Assessing the Performance of Thermal Inertia and Hydrus Models to Estimate Surface Soil Water Content
Bibliographic record
Abstract
The knowledge of soil water content (SWC) dynamics in the upper soil layer is important for several hydrological processes. Due to the difficulty of assessing the spatial and temporal SWC dynamics in the field, some model-based approaches have been proposed during the last decade. The main objective of this work was to assess the performance of two approaches to estimate SWC in the upper soil layer under field conditions: the physically-based thermal inertia and the Hydrus model. Their validity was firstly assessed under controlled laboratory conditions. Thermal inertia was firstly validated in laboratory conditions using the transient line heat source (TLHS) method. Then, it was applied in situ to analyze the dynamics of soil thermal properties under two extreme conditions of soil-water status (well-watered and air-dry), using proximity remote-sensed data. The model performance was assessed using sensor-based measurements of soil water content acquired through frequency (FDR) and time domain reflectometry (TDR). During the laboratory experiment, the Root Mean Square Error (RMSE) was 0.02 m3 m−3 for the Hydrus model and 0.05 m3 m−3 for the TLHS model approach. On the other hand, during the in situ experiment, the temporal variability of SWCs simulated by the Hydrus model and the corresponding values measured by the TDR method evidenced good agreement (RMSE ranging between 0.01 and 0.005 m3 m−3). Similarly, the average of the SWCs derived from the thermal diffusion model was fairly close to those estimated by Hydrus (spatially averaged RMSE ranging between 0.03 and 0.02 m3 m−3).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".