Abstract: To address the issues of high cost and long cycles associated with traditional reservoir storage monitoring methods,this study explores an efficient and low-cost dynamic monitoring technology,providing a novel solution for medium and small reservoir management.Taking Jurong reservoir as the study case,we integrated Sentinel-2 data with in-situ measurements and developed a satellite-derived bathymetry model using the random forest algorithm.By combining water level data,underwater topography was reconstructed to generate an updated storage capacity curve.The results demonstrate good accuracy in water depth inversion (R²=0.90, RMSE=0.43 m),with the derived storage capacity showing strong consistency with official data trends.A linear relationship between surface area and storage increment was established.Compared to conventional methods,this approach significantly shortens the update cycle of storage capacity curves while reducing costs.Therefore,satellite-derived bathymetry method proves to be a reliable method for dynamic storage monitoring,offering advantages in speed,cost-effectiveness,and operational convenience.This technique is particularly suitable for routine management of medium and small reservoirs,providing water authorities with a practical and efficient technical solution.

Key words: Jurong reservoir, random forest, satellite-derived bathymetry, underwater topography, reservoir capacity curve