Water level data traditionally 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, exhibiting strong nonlinearity, non-stationarity and spatial heterogeneity. Satellite remote sensing offers an alternative, but conventional altimeters have sparse cross-track spacing, Synthetic Aperture Radar (SAR) has limited temporal resolution, and the Cyclone Global Navigation Satellite System (CYGNSS) suffers from coarse spatial resolution and observational noise. Based on these challenges, there is a need for in-depth research on feature-level fusion of SAR and CYGNSS multi-source observations.
In a study published in Satellite Navigation on October 12, 2026, 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 Physics-Guided Noise-Enhanced Adaptive Fusion Dual-Stream Network (PGNAF-DSNet) for water level estimation over the complex river network of the Jiangsu plain in China. The model integrates Sentinel-1 SAR and CYGNSS observations through physics-guided feature construction, dual-stream temporal encoding and adaptive feature fusion.
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 (TPE) 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 (LSTM) encoders to capture global and local temporal patterns, respectively, before fusion through an Adaptive Feature Fusion (AFF) 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 (CNN), Transformer, LSTM and Transformer-LSTM baselines in the model achieved a root mean square error (SRMSE) (0.2388 m), mean absolute error (SMAE) (0.1624 m) and a correlation coefficient (R) (0.9889). Notably, the model maintained R > 0.95 across all forecast horizons up to T+6. Ablation experiments confirmed each component's contribution, and SHAP (SHapley Additive exPlanations) 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. They said 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. They also said 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 (R > 0.95), demonstrating promise for operational forecasting. The framework could also be extended to incorporate additional satellite observations, such as Surface Water and Ocean Topography (SWOT) water surface elevations, and adapted to other river systems through transfer learning.
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References
DOI
10.1186/s43020-026-00219-7
Original Source URL
https://doi.org/10.1186/s43020-026-00219-7
Funding Information
This work was supported by the Henan International Science and Technology Cooperation Key Program (Grant No. 241111520700), National Natural Science Foundation of China (Grant No. 42671590), and Natural Science Foundation of NJUPT under Grant NY224164.
About Satellite Navigation
Satellite Navigation (ISSN: 2662-1363; ISSN: 2662-9291) Satellite Navigation is the official journal of the Aerospace Information Research Institute. The journal aims to report innovative ideas, new results, and progress in the theories, techniques, and applications of satellite navigation. The journal welcomes original articles, reviews and commentaries.