Snow persistence lowers and delays peak NDVI, the vegetation index that underpins Arctic greening analyses
Bibliographic record
Abstract
Abstract Satellite imagery is critical for understanding land-surface change in the rapidly warming Arctic. Since the 1980s, studies have found positive trends in the normalised difference vegetation index (NDVI) derived from satellite imagery over the Arctic—commonly referred to as ‘Arctic greening’ and assumed to represent increased vegetation productivity. However, greening analyses use satellite imagery with pixel sizes ranging from tens to hundreds of metres and do not account for the integration of abiotic phenomena such as snow within vegetation indices. Here, we use high-resolution drone data from one Arctic and one sub-Arctic site to show that fine-scale snow persistence within satellite pixels is associated with both reduced magnitude and delayed timing of annual peak NDVI, the base metric of Arctic greening analyses. We found higher snow persistence within Sentinel-2 pixels is associated with a lower magnitude and later peak NDVI, with a mean difference in NDVI of 0.1 and seven days between high and low snow persistence pixels. These effects were stronger in NASA HLSS30 data, representative of Landsat data commonly used in greening analyses. Our findings indicate that unaccounted changes in fine-scale snow persistence may contribute to Arctic spectral greening and browning trends through either biotic responses of vegetation to snow cover or abiotic integration of snow within the estimated peak NDVI. In order to improve our understanding of Arctic land-surface change, studies should integrate very-high-resolution data to estimate the dynamics of late-season snow within coarser satellite pixels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 teacher head, 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".