Assessment of tree species specific phenology metrics from Sentinel-2 data to complement in situ monitoring
Bibliographic record
Abstract
Monitoring tree phenology is key to understanding forest dynamics under climate change. Events like leaf unfolding and senescence affect ecosystem productivity, tree mortality, and species interactions. While in situ phenology observations provide valuable ground information, they are typically restricted in spatial and temporal coverage and may be influenced by observer-related inconsistencies. Here, we derived species-specific phenology metrics from Sentinel-2 satellite data for Switzerland’s two dominant tree species: beech ( Fagus sylvatica ) and spruce ( Picea abies ). We extracted start (SOS), peak (POS), and end (EOS) of season metrics and compared them to in situ observations to study interannual, regional, and topographic variation. Sentinel-2-derived metrics differed significantly from the in situ observations for the SOS and EOS of Fagus sylvatica and the SOS of Picea abies . Sentinel-2 metrics indicated a shorter growing season – later SOS (5 days for Fagus sylvatica ; 3 days for Picea abies ) and earlier EOS (13 days for Fagus sylvatica ). Despite these offsets, satellite data captured similar annual and regional trends. POS closely tracked SOS trends, but offered more reliable sampling opportunities due to more stable vegetation conditions and typically lower cloud cover during summer. Satellite-derived EOS may reflect stress responses missed by ground observations. Elevation trends also differed, with in situ data showing steeper slopes of the SOS-elevation relationships. Limitations of satellite data remained in mountainous regions due to topography and cloud cover, limiting sampling sizes. Overall, satellite remote sensing can complement in situ observations by facilitating observations across large geographic and temporal domains. In contrast, in situ observations provide long-term historical data unaffected by atmospheric conditions or possible technical issues of satellites. • Phenology metrics for beech and spruce derived from satellite remote sensing. • Comprehensive comparison of in situ and Sentinel-2 species phenology metrics. • Analyzing interannual and regional patterns of phenology between data sources. • Assessing the influence of elevation and topographic wetness on phenology metrics. • Discussing how satellite remote sensing can support conventional phenology monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".