Strait water quality is traditionally monitored and estimated based on in-situ data. Collecting and analyzing in-situ water quality data are expensive, time consuming and with large parts of the water body never sampled. In this study we utilize MODIS data to estimate the water quality of Taiwan Strait, and propose a nonlinear model which incorporates improved real-coded grammatical evolution (GE) with a genetic algorithm (GA). The GE, an evolutionary automatic programming type system, automatically discovers complex nonlinear mathematical relationships among observed salinity concentrations and remote sensed imageries. The algorithm discovers significant input variables and combines them to form mathematical equations automatically. Utilizing GA with GE optimizes an appropriate type of function and its associated coefficients. To enhance searching efficiency and genetic diversity during GA optimization, the macroevolutionary algorithm (MA) is processed as a selection operator. The results of this study indicate that the proposed GEMA yields an efficient optimal solution. GEMA has the advantages of its ability to learn relationships hidden in data and express them automatically in a mathematical manner. Compared with linear regression (LR1), LN transform of linear regression (LR2), and back-propagation neural network (BPN), the performance of GEMA was found better than LR1, LR2 and BPN.