Using Sentinel-2 data and multiple methods to invert the permanganate index （COD_Mn） of deep-water lakes and reservoirs in the Karst Plateau is of great significance for the regional water environment management and enrichment of water quality inversion theories. Taking Hongfeng Lake and Baihua Lake as the research area， based on the Sentinel-2 MSI image and COD_Mn concentration data， use Random Forest Regression （RFR）， Support Vector Regression Method （SVR）， Gaussian Process Regression （GPR）， Obtaining COD_Mn spatial distribution in different periods of 2018~2020. The results show that： ① The RFR model has the highest accuracy， the verification set is 0.222 mg·L^-1， MAPE is 5.84%， and R² is 0.841； In addition to the upstream of Baihua Lake， the COD_Mn concentration of the overall lake is low and there is not much change over time. Studies have shown that the RFR model and Sentinel-2 data are well applicable to monitoring the COD_Mn concentration monitoring of deep-water lakes in the Karst Plateau.