Water table depth dynamics derived from optical remote sensing data in northern peatlands
Bibliographic record
Abstract
Water table depth (WTD) is one of the key factors that affect the carbon balance in peatlands. Optical remote sensing can detect WTD indirectly through the estimation of surface moisture. In peatlands, WTD and surface moisture conditions are closely related through the strong capillary connection in the topmost peat layer. We took advantage of this strong connection and calculated the OPtical TRApezoid Model (OPTRAM) that relies on the assumption that short-wave infrared reflectance represents the surface moisture conditions. OPTRAM was calculated based on Sentinel-2 MSI and Landsat 8 OLI over selected northern peatlands in Finland, Sweden, Canada, the USA, and Estonia. This is the first study in which the advantages and shortcomings of OPTRAM estimation from Sentinel-2 MSI and Landsat 8 OLI data were discussed. We calculated OPTRAM in two ways: (i) using a manual parametrisation and (ii) utilising a recently developed automatic parameterisation in Google Earth Engine. Further, we analysed the impact of these two parameterisations on OPTRAM performance in various peatlands. Our findings provide an important insight into the global applicability of OPTRAM for monitoring moisture conditions in northern peatlands.
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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) | 0.000 | 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".