Impact of sensor placement in soil water estimation*
Bibliographic record
Abstract
Soil moisture estimation is an essential element in the implementation of a closed-loop irrigation system. The determination of the best locations to install the sensors such that good state estimation can be obtained is an important problem. In our previous work, this issue has been addressed by employing the modal degree of observability based on extensive simulations. It was found that optimally placed sensors can lead to much-improved soil moisture estimation performance. However, it is unclear whether the significantly improved estimation performance can still be observed in actual applications. In this work, we consider an actual agricultural field in Lethbridge, Alberta, Canada, and study the impact of sensor placement in soil water estimation. Soil moisture measurements from 42 soil moisture sensors installed at different depths were collected for one growing season. First, a three-dimensional agro-hydrological model with heterogeneous soils is developed. Then, a state estimator designed based on the extended Kalman filter (EKF) is adopted to estimate the soil water content. Subsequently, we apply the modal degree of observability to the three-dimensional system and determine where the best sensor locations are. Different scenarios are considered to estimate the soil water content and the estimation results are analyzed for all the scenarios.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".