band rather than at the 531 nm signal band associated with xanthophyll pigment interconversions. This study refines our interpretation of vegetation properties from space with implications for our interpretation of signals related to terrestrial photosynthesis derived from sensors spanning a range of illumination conditions and angles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".