A spectral mixing algorithm for quantifying suspended sediment concentration in the Yellow River: a simulation based on a controlled laboratory experiment

This study investigated the upper limit of suspended sediment concentration SSC with respect to the relationship between SSC and reflectance to develop an SSC remote-sensing model for the highly turbid Yellow River. An SSC quantification model was generated by using the spectral mixing index of sediments in water and sediment mixtures. In this study, laboratory experiments were made to measure the spectral curves of sediment-laden water with a high-resolution spectroradiometer. River-bed deposited sediments from two sites, one on the Yellow River at Huayuankou HYK and the other on the Wei River at Yangling YL, and their sand, silt and clay particle groups were used for laboratory experiments to measure the spectral responses of sediment-laden water. The correlation analysis depicted stable correlation between SSC and reflectance at wavelengths ranging from 450 to 1000 nm, in which Spearman rank correlation coefficient rs for all sediments was above 0.7 while rs for the HYK natural sediment exceeded 0.9. Experimental results revealed the curves of the relationship between SSC and reflectance, up to 40 g l−1. A physical-based exponential model > 0.9 at each simulated Landsat band effectively interpreted the relationship between SSC and reflectance. The highest upper limit SSCs at 21 and 15 g l−1 in natural YL and HYK sediments, respectively, were observed in Landsat Band 4. A spectral mixing algorithm was used to build the model and estimate the SSC from reflectance at correlated wavelength bands. The spectral mixing algorithm can generate a uniform model that disregards the effects of sediment type by adopting the reflectance curve at the upper-limit SSC to represent the standard reflectance of sediment. This study is useful in understanding the spectral characteristic of high SSC in water and in applying remote-sensing techniques to monitor SSC in the Yellow River.