Investigating mixing patterns of suspended sediment in a river confluence using high-resolution hyperspectral imagery

•High-resolution hyperspectral images yielded detailed river-confluence SSC maps.•Cluster-based ML regression functioned in optically complex conditions.•Hyperspectral clustering classified the mixing layer between rivers.•Wake and dual counter-rotating cells inhibited mixing in the river confluence.•Hyperspectral retrieval can estimate the mixing coefficient with high reliability. In a river confluence, the fluvial process is complex because of the merging of different flows from rivers with distinct morphologies. However, the mixing of suspended sediment in river confluences has been inadequately investigated owing to the low resolution of conventional techniques for measuring suspended sediment. In this study, we investigated the mixing of suspended sediment at the confluence of the Hwang and Nakdong Rivers in South Korea by analyzing the spectral characteristics of suspended sediment from high-resolution hyperspectral data. We retrieved the suspended sediment concentration (SSC) from high-resolution hyperspectral imagery using the conversion technique, which is a cluster-based machine learning regression with optical variability. Hyperspectral clustering was first applied to classify the water regions of the main river (Nakdong) and its tributary (Hwang). The clustering result at a near-field after the confluence point was strongly related to the mixing degree of flow and sediments from two different rivers. Through segmentation, regressors with hyperspectral clusters enabled accurate SSC measurement. In particular, they precisely retrieved the concentrations adjacent to the mixing layer, even when the relationship between the spectral data and suspended sediment concentrations of the main river and tributary was substantially different. Using a detailed SSC map, we compared two cases of mixing patterns by calculating the variation in sediment concentration along the downstream river confluence. We found that the mixing pattern was different because of the flow velocity and a large sandbar near the stagnant area even though river discharge was similar for both cases. In the slow mixing case, the sandbar caused a velocity-deficit with a wake that reduced the variance of SSC due to the irregular flow near the mixing layer. In this case, dual counter-rotating secondary flow cells also limited mixing across the post-confluence reach. However, when the wake and dual counter-rotating secondary flows weakened due to the erosion of the sandbar and lower flow v