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A new approach for monitoring water quality is proposed based on quantitative remote sensing in Huangpu River, Shanghai. The data processing for multi-spectral remote sensing imagery is first presented. The inversion models for two typical water quality parameters - Dissolved oxygen (DO) and Secchi disk (SD) are then developed. Based on the derived models and multi-temporal remote sensing imagery, the spatial- temporal analysis for the water quality variation is therefore conducted. The results show that the proposed models can detect effectively the temporal and spatial distribution of water quality.