A Dual-Source Deep Learning Model for Mapping Wet Anabranches in Braided Rivers on the Tibetan Plateau

The Tibetan Plateau is home to dozens of large braided rivers, a phenomenon that is extremely rare in the global distribution of alluvial rivers. Dynamically monitoring these rivers is crucial for understanding the unique sedimentary processes and hydrodynamic patterns of the plateau. However, the complex network of water channels and sandbars, coupled with frequent cloud cover, intricate terrain, and numerous branches in the plateau region, poses significant challenges to traditional monitoring and extraction methods. To address these challenges, this study proposes a novel dual-feature encoder deep learning model, DIResU-Net, which integrates Sentinel-1 and Sentinel-2 data to achieve high-precision and long-term extraction of braided rivers. The model employs dual encoders to extract features from optical and radar data, combined with a unified decoder and an attention mechanism for efficient feature fusion. Additionally, a multi-composite loss function was designed to enhance the model's performance. Experimental results demonstrate that the proposed DIResU-Net achieves a high F1-score of 0.87 and IoU of 0.79 under cloud-free conditions, significantly outperforming traditional single-source models. In cloud-covered scenarios, the model maintains robust performance (IoU > 0.73) by leveraging the complementary advantages of Sentinel-1 and Sentinel-2 data. The model also exhibits strong temporal generalization in mapping river morphology from 2019 to 2024, highlighting its value for long-term monitoring and environmental management. Further analysis of morphological parameters—such as river width, channel density, and braiding index—reveals clear seasonal and interannual fluctuations across typical river sections, reflecting the dynamic nature of braided river systems on the plateau. This study provides a scalable framework for high-resolution mapping and long-term monitoring of braided rivers, with implications for hydrological analysis and basin-scale management on the Tibetan Plateau.