Gap Filling for ISMN Time Series Using CYGNSS Data
Bibliographic record
Abstract
This study introduces a method for filling the data gaps in the International Soil Moisture Network (ISMN) by soil moisture (SM) estimated using data from the Cyclone Global Navigation Satellite System (CYGNSS). The estimation process leverages the random forest (RF) algorithm, incorporating CYGNSS-derived products along with soil and surface parameters as input features. This research was conducted based on the daily SM data from the ISMN for the entire years of 2019 and 2020, which served as training and test datasets. Comparison experiments were performed to highlight the limitations of existing methods and SM products for gap filling in ISMN SM data. Subsequently, the optimal retrieval model was deployed to estimate SM for the duration of the study, thereby filling the gaps within the ISMN dataset. The SM results after gap filling showed strong consistency with measured SM, achieving an R-squared ($R^{2}$) of 0.7930 and a root-mean-square error (RMSE) of 0.0492 cm3/cm3. These results indicate that CYGNSS-based SM inversion is a promising approach to enhance the completeness of the ISMN dataset.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".