Polygon‐Based Spectral Reflectance Sampling for Real‐Time River Discharge Estimation

Abstract

River discharge estimation (RDE) methods are traditionally prone to high costs, vulnerability to extreme events, and are constrained by real‐time data availability. Current remote sensing‐based RDE approaches mainly extract basic river features such as width and water surface elevation, but rarely exploit the full spectral information from satellite imagery. Most studies also lack proper uncertainty assessment, which is essential for reliable water management decisions. This study proposes a machine learning (ML) approach for RDE that overcomes traditional limitations due to geometry, shadows and vegetated banks. Data are analyzed from 12 United States Geological Survey (USGS) hydrometric stations, spanning four river size classes: stream, small, medium, and large rivers. For each station, mean spectral band values are extracted from multiple polygons along the river channel and paired with temporally coincident in situ discharge measurements. This integrated data set is used to train five ML models—Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and three Bayesian Neural Networks (BNNs)—establishing a robust estimation framework that quantifies uncertainty. Models are trained and tested on 2016–2023 data, then evaluated on 2024–2025 observations to assess temporal transferability. Test (evaluation) results show strong performance with Kling‐Gupta Efficiency (KGE) ranging from 0.60 (0.35) for streams to 0.96 (0.92) for medium and large rivers. The approach maintains accuracy under both normal and extreme flow conditions and is robust to shadowed areas and complex channel geometries. By integrating satellite observations with ML, the proposed method enables real‐time RDE essential for streamflow forecasting, water allocation, and reservoir operation.