A new artificial intelligence framework, named RivDepth, has been developed to map river depth in highly sediment-laden waters, where conventional satellite bathymetry often fails. The method, detailed in a study accepted for publication in Environmental Science and Ecotechnology (DOI: 10.1016/j.ese.2026.100711), combines Sentinel-2 satellite spectral information with an optically derived suspended sediment concentration (SSC) proxy to retrieve water depth pixel by pixel. Tested on the lower Yellow River, one of the world's most sediment-laden rivers, the model captured complex interactions among water depth, reflectance, and sediment load with high accuracy.
The research was conducted by scientists from the State Key Laboratory of Hydroscience and Engineering at Tsinghua University, the State Key Laboratory of Water Cycle and Water Security in River Basin, and the State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin at the China Institute of Water Resources and Hydropower Research. RivDepth was applied to an approximately 786-kilometer reach of the lower Yellow River, from Xixiayuan to Lijin, using Sentinel-2 Level-2A imagery, field-measured cross-sectional elevation data, water-level records, and in situ SSC observations.
RivDepth's innovation lies in its adaptive AI expert module, which integrates parallel random forest (PRF), extreme gradient boosting (XGBoost), support vector regression (SVR), and multilayer perceptron (MLP). Instead of applying a single model uniformly, RivDepth performs preliminary prediction, inference, and decision-making to select the most suitable strategy for each water condition. Shapley additive explanations (SHAP) analysis identified shortwave infrared bands, red and red-edge bands, the water vapor band, the aerosol/blue band, and the SSC proxy as key predictors.
By learning different depth–reflectance–SSC patterns and choosing prediction strategies at the pixel level, the model adapts to spatially changing sediment and channel conditions. This is particularly important for rivers such as the Yellow River, where suspended sediment, flow structure, and optical signals vary sharply over long distances. The method turns routine satellite observations into actionable depth information for river science and management. More frequent and continuous bathymetric information could help track channel change, identify thalweg migration, improve sediment-transport modeling, and support flood-risk and habitat assessments.
The approach offers a new way to monitor underwater river topography, supporting flood assessment, sediment budget, channel management, and integrated river management. RivDepth can be further improved as higher-resolution satellite imagery and more accurate spatial SSC indicators become available. With broader validation, the workflow may be adapted to other turbid river systems, offering a scalable tool for integrated watershed monitoring and management.
Funding for this work was provided by the Team Key Project of the State Key Laboratory of Hydroscience and Engineering (No. sklhse-TD-2024-E01) and the National Natural Science Foundation of China (U2243218, U2243222). The study was accepted for publication on May 20, 2026, in Environmental Science and Ecotechnology, an international, peer-reviewed, open-access journal published by Elsevier with an impact factor of 14.3. The original source URL for the study is https://doi.org/10.1016/j.ese.2026.100711.
