Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts
Bibliographic record
Abstract
Spatiotemporal modeling is an innovative way of predicting soil moisture and has promising applications that support sustainable forest operations. One such application is the prediction of rutting, since rutting can cause severe damage to forest soils and ecological functions. In this work, we used ERA5-Land soil moisture retrievals and several topographic indices to model variations in the in situ soil water content by means of a random forest model. We then correlated the predicted soil moisture with rut depth from different trials. Our spatiotemporal modeling approach successfully predicted soil moisture with Kendall's rank correlation coefficient of 0.62 ( R 2 of 64 %). The final model included the spatial depth-to-water index, topographic wetness index, stream power index, as well as temporal components such as month and season, and ERA5-Land soil moisture retrievals. These retrievals were shown to be the most important predictor in the model, indicating a large temporal variation. The prediction of rut depth was also successful, resulting in Kendall's correlation coefficient of 0.61. Our results demonstrate that by using data from several sources, we can accurately predict soil moisture and use this information to predict rut depth. This has practical applications in reducing the impact of heavy machinery on forest soils and avoiding wet areas during forest operations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".