At present, the use of remote sensing technology in a timely and accurate monitoring of water quality, a comprehensive understanding of the distribution of water quality is of great significance to the protection of water environment.However, the relationship between water quality parameters and surface reflectance is not simple linear. BP neural network and SVM are widely used in water quality inversion because of their nonlinear simulation characteristics.However, the traditional BP neural network has the problems of slow convergence and easy to fall into local optimum. Although SVM has good fitting ability, it is greatly affected by penalty coefficient and kernel function parameter.Therefore, taking Yunlong Lake as the study area, using Sentinel-2 image and measured data, aiming at the important water quality parameters of conductivity and turbidity, a water quality inversion coupling model based on sparrow search algorithm (SSA) to optimize BP neural network and SVM was proposed. SSA was used to optimize the parameters of BP neural network and SVM, the model weight was calculated based on verification set MAE, and the final inversion result was obtained after the weighted calculation of output layer of SSA-BP and SSA-SVM model test group.And compared with BPNN, SVM, SSA-BP, SSA-SVM model.The results show that: (1) The sensitive bands of Sentinel-2 image to conductivity and turbidity are visible light and short wavelength infrared.(2) that precision of the couple model of SSA-BP-SVM is higher, and the R2 of the inversion model of conductivity and turbidity are 0.92 and 0.89 respectively.(3) the yunlong lake has the typical characteristics of urban water body, conductivity is greatly affected by the upstream of the south at water treatment plant drainage, turbidity is greatly affected by the social production activities of particulate pollutants.The SSA-BP-SVM model based on Sentinel-2 image has a good application potential in water quality inversion, which can provide a certain technical support for water quality monitoring and protection measures of Yunlong Lake.