Assessing the variability of soil water content with ground-penetrating radar and electromagnetic induction
Bibliographic record
Abstract
Determining the spatiotemporal variability of soil water content (SWC) in agricultural fields is crucial to ensure efficient water management practices to support precision agriculture. Standard methods are tedious, destructive, and mainly provide point-scale measurements only. Hydrogeophysics uses near-surface geophysical methods to determine the spatiotemporal variability of hydrological and physical soil properties. Integrating near-surface geophysical methods such as Electromagnetic Induction (EMI) and Ground Penetrating Radar (GPR) to support PA is a novel approach. SWC variations derived from soil proxies like apparent electrical conductivity (ECa) from EMI and ground wave velocity (GWV) from GPR can be validated with Time domain reflectometry (TDR) measurements. The objectives of this study were to assess the responses of ECa and GWv to SWC variability under different compactions in a podzolic soil and validate these proxies using TDR measurements. Field data were collected during irrigation and drainage under different compactions at the Pynn's Brook Research Station, Western Newfoundland, Canada. Irrigation was applied at a rate of 0.13 cm/min for 15 min by considering a root depth of 20 cm. ECa and GWV data were collected using an EMI sensor and a 500 MHz frequency GPR system, respectively. The site-specific calibration between TDR and the gravimetric method for loamy sand soil (0-12 cm depth) gave a strong positive correlation (R2 = 0.969, p=0.0000). According to preliminary analyses, the use of ECa and GWV as SWC proxies during irrigation and drainage was successfully validated by TDR. Data analyses are still ongoing, it is expected that this research will improve our understanding on evaluating these proxies for mapping SWC variability. The knowledge gained during the method development and implementation of EMI and GPR for in situ SWC measurements will not only improve the ability to apply in agriculture but may also be extended to other environments such as contaminated sites or forests.
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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 | 0.000 |
| 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".