Monitoring water quality with classical methods is not an easy task. Remote sensing with wide coverage and multiple temporal monitoring is the best solution for surface water quality monitoring. This paper demonstrates the determination of surface water quality parameters by using principal component analysis (PCA) data fusion and mining techniques with the aid of Landsat 8 OLI (L8 OLI), Sentinel 2A (S2A), and Gokturk-2 (GK2) satellite sensors. Chlorophyll-a, dissolved oxygen, total suspended solids, Secchi disk depth, total dissolved substance, and pH were the parameters selected for surface water quality analysis. High spectral resolution of L8 OLI/S2A images and the high spatial resolution of GK2 images were fused and analyzed by a suite of data mining models to provide more reliable images with both high spatial and temporal resolutions. Surface water quality parameters calculated by PCA-based response surface regression (RSR) method were compared with results obtained from multiple linear regression (MLR), artificial neural network (ANN), and support vector machines (SVMs) data mining methods. The performance of the data mining models derived using only multispectral band data and PCA fused data were quantified using four statistical indices; such as mean-square error (MSE), root MSE, mean absolute error, and coefficient of determination (R-2). The analysis confirmed that the PCA-based RSR method is superior to MLR, ANN, and SVM data mining models to accurately estimate water quality parameters in lakes.