Laboratory and field evaluation of five soil water sensors
Bibliographic record
Abstract
Accurate in situ determination of soil water content is important in many fields of agricultural, environmental, hydrological, and engineering sciences. As numerous soil water content sensors are available on the market today, the knowledge of their performance will aid users in the selection of appropriate sensors. The objectives of this study were to evaluate five soil water sensors in the laboratory and to determine if laboratory calibration is appropriate for the field. In this study, the performances of five sensors, including the Profile Probe™ (PP), ThetaProbe™ , Watermark™, Aqua-Tel™, and Aquaterr™ were compared in the laboratory. The PP and ThetaProbe™ were more accurate than the other soil water sensors, reproducing soil water content using factory recommended parameters. However, when PP was installed on a loamy sand in the field, the same soil that was used for the laboratory evaluation, it overestimated field soil water, especially at depth. Another laboratory experiment showed that soil water content readings from the PP were strongly influenced by soil bulk density. The higher the soil bulk density, the greater was the overestimation of soil water content. Two regression parameters, a 0 and a 1 , which are used to convert the apparent dielectric constant to volumetric water content, were found to increase linearly with the soil bulk density in the range of 1.2 to 1.6 Mg m -3 . Finally, the PP was calibrated in the field and a good calibration function was obtained with an r 2 of 0.87 and RMSE of 2.7%. The values of a 0 and a 1 obtained in the field were different from factory recommended parameters (a 0 = 2.4 versus 1.6 while a 1 = 12.5 versus 8.4) and were independent of soil depth, bulk density, and texture. As such, individual field calibration will be necessary to obtain precise and accurate measurement of soil water content with this instrument. Key words: Soil water content, Profile Probe, calibration, soil water content sensor
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".