Evaluating the influence of surface soil moisture and soil surface roughness on optical directional reflectance factors
Bibliographic record
Abstract
Summary Fine‐scale information on soil surface roughness ( SSR ) is needed for calculating heat budgets, monitoring soil degradation and parameterizing surface runoff and sediment transfer models. Previous work has demonstrated the potential of using hyperspectral, hemispherical conical reflectance factors ( HCRF s) to retrieve the SSR of different soil crusting states. However, this was achieved by using dry soil surfaces, generated in controlled laboratory conditions. The primary aim of this study was therefore to test the impact that in situ variations in surface soil moisture ( SSM ) content had on the ability of directional reflectance factors to characterize SSR conditions. Five soil plots (20 cm × 20 cm in area) representing different agricultural conditions were subjected to different durations of natural rainfall to produce a range of different levels of SSR . The values of SSM varied from 8.7 to 20.1% across all soil plots. Point laser data (4‐mm sample spacing) were geostatistically analysed to give a spatially‐distributed measure of SSR , giving sill variance values from 3.2 to 23.0. The HCRF s from each soil state were measured using a ground‐based hyperspectral spectroradiometer for a range of viewing zenith angles from extreme forward‐scatter ( θ r = −60°) to extreme back‐scatter ( θ r = +60°) at a 10° sampling resolution in the solar principal plane. The results showed that despite a large range of SSM values, forward‐scattered reflectance factors exhibited a very strong relationship with SSR ( R 2 = 0.84 at θ r = −60°). Our findings demonstrate the operational potential of HCRF s for providing spatially‐distributed SSR measurements, across spatial extents containing spatio‐temporal variations in SSM content.
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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 | 0.000 | 0.000 |
| 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".