Abstract: The reservoir water level fluctuation zone (WLFZ) serves as a final barrier preventing pollutants from hillslope runoff from entering reservoir water bodies.Accurate monitoring of its spatial information is critical for effective reservoir management.However, the dynamic exposure of WLFZs caused by water level fluctuations poses significant challenges for large-scale monitoring, and existing extraction methods often suffer from limited precision.Focusing on 35 large-scale reservoirs in Sichuan Province, this study proposes an optimized digital image threshold segmentation algorithm, GA-IWOA-Otsu, which integrates the Genetic Algorithm (GA) and Improved Whale Optimization Algorithm (IWOA) with the Maximum Inter-class Variance method (Otsu).The WLFZs are delineated by extracting water surface extents from dual-phase Sentinel-1 Synthetic Aperture Radar (SAR) imagery and performing spatial clipping operations.The results demonstrate that: ① Based on macroscopic comparisons of regional distribution and calculated areas, the GA-IWOA-Otsu coupling algorithm exhibits superior overall performance, particularly in extracting water bodies with complex multi-tributary systems.It also achieves high extraction accuracy for reservoirs with WLFZ areas ranging from 20~100 km².② Compared with Otsu, GA-Otsu, Canny-Otsu, and ISODATA algorithms, the proposed GA-IWOA-Otsu algorithm performs better across three evaluation metrics: Kappa coefficient, RMSE and R², enabling more precise extraction of WLFZs.This study contributes to the spatial information extraction of regional reservoir WLFZs, facilitating ecological restoration and enhancing reservoir operation management.