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This paper brought out a new idea on the retrieval of suspended sediment concentration,which uses both the water-leaving radiance from remote sensing data and the grain size of the suspended sediment.A principal component model and a neural network model based on those two parameters were constructed.The analyzing results indicate that testing errors of the models using the two parameters are 0.256 and 0.244,while the errors using only water-leaving radiance are 0.384 and 0.390.The stability of the models with grain size parameter is also better than the one without grain size.This research proved that it is necessary to introduce the grain size parameter into suspended sediment concentration retrieval models in order to improve the retrieval precision of these models.
This paper brought out a new idea on the retrieval of suspended sediment concentration, which uses both the water-leaving radiance from remote sensing data and the grain size of the suspended sediment. A principal component model and a neural network model based on those two parameters were constructed.The analytical results indicate that that testing errors of the models using the two parameters are 0.256 and 0.244 while the errors using only water-leaving radiance are 0.384 and 0.390.The stability of the models with grain size parameter is also better than the one without grain size. This research proved that it is necessary to introduce the grain size parameter into suspended sediment concentration retrieval models in order to improve the retrieval precision of these models.