DiffWater: A Conditional Diffusion Model for Estimating Surface Water Fraction Using CyGNSS Data
Bibliographic record
Abstract
Recent advances in Cyclone Global Navigation Satellite System (CYGNSS) data have significantly improved the extraction of monthly surface water fraction (SWF), with neural networks being widely used for large-scale water body mapping based on GNSS-R signals. However, inherent noise in CYGNSS signals, such as multipath effects and interference, presents substantial challenges to the accuracy of SWF estimation. Diffusion models, an emerging class of generative deep learning techniques, have shown remarkable capabilities in capturing complex data distributions. By leveraging an iterative process of noise addition and removal, these models demonstrate significant advantages in processing low signal-to-noise ratio data, offering a novel methodology for precise SWF estimation from CYGNSS data. This study introduces DiffWater, a framework designed to address the unique characteristics of CYGNSS data and systematically explore the applicability of conditional diffusion models for remote sensing tasks. Utilizing a composite reference dataset, which includes the Global Surface Water (GSW) dataset and the Global Surface Water Dynamics (GLAD) dataset as training targets, DiffWater enhances the objectives of conditional diffusion models by integrating advanced conditional feature extractors and implementing multi-level fusion of conditional and temporal features, thereby achieving significant improvements in SWF estimation performance. Comprehensive experimental evaluations on the reference dataset demonstrate that DiffWater achieved the best performance, with a root mean square error (RMSE) of 4.987% and a correlation coefficient (R) of 0.946. Compared to state-of-the-art SWF estimation methods, the proposed approach demonstrated significant improvements in both quantitative and qualitative results.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".