Research on River Water Body Extraction and Discharge Estimation Using Multi-Source Remote Sensing Data

River discharge is a pivotal metric in hydrological and water resources management. To address limitations in traditional hydrological monitoring stations, such as sparse distribution and high data acquisition costs, this study focuses on the Fuyang River LHK hydrological station in Handan City, Hebei Province, China, and proposes a synergistic estimation method for river discharge using multi-source remote sensing data. The approach first extracts river water bodies from Sentinel-1 SAR imagery and Sentinel-2 optical imagery via EN-OTSU and MNDWI-OTSU algorithms, respectively. Subsequently, river width is calculated using the water area-to-length ratio method to reduce errors caused by edge effects. Finally, a power-law discharge estimation model is developed by fitting river width to discharge data. For water body extraction, the Sentinel-2 MNDWI-OTSU method achieves the highest accuracy (overall accuracy: 95.31%, Kappa coefficient: 0.90), followed by the Sentinel-1 EN-OTSU method (overall accuracy: 92.55%, Kappa coefficient: 0.89). For discharge estimation, both data sources exhibit robust inversion performance, with the Sentinel-1-based model showing superior error stability (NSE=0.83, R²=0.83, RRMSE=0.24) and the Sentinel-2-based model marginally better theoretical fit (NSE=0.84, R²=0.84, RRMSE=0.26). Compared with traditional in situ measurements and single-sensor approaches, this method enables a shift from point-based to basin-wide dynamic monitoring, resolving data scarcity in ungauged regions; it integrates the high boundary delineation precision of optical remote sensing with the all-weather penetration of radar, effectively countering interruptions from cloudy and rainy conditions; and it reduces reliance on ground infrastructure, providing a cost-effective, reliable framework for river monitoring and informed water resource allocation.