The aim of this study is to examine the feasibility of Support vector regression (SVR) in retrieval of suspended sediment
concentration by comparing it with band ratio regression models. First, the remote sensing reflectance and the suspended
sediment concentrations were measured in field and in laboratory. The in situ dataset and laboratory dataset were used in
t developing retrieval models based on support vector regression and band ratio regression. Second, we select band ratio
regression model with high R-square value and low Root Mean Squared Error as the best band ratio regression model.
Finally, the best band ratio regression model was compared with SVR model in different datasets by leave-one-out cross
validation. The experimental results demonstrate that the prediction accuracy of support vector regression outperforms
the band ratio regression models based on the mean absolute error in general. SVR using all bands yielded slightly
superior results than using TM1 and TM4 bands in terms of accuracy. The findings suggest that the SVR model is
available using all bands data. The support vector regression can be applied in retrieval of suspended sediment
concentration without selecting bands and constructing band ratio expression. SVR is a promising alternative to
suspended sediment retrieval models.