Reconstructing Reservoir Water Level-Area-Storage Volume Curve Using Multi-source Satellite Imagery and Intelligent Classification Algorithms

The water level-area-storage volume (Z-A-V) relationship serves as the cornerstone of reservoir operations, governing water allocation, flood mitigation, and power generation. Sedimentation-induced capacity alterations can progressively degrade Z-A-V accuracy, yet systematic curve updates remain inadequately implemented across developing nations. Traditional reconstruction approaches face inherent limitations due to resource-intensive requirements including costly field surveys and data scarcity. Emerging satellite remote sensing technologies show transformative potential for dynamic reservoir monitoring, though their application in Z-A-V curve updating still requires substantive exploration. This study evaluates the capability of multi-source satellite imagery, including optical data from Landsat 8 and Sentinel-2, and synthetic aperture radar (SAR) data from Sentinel-1, for accurately reconstructing the Z-A-V curve. To extract high-accuracy reservoir surface extents, two advanced algorithms–Random Forest Classification (RFC) and Otsu thresholding–are applied to the optimal and SAR imagery, respectively, to delineate water and non-water pixels. The suitability of the satellite-derived Z-A-V curve is further assessed by estimating the storage capacity loss due to sedimentation accumulation and comparing these estimates with that derived from design curve. Using the Hongjiadu Reservoir in the upper reach of the Wujiang River, China, as a case study, the results show that: (1) all three satellite datasets accurately extract the reservoir surface areas, achieving average accuracies of 96%, 89%, and 88% for Sentinel-1, Landsat 8, and Sentinel-2, respectively; (2) the Z-A curve reconstructed from Sentinel-1 SAR imagery achieves the highest accuracy with and the coefficient of determination (R²) > 0.99 and root mean square error (RMSE)