Remote sensing of inland water quality parameters

Optical sensors on various platforms provide both spatial and temporal information for understanding of water quality. Highly significant relationships between remotely sensed data and water quality parameters have been identified. Bio-optical models in recent years are summarily introduced. Linear model is simple and effective, and complicated model, such as multivariate optimization and artificial neural network, also have successful applications. TO get accurate remotely sensed image, heterogeneous distribution of aerosol has to be taken into consideration, and a proper atmospheric correction is essential. Principle and major methods of atmospheric correction are summarized and assessed in this paper. Quantification of suspended sediments, chlorophyll and dissolved organics by remote sensing are evaluated extensively and separately as well as their spectral features. Validation of model results is also discussed briefly. Though many regressed algorithms have been presented with good results, they are mostly site-specific, really multi-temporal and multi-spatial model for remote sensing of inland water quality parameters is still unavailable. The integration of remote sensing and geographic information system can make the water quality monitoring and assessment wider, more rapid, and more accurate.