Abstract: Discrete binary particle swarm optimization (DBPSP) has been used in this study to prioritize the characteristic bands of suspended solid concentration and turbidity. This method can reduce the number of input parameters and uncertainty in partial least squares (PLS) modeling, and improve the prediction accuracy of PLS models. In a case study of water quality of the Nansi Lake, we have developed and verified a PLS model and a NDBPSO-PLS model separately for retrieval calculations of suspended solid concentration and turbidity using the hyper-spectral remote sensing data and synchronous sampling data measured on July 21 to 23, 2014. The results indicate that for the NDBPSO-PLS calculations of suspended solid concentration and turbidity, the number of characteristic bands required by it was only 137 and 134 respectively, a great reduction relative to the number of 370 required by the PLS model. And its input feature variables were reduced to 21 from 60 PLS variables. Its prediction accuracy is better than the PLS model. Thus, NDBPSO-PLS would provide an effective method for improvement of the retrieval calculations of suspended solid concentration and turbidity.