Comparative evaluation of vegetation greenness trends over circumpolar Arctic tundra using multi-sensors satellite datasets
Bibliographic record
Abstract
The circumpolar arctic tundra, located at Earth’s highest latitudes, is extremely sensitive to climate warming. Studies on arctic greening, based on satellite data and field measurements, show discrepancies due to differences in spatial resolution across datasets (e.g., Landsat 30-m, MODIS 250-m, and AVHRR GIMMS 8 km). Research on scale effects has been limited, mostly focusing on small areas rather than the entire 7.11 million km² arctic tundra. Our study addresses this by mapping scale effects across the entire tundra using Normalized Difference Vegetation Index (NDVI) measurements. Findings reveal: (1) Landsat data provides detailed spatial trends, identifying 18.7% of the area as significantly greening, whereas GIMMS data detects more browning due to spectral mixing; (2) GIMMS underestimates the greening to browning ratio at 2.2:1, compared to Landsat and MODIS ratios of 14.1:1 and 15.1:1, respectively; (3) Over 93% agreement exists between Landsat and MODIS or GIMMS trends, with discrepancies in limited areas. This highlights the importance of high-resolution data and field studies for accurately understanding vegetation trends across the arctic tundra.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".