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 (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) 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 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 | 0.000 | 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".