The timely and accurate monitoring of water quality via remote sensing is of great significance to water environment protection.However,the relationship between water quality parameters and surface reflectance is not a simple linear one.BP neural network and Support Vector Machine (SVM) have been widely used in water quality inversion for their nonlinear simulation characteristics,yet traditional BP neural network is perplexed by slow convergence and being easy to fall into local optimum,while SVM is greatly affected by penalty coefficient and kernel function parameter.Here,a coupled model using Sparrow Search Algorithm (SSA) to optimize BP neural network and SVM is proposed to retrieve water quality parameters of conductivity and turbidity in Yunlong Lake from Sentinel-2 images.SSA is used to optimize the parameters of BP neural network and SVM,the model weight is calculated based on verification set MAE,and the final inversion results are obtained after the weighted calculation of output layer of SSA-BP and SSA-SVM model test group.And comparisons are carried out between the proposed SSA-BP-SVM model and BPNN,SVM,SSA-BP,and SSA-SVM models.The results show that,the sensitive bands of Sentinel-2 image to conductivity and turbidity are visible light and shortwave infrared;the proposed model of SSA-BP-SVM is more precise with the R² of the inverted conductivity and turbidity being 0.92 and 0.89,respectively;the Yunlong Lake is a typical urban water body with conductivity being greatly affected by the drainage from upstream water treatment plant and turbidity being greatly affected by particulate pollutants from social production activities.The proposed SSA-BP-SVM model has good application potential in water quality inversion from Sentinel-2 image,which can provide technical support for water quality monitoring and protection of Yunlong Lake.