The suspended sediment concentration （SSC） is an extremely important property for water monitoring. Since machine learning technology has been successfully applied in many domains， we combined the strengths of empirical algorithms and the artificial neural network （ANN） to further improve remote sensing retrieval results. In this study， the neural network calibrator （NNC） based on ANN was proposed to secondarily correct the empirical coarse results from empirical algorithms and generate fine results. A specialized regularization term has been employed in order to prevent overfitting problem in case of the small dataset. Based on the Gaofen-5 （GF-5） hyperspectral remote sensing data and the concurrently collected SSC field measurements in the Yangtze estuarine and coastal waters， we systematically investigated 4 empirical baseline models and evaluated the improvement of accuracy after the calibration of NNC. Two typical applications of NNC models consisting baseline model calibration and temporal calibration have been tested on each baseline models. In both applications， results showed that the calibrated D’Sa model is of highest accuracy. By employing the baseline model calibration， the root mean square error （RMSE） decreased from 0.1495 g/L to 0.1436 g/L， the mean absolute percentage error （MAPE） decreased from 0.7821 to 0.7580 and the coefficient of determination （R²） increased from 0.6805 to 0.6926. After implementation of the temporal calibration， MAPE decreased from 0.8657 to 0.7817 and R² increased from 0.6688 to 0.7155. Finally， the entire GF-5 hyperspectral images on target date were processed using the NNC calibrated model with the highest accuracy. Our work provides a universal double calibration method to minimize the inherent errors of the baseline models and a moderate improvement of accuracy can be achieved.