Temporal water table dynamics derived from optical satellite data
Bibliographic record
Abstract
Water table constitutes a master control of the general biogeochemistry in northern peatlands. The performance of peatland simulations in global ecosystem models is strongly hampered by the accuracy of the water table predictions. We examined the applicability of the Optical TRApezoid Model (OPTRAM) to monitor the temporal fluctuations in water table over 53 intact, restored, and drained northern peatlands in Finland, Estonia, Sweden, Canada, and the USA from 2018 through 2021. Various OPTRAM were computed based on Sentinel-2 data with the Google Earth Engine cloud platform. We found that (i) the choice of vegetation index utilised in OPTRAM does not significantly affect OPTRAM performance; (ii) the tree cover density is a significant factor controlling the sensitivity of OPTRAM to water table dynamics; (iii) the relationship between water table and OPTRAM often disappears for deep water tables. Based on an anomaly analysis, we further found that OPTRAM seems to be in particular suitable to monitor long-term (i.e., interannual) water table variability while the performance for short-term changes (e.g., response to individual rain events) was lower. Overall, our results support the application of OPTRAM to monitor water table dynamics in intact and restored northern peatlands with low tree cover density when the water table is shallow to moderately deep.
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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 | 0.001 |
| 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".