Statistical Analysis of CyGNSS Speckle and Its Applications to Surface Water Mapping
Bibliographic record
Abstract
The Global Navigation Satellite System reflectometry (GNSS-R) technique has demonstrated its potential for terrestrial applications. Over inland water bodies, the dominance of coherent components in GNSS-R has been widely recognized. Nevertheless, little attention is given to GNSS-R speckle, which is inherent to coherent imaging systems. In this study, taking the multiplicative speckle into account, we regard GNSS-R coherent scattering as a statistical distribution. First, the expression of the statistical distribution is identified and parameterized using observations from the Cyclone Global Navigation Satellite System (CyGNSS). The results suggest that the power tends to obey a three-degrees-of-freedom distribution model. Second, the multilook statistics of CyGNSS, such as the mean value and the coefficient of variation (CV), are analyzed on different spatial–temporal scales. Finally, we realize surface water mapping using multilook statistics. Comparison with the state-of-the-art algorithms shows that the proposed method can effectively improve the goodness of water mapping, with higher overall accuracies (~0.97) and F1 scores (~0.60). This study provides new insights into future GNSS-R land observations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".