Land surface phenology from optical satellite measurement and CO<sub>2</sub> eddy covariance technique
Bibliographic record
Abstract
Land surface phenology (LSP) is an integrative indicator of vegetation dynamics under a changing environment. Increasing amounts of remote sensing measurements and CO 2 flux observations offer unprecedented opportunities to quantify LSP phases at landscape scale. LSP start of season (SOS) and end of season (EOS) estimates are often based on the use of a single‐purpose vegetation index derived from optical satellite data, characterized by poor performances in decoupling soil and snow cover dynamics from LSP cycles, as well as contrasting responses of the needleleaf and broadleaf forests in boreal ecosystems. We propose a new remote‐sensing‐based phenology index (PI) which combines the merits of normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) by taking the difference of squared greenness and wetness to remove the soil and snow cover dynamics from key vegetation LSP cycles. We have cross‐validated the remote‐sensing‐based LSP dates with those of CO 2 flux observations from 11 selected tower sites across Canada and the United States consisting of needleleaf forests, broadleaf forests, and croplands. The results indicate that PI estimates the SOS and EOS dates better than NDVI when compared to the LSP dates from CO 2 flux measurements (reduced RMSE, bias and dispersions, and higher correlation). PI‐based SOS and EOS estimates are in good agreement with those derived from CO 2 flux measurements with mean bias comparable to the temporal resolution of the high‐quality, 8‐day composite satellite measurements. Finally, PI also shows a smoother time series compared to NDVI and NDII.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".