Assessment of SWOT-RiverSP Data Reliability and Accuracy over Canadian Rivers
Bibliographic record
Abstract
Launched in December 2022, the SWOT (Surface Water and Ocean Topography) mission represents a major breakthrough in global surface water monitoring. Leveraging Ka-band radar interferometry, SWOT can observe water surface elevation (WSE) in rivers wider than 50 m, covering more than 150,000 km of linear river across Canada. While the scientific potential of SWOT is substantial, its operational use hinges critically on their reliability and accuracy of the data. In this context, a study was conducted to assess the quality of SWOT data comparing the RiverSP product with multiple reference sources across Canadian territory. The first component of this work compares SWOT-derived WSE with ground truth across diverse hydrological contexts, using records from national hydrometric stations. Approximately 100 stations located on rivers with known elevation datum were selected and properly converted to the SWOT datum. Each station was then matched to the nearest SWOT RiverSP node within a 200-meter range, allowing for direct comparison between in situ and SWOT WSE measurements. The second component is based on field campaigns conducted between 2023 and 2025 on four rivers: the Nashwaak (New Brunswick), Saint-François (Québec), Chaudière (Québec), and Au Saumon (Québec). High-precision GNSS measurements were performed from riverbank or by boat to measure WSE along the rivers during SWOT overpasses. These ground-based measurements were then compared to the nearest SWOT nodes, providing an opportunity to validate SWOT outputs under known conditions, while considering factors such as river width, slope, discharge, and environmental influences including vegetation and infrastructure. The third component examines the reliability of the SWOT-RiverSP product by analyzing the factors that influence the availability of high quality data. It was observed that certain areas consistently yield high-quality nodes (node_q < 2), while others regularly produce poor quality nodes (nod_q > 1). These factors are divided into three categories: 1) those associated with the sensors, such as cross-track distance, flow angle, or cross-over calibration, 2) those associated with river characteristics, such as width, the presence of canopy on the bank or rock in the river, proximity to a bridge, meanders, and slope; and 3) finally, those related to hydroclimatic conditions, such as lack of wind (which affects dark water), precipitation, discharges, and ice cover. A Random Forest model is used to rank the relative importance of these variables in predicting node reliability. The analysis is performed on rivers in New Brunswick and Québec where discharge data are available (from stations or hydrological models), as well as LiDAR data for river characterization. Wind and precipitation conditions are derived from ERA5-Land datasets, whereas vegetation analysis is assessed from aerial imagery. In addition, field campaigns have also been carried out in specific areas of concern to better understand the factors that may affect the SWOT signal. Overall, this study aims to assess the reliability and accuracy of SWOT data across Canadian rivers by integrating satellite observations, hydrometric stations, and field measurements. It lays the groundwork for the informed use of SWOT in hydrological modeling, flood forecasting, and freshwater resource management.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".