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Estimation of suspended sediment concentrations in the Yellow River by network monitoring records and satellite data

The sediment concentration in river flow is very important in monitoring of water quality, operation of the hydraulic facilities, and management of water resources. Commonly used sampling method is time consuming, labor intensive, and providing only point data at gauging station. This study is presenting a remote sensing approach to quantify suspended sediment concentration (SSC) of the high turbid flow in the Yellow River in China, where the high sediment transportation from severe soil erosion is a big environmental concern. The approach was based on public accessible satellite images and surface networking monitoring data. With the longest time series records, the Landsat EMT+ images were chosen to establish the remote sensing approach. Daily sediment records from 2 hydrological stations from 1999 to 2008 in the middle part Yellow River were associated with available satellite imaginary. The water reflectance was retrieved from the Landsat images by using an effective easy-to-use atmospheric correction method. Correlation among water reflectance at band 1 to 4, particle size of suspended sediment and SSC are analyzed to establish the SSC indices. According to the significance of relation between SSC and the water reflectance at different bands of Landsat data, regression models between SSC and water reflectance was developed. The model was calibrated by the daily sediment records from surface observation.