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

Abstract

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 (r s ) for all sediments was above 0.7 while r s 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 (> R 2 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. ARTICLE HISTORY

Algorithm to Calculate Suspended Sediment Concentration Using Landsat 8 Imagery

“…However, correlation between the SSC and the reflectance at the ocean surface was weak (R 2 = 0.46), because of the environmental effects (atmosphere, aerosols, tides etc.) (Qu et al, 2016). Data obtained in the delta of the Grijalva river using Landsat 7 and the most recent Landsat 8 satellites, has a correlation of R 2 = 0.279 with 29 samples and R 2 = 0.334 with 20 samples of Landsat 7 and R 2 = 0.228 with 29 samples and R 2 = 0.423 with 20 samples of Landsat 8.…”

“…First calculations of sediments concentrations were based mainly on atmospheric variables due to the fact that they represented major variations in these estimations, however, between 1950 and 1970, rivers in the world with big flows started to show a decrement in the sediments they were transporting, this provoked that spectral responses had variations (Qu et al, 2016;Tessler et al, 2015).…”

“…Between 1983 and 2009 accumulation of sediments drop from 158 × 10 6 m 3 to 6.1 × 10 6 m 3 yr -1 (Nooren et al, 2017). Nowadays, the algorithms and models developed to calculate SSC take into account changes in the spectral response of the sediments, as well as atmospheric variables (Lu et al, 2013;Qu et al, 2016;Zhang et al, 2015). In the dispersion diagram the relationship between both variables is low due the high atmospheric interference between the satellite and water surface; additionally, water vapor and aerosols are present, which causes the sensor not to detect the total reflectance of the sediments.…”

“…Suspended sediments in the plume of a river mouth control the delta morphology and ecology. In water bodies suspended sediments are quantified as a concentration, as the total amount of solids suspended in the mixture water-sediment, generally expressed in mg/l (Carbonneau and Piégay, 2012;Qu, 2016).…”

Practical Assessment of Real‐Time Suspended Sediment Load Monitoring Using Time Domain Reflectometry

“…Therefore, field automated surrogates for SSC measurement are required to overcome these limitations. Among these automated surrogates are optical turbidity meter–based methods (Campbell et al., 2005; Downing, 1996; Gentile et al., 2010; Gillett & Marchiori, 2019; Khairi et al., 2015; Matos et al., 2019; Navratil et al., 2011; Omar & MatJafri, 2009; Pallarès et al., 2021), acoustic transmission or backscattering methods (Huang et al., 2018; Li et al., 2018; Moore et al., 2013; Thorne & Hanes, 2002; Thorne & Meral, 2008; Thorne et al., 1991; Topping et al., 2007; Wenjie et al., 2019), focused beam reflectance measurement methods (which involve the use of a laser focused on a focal plane and retrieval of the backscattering data of a sediment particle) (Heath et al., 2002; Jeldres et al., 2020; Law & Bale, 1998), laser diffraction methods (Agrawal & Pottsmith, 1994; Blott et al., 2004; Czuba et al., 2015), nuclear methods (which involve the use of a gamma radioactive source to measure the density of turbid water) (Berke & Rakoczi, 1981; McHenry et al., 1968), spectral reflectance methods (which involve the use of a remote sensing technique to capture the increased volume reflectance of water areas) (Choubey, 1994; Han, 1997; Qu, 2014; Sváb et al., 2005), and differential pressure methods (which involve the measurement of differential pressure from two transmitters at different water depths) (Hsu & Cai, 2010; Lewis & Rasmussen, 1996; Petrovic et al., 2016; Sumi et al., 2002; Tollner et al., 2005).…”