Cross-Evaluation of Soil Moisture Based on the Triple Collocation Method and a Preliminary Application of Quality Control for Station Observations in China
Bibliographic record
Abstract
Soil moisture (SM) measurements from ground stations are often after quality control (QC) in the operational system, but the QC flags may not be reliable in some cases when precipitation events or manual watering happen. This study applies the triple collocation (TC) method to conduct a cross-evaluation of SM data from ERA5 reanalysis estimates, ESA-CCI estimates, and ~2000 ground stations across the China domain. The results show that all datasets can capture the spatial pattern of SM in China. TC-based correlation coefficient (CC) and root mean square error (RMSE) show that the station data have worse performance in western and central China. For most stations, TC-based CC is between 0.6~0.9, and TC-based RMSE is between 0.01~0.06 m3/m3. In addition, TC-based metrics show good agreement with the CC between precipitation and SM, indicating that these metrics can reflect the quality of station data. We further selected typical stations (e.g., CC ≤ 0.2, RMSE ≥ 0.06 m3/m3) to check the quality of the QC procedure. The comparison shows that TC-based metrics can better represent the actual quality for these stations compared to raw QC flags. This study indicates that TC has the potential to detect problematic stations and could be a supplement to traditional QC of station observations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 teacher head, 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".