Characterizing soil surface roughness using a combined structural and spectral approach
Bibliographic record
Abstract
Summary The ability to quantitatively and spatially assess soil surface roughness is important in geomorphology and land degradation studies. This paper describes the results of an experiment designed to investigate whether hyperspectral directional reflectance factors can describe fine‐scale variations in soil surface roughness. A Canadian silt loam soil was sieved to an aggregate size range of 1–4.75 mm and exposed to five different artificial rainfall durations to produce soils displaying progressively decreasing levels of surface roughness. Each soil state was measured using a point laser profiling instrument at 2 mm spatial resolution, in order to provide information on the structure and spatial arrangement of soil particles. Hyperspectral directional reflectance factors were measured using an Analytical Spectral Devices FieldSpec Pro Spectroradiometer (range 350–2500 nm), at a range of measurement angles (θ r =−60° to +60°) and illumination angle conditions (θ i = 28°–74°). Directional reflectance factors varied with illumination and view angles, and with soil structure. Geostatistically‐derived indicators of soil surface roughness (sill variance) were regressed with directional reflectance factors. The results showed a strong relationship between directional reflectance and surface roughness ( R 2 = 0.94 where θ r =−60°, θ i = 67°–74°). This fine‐scale quasi‐natural experiment allowed the control of slope, initial aggregate size and rainfall exposure, permitting an investigation into factors affecting a soil’s bidirectional reflectance response. This has highlighted the relationship between fine‐scale variations in surface roughness, illumination angle and reflectance response. The results show how the technique could provide a quantitative measure of surface roughness at fine spatial scales.
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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) | 0.001 | 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".