Exploring Linkages Between Remotely Sensed Canopy Nitrogen and Albedo in U.S. and Canadian Forests
Bibliographic record
Abstract
Terrestrial ecosystems influence the Earth's climate through a variety of processes involving exchanges of matter and energy with the atmosphere. Using data from remote sensing, field measurements and eddy flux towers, we recently demonstrated that two important mechanisms of climate regulation, uptake of CO2 and total shortwave surface albedo, are strongly correlated and co-vary with the nitrogen status of plant canopies (%N). Specifically, we found that (1) much of the variability in canopy nitrogen (%N) is related to simple but previously unrecognized reflectance properties in the near infrared region; (2) mean canopy %N for the footprint areas around each flux tower is positively and significantly correlated with canopy-level photosynthetic capacity; and (3) canopy %N is significantly and positively correlated with total shortwave albedo. Although these findings have important implications for ecosystem-climate interactions, the specific mechanisms driving the observed linkages remain unclear. For example, although canopy %N and albedo are significantly correlated, other canopy traits such as LAI and canopy structure could underlie the observed trends. Here, we explore the basis for these relationships by incorporating additional field and remote sensing measurements and by expanding the dataset to include sites from the Canadian Carbon Program. The combined data set represents a wide range of forest types, stand ages, climate conditions and disturbance regimes. Results are discussed with respect to local variation at individual sites as a means of understanding mechanisms responsible for the observed carbon-nitrogen-albedo trends.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".