Retrieval of oceanic suspended sediment concentration with fBm-wavelet regression

Abstract

The aim of this study is to examine the feasibility of the fBm-Wavelet method which combined Fractional Brownian motion (fBm)with wavelet analysis in retrieval of suspended sediment concentration. The fBm-Wavelet method is compared with traditional band ratio regression models for the aim. 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 development of retrieval models based on the fBm-wavelet method and band ratio regression. Second, we select band ratio regression model with high R-square value and low Root Mean Squared Error as the model. Finally, the best band ratio regression model is compared with fBm-wavelet model in various datasets by leave-one-out cross validation. The experimental results show that the prediction accuracy of the fBm-wavelet model is better than the band ratio regression models based on the mean absolute error. The fBm-wavelet model using full bands yielded superior results than using TM1 and TM4 bands in terms of accuracy. The findings suggest that the fBm-wavelet model is available using full bands data. The fBm-wavelet method can be applied in retrieval of suspended sediment concentration without selecting bands and constructing band ratio formula. It is a promising alternative to suspended sediment retrieval models.