Impact of soil surface characteristics on soil water content variability in agricultural fields
Bibliographic record
Abstract
Abstract The accuracy of soil water content (WC) interpretation from satellites and its application in hydrological modelling is dependent on our understanding of the effects of field‐scale surface soil properties on soil WC variability. Soil texture, surface roughness and surface residue were evaluated for their influence on soil WC variability with data obtained from satellite ground verification sampling near Carman, Manitoba, within a 28 km2 area. Over the course of five selected dates from 23 April to 18 May 2008, soil WC and other physical variables were obtained in 38 agricultural fields covering three distinct soil textural classifications. Within each field, at each of 16 sampling locations, four individual soil WC measurements were taken, including sampling points with and without the influence of crop residue left on the soil surface. A principal component analysis and multiple linear regression both identified soil texture as the primary physical process controlling variability in soil WC and coefficient of variation (CV) among fields during the campaign. Residue cover was also a significant factor representing a second principal component that explained 37% of variability in average soil WC and contributed to 31% of variability in CV. Whereas soil texture predominated in soil WC and CV on most sampling dates, residue cover was of equal significance as texture in dry soils. This study identified specific effects of tillage and residue management contributing to field‐scale soil WC variability that could contribute significant improvements to interpretation of remotely sensed soil WC and subsequent scaling efforts over agricultural regions. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.000 | 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".