Accurate river water level estimation is essential for flood prevention, disaster mitigation and water resource management, but complex plain river-network regions present severe 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. The model achieved a root mean square error of 0.2388 meters and a correlation coefficient of 0.9889 on a strictly chronological test set, reducing prediction errors by approximately 50% in regions significantly affected by human activities.
The importance of this work lies in its potential to overcome the limitations of traditional hydrological stations, which suffer from sparse spatial coverage, high operational costs and limited real-time capability. 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. The 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 addressed these challenges through feature-level fusion of SAR and CYGNSS multi-source observations.
The study introduces several key innovations. First, researchers constructed physics-guided fused features: PFSWI (Polarization Fusion Surface Water Index), which multiplies Sentinel-1 backscatter differences by CYGNSS reflectivity, and Width_cv (river width coefficient of variation), embedding physical priors into the model. Second, they introduced a selective Gaussian noise enhancement strategy based on wavelet-denoised residual analysis, optimized using the Tree-structured Parzen Estimator algorithm (σ = 0.04157), applied only to channels susceptible to observational errors such as VV, VH, river width and precipitation. Third, the dual-stream architecture employs parallel Transformer and long short-term memory 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 Convolutional Neural Network, Transformer, LSTM and Transformer-LSTM baselines, achieving a mean absolute error of 0.1624 meters and maintaining 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, as previous approaches mainly used CYGNSS to fill temporal gaps in SAR observations without fully exploiting cross-modal feature interactions. They said that 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, while 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.
