Estimation of Gross Primary Productivity of a raised bog ecosystem using satellite models and eddy covariance techniques under exceptional climatic conditions  
Bibliographic record
Abstract
Peatlands are vital to the global carbon (C) cycle as they act as a significant C store and these systems in Ireland store between 1064 –1503 Gt C on ~20% of the land area. However, around 90% of this area has been drained and degraded by various anthropogenic activities and the emissions from these activities are approximately 3 million t C per year. A better understanding of the land-atmosphere C and greenhouse gas (GHG) dynamics is vital to halt these emissions and enhance the C sink strength of these ecosystems. Gross Primary Productivity (GPP) is a major part of the peatland carbon cycle and detailed knowledge of the spatial and temporal extent of GPP is imperative for improving our predictions of peatland ecology, biogeochemistry and carbon balance in response to global change. Eddy covariance (EC) techniques are widely used to measure carbon fluxes but can only account for fluxes within the flux footprint of the tower, and it is challenging to scale up data from EC towers to regional and global scales due to the limited number of towers and their geographic locations. This research assesses the relationship between remote sensing and ground-based measurements for a near-natural raised bog in Ireland using EC techniques and high-resolution Sentinel 2A satellite imagery. Vegetation indices (VIs) are one of the key input parameters for satellite-based GPP and most of the existing VIs have been developed for grassland, agriculture, and forest ecosystems. This study developed a hybrid index for raised bogs using multiple linear regression and six widely practiced conventional vegetation indices. Two approaches have been used in this study for estimating GPP using the LUE model. Initially, all the individual indices have been used to model the GPP, which was subsequently compared with the EC GPP to determine the performance of each index against the EC data. The model was run with meteorological data and satellite-derived vegetation indices. During the 2018 study period, the weather was exceptionally dry which made it challenging and rewarding at the same time as the hybrid index was developed for an exceptional year. It was crucial to test the performance of the hybrid index under more normal weather conditions with limited clear sky satellite imagery. Therefore, the hybrid index was validated for the year 2019 which had normal weather conditions. The hybrid index based modelled GPP showed a significant correlation with the EC GPP for both the years with an R2 > 0.95. Overall, this research has demonstrated the potential of combining EC techniques and the hybrid index along with satellite-derived models to better understand and monitor key drivers and patterns of GPP of raised bog ecosystems under different climate scenarios.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".