Accurate river water level estimation is essential for flood prevention, disaster mitigation, and water resource management, but complex plain river-network regions present significant challenges due to strong non-stationarity and nonlinearity. A study published in Satellite Navigation on October 12, 2026, introduces a physics-guided noise-enhanced adaptive fusion dual-stream network (PGNAF-DSNet) that integrates observations from the Cyclone Global Navigation Satellite System (CYGNSS) and Sentinel-1 synthetic aperture radar (SAR) over the Jiangsu plain in China.
Traditional water level data come from hydrological stations, but sparse spatial coverage, high operational costs, and limited real-time capability hinder continuous monitoring in complex plain river-network regions, where water level variations are jointly influenced by precipitation, sluice regulation, human activities, and agricultural irrigation. Satellite remote sensing offers an alternative, but conventional altimeters have sparse cross-track spacing, SAR has limited temporal resolution, and CYGNSS suffers from coarse spatial resolution and observational noise. These limitations underscore the need for feature-level fusion of SAR and CYGNSS observations.
The PGNAF-DSNet model addresses these challenges through physics-guided feature construction, dual-stream temporal encoding, and adaptive feature fusion. Researchers from Nanjing University of Posts and Telecommunications, Henan Polytechnic University, Anhui University, and the Nanjing Institute of Geography and Limnology of the Chinese Academy of Sciences developed the model. Key innovations include the Polarization Fusion Surface Water Index (PFSWI), which multiplies Sentinel-1 backscatter differences by CYGNSS reflectivity, and the river width coefficient of variation (Width_cv), embedding physical priors into the model. A selective Gaussian noise enhancement strategy, optimized using the Tree-structured Parzen Estimator algorithm (σ = 0.04157), is applied only to channels susceptible to observational errors such as VV, VH, river width, and precipitation. The dual-stream architecture employs parallel Transformer and long short-term memory (LSTM) encoders to capture global and local temporal patterns, respectively, before fusion through an Adaptive Feature Fusion module that dynamically weights the two branches at the channel level.
On a strictly chronological dataset (training: January 2021–November 2022; testing: May–October 2023), PGNAF-DSNet outperformed CNN, Transformer, LSTM, and Transformer-LSTM baselines, achieving a root mean square error of 0.2388 meters, mean absolute error of 0.1624 meters, and a correlation coefficient of 0.9889. Notably, the model maintained a correlation coefficient above 0.95 across all forecast horizons up to T+6. Ablation experiments confirmed each component's contribution, and SHAP analysis indicated that Width_cv and PFSWI both exhibit high feature importance.
The authors said the study's core contribution lies in moving beyond simple data-level fusion. Previous approaches mainly used CYGNSS to fill temporal gaps in SAR observations without fully exploiting cross-modal feature interactions. By constructing physics-guided fused features and adopting a dual-stream architecture, the model captures both global trends and local dynamics of water level changes in complex river networks. The selective noise enhancement strategy acts as a regularizer, improving robustness against observational uncertainty and human-induced disturbances.
PGNAF-DSNet offers a practical framework for water level monitoring in plain river-network regions, with potential applications in flood early warning, water resource management, and river-network regulation. The model reduced prediction errors by approximately 50% in areas significantly affected by human activities such as sluice regulation and agricultural irrigation, and maintained stable performance at T+6 forecasts, demonstrating promise for operational forecasting. The framework could also be extended to incorporate additional satellite observations, such as Surface Water and Ocean Topography water surface elevations, and adapted to other river systems through transfer learning. The full study is available via DOI 10.1186/s43020-026-00219-7.
