Monitoring water quality of inland lakes and reservoirs is a great concern for the public and government in China. Water turbidity is a reliable and direct indicator that can reflect the water quality. Remote sensing has become an efficient technology for monitoring large-scale water turbidity. This study aims to search an optimal regression model to accurately predict water turbidity using remote sensing data. To achieve this goal, 187 water samples were collected from field campaigns across Northeast China, in 2018, of which the samples were gathered within ±6 days of Sentinel-2 overpasses. The spectral reflectance data was used as independent variables for modeling. The simple regression, partial least squares regression, support vector regression, extreme learning machine, back-propagation neural network, classification and regression tree, gradient boosting decision tree (GBDT), random forest (RF), and K-nearest neighbor were used to compare. From model validation, we identified GBDT as the best regression model ( R ² = 0.88, RMSE = 9.90 NTU, MAE = 6.71 NTU). We applied GBDT to retrieve the water turbidity and obtained a satisfactory result. Feature selection technique from tree-based ensemble method was also tested. We selected B2, B3, B4, and B5 as the important variables because of their high ability to explain the variation of turbidity. These results demonstrated the significance of using a promising method to retrieve water turbidity using Sentinel-2 imagery at the regional scale. It is beneficial to monitor the spatial-temporal distribution of water turbidity; support water quality management and inland water environment protection.