A groundwater level-based filtering to improve the accuracy of locating agricultural tile drain and ditch networks
Bibliographic record
Abstract
Remote sensing (RS) using satellites circling around the Earth has great potential for monitoring surface processes with reduced cost and greater access. This study uses three approaches to identify possible drainage unit locations: existing benchmark techniques and a novel complementary approach based on groundwater table depth. The study area comprises a site in Ontario, Canada, and the Kleine Nete catchment, Belgium. First, a change detection method based on the interpretation of RS imagery is used to retrieve soil moisture differences. Based on the retrieved soil moisture differences, it is possible to distinguish between drained and undrained fields. Secondly, the decision tree classification (DTC) method based on filtering pixels corresponding to agricultural fields with a slowly draining soil class along with a gentle slope was applied to identify drainage units. Finally, a novel filtering technique based on groundwater table depth is applied as a complementary identification tool to the former approach. The remote sensing method resulted in 87.8% accuracy in the first study area, while the decision tree classification achieved 96.7% accuracy. Although the RS approach was not successful in following the ditch network, the DTC was able to indicate ditch networks with up to 58% accuracy. However, the additional filtering using groundwater level measurements increased the drainage unit identification accuracy in the first study area (corresponds to finding an additional 19.4 km2 area of drains). A final quantitative assessment for the second study area revealed a close follow-up of the ditch network to the shallow groundwater table maps. In general, it can be concluded that both the remote sensing and the DTC method have tremendous potential to identify drainage units, although with limitations in particular cases, such as low accuracy. Moreover, it can be advised that a local visit to the study area is required to investigate what type of drainage system is used. Next, the novel use of groundwater level-based filtering further improves the drainage identification procedure. Finally, combining several data and techniques allows for accurately identifying drainage units, which is ultimately useful for the sustainable management of drained water from agricultural fields.
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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 | 0.001 | 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".