The effect of properties of sediment type on the relationship between suspended sediment concentration and radiance

Choubey, V. K.

doi:10.1080/02626669409492768

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…10, an opposite phenomenon was observed. Our observations contradict the results obtained by Choubey [28] that the increased proportion of the finer fraction could increase the reflection, despite the darker color of the grains. This observation points to the fact that in the relationship between the RRS and sediment parameters, particle size does not play a key role, at least in the case of a comparative analysis of different colored sediments.…”

Section: Table IV Summary Of Spearman's Rank Correlation Coefficient ...contrasting

confidence: 99%

“…First, in both types of sediment, silica predominates in the composition, which proves the clay structure of minerals with a similar specific gravity. This observation excludes a factor that could differentiate the two sediment types in terms of a different amount of reflected radiation [28]. According to Guimarães et al [29], the main color-forming factors of the sediment are the content of TOC and the presence of various forms of iron.…”

Section: Table IV Summary Of Spearman's Rank Correlation Coefficient ...mentioning

confidence: 92%

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The Impact of Glacial Suspension Color on the Relationship Between Its Properties and Marine Water Spectral Reflectance

Wojcik-Dugoborska

Osińska

Bialik

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This study enabled us to determine the sources of sediment for glacial catchments and investigate the differences in properties, i.e., suspended sediment concentration (SSC), turbidity measured in the laboratory (TLAB) and in the field (TF), mean particle diameter (MPD) and chemical composition, between two different-colored sediments that flowed from the glacier terminus. Additionally, the relationship between these properties for two types of suspensions and remote sensing reflectance (RRS) was tested, and the factor with the greatest impact on the value of RRS was determined. The results showed that within one catchment area, there were 4 sediment sources that provide white (S.1) and red (S.2) sediment. Chemical analysis showed that the differences in sediment color may be influenced by the increased content of carbonates in the white sediment (S.1). The S.2 sediment is characterized by mean TLAB, TF, and SSC values higher than 26.6 formazine nephelometric units (FNU), 13.5 FNU, and 50 mg/L, respectively, and the mean MPD was 4.25 lower than that of the S.1 sediment. However, the red sediment had on average 0.1 lower RRS than the white sediment. In addition, the properties of S.1 correlated better with reflectance, reaching a maximum correlation of 0.69 (SSC/RRS 770-810 nm), while S.2 exhibited a negative correlation in 7 out of 12 cases, reaching a maximum correlation of 0.16 (TLAB/SSC/RRS 730-740 nm) and a negative correlation of -0.37 (SSC/RRS 530-570 nm). This result indicated that sediment color may be a key factor in the dependence of glacial suspension properties and spectral reflectance.

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Section: Table IV Summary Of Spearman's Rank Correlation Coefficient ...contrasting

confidence: 99%

Section: Table IV Summary Of Spearman's Rank Correlation Coefficient ...mentioning

confidence: 92%

The Impact of Glacial Suspension Color on the Relationship Between Its Properties and Marine Water Spectral Reflectance

Wojcik-Dugoborska

Osińska

Bialik

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Chung

Wang

2022

Water Resources Research

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Because high suspended sediment concentrations (SSCs) above 10,000 mg L −1 (or ppm) are frequent during severe typhoons and rainfall events in Taiwan, appropriate SSC sampling for suspended sediment load quantification is critical for formulating water soil conservation strategies, understanding scour and erosion characteristics, implementing sedimentation and sluicing operations, ensuring sound water supply management, and addressing environmental ecological problems. However, predicting the appropriate timing for sediment sampling is a complicated task, and manual sampling is risky during storms. Therefore, field automated surrogates for SSC measurement are required to overcome these limitations. Among these automated surrogates are optical turbidity meter-based methods (

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“…Various visible and near-infrared radiationband (NIR-band) combinations have acted as SSC indicators in coastal, estuarine, lagoon, lake, and reservoir environments (Bhargava and Mariam 1991;Choubey 1994;Novo, Hansom, and Curran 1989b;Novo, Steffen, and Braga 1991;Schiebe, Harrington Jr, and Ritchie 1992).…”

Section: Introductionmentioning

confidence: 99%

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

Lei

Ning³

et al. 2016

International Journal of Remote Sensing

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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

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The effect of properties of sediment type on the relationship between suspended sediment concentration and radiance

Cited by 9 publications

References 21 publications

The Impact of Glacial Suspension Color on the Relationship Between Its Properties and Marine Water Spectral Reflectance

The Impact of Glacial Suspension Color on the Relationship Between Its Properties and Marine Water Spectral Reflectance

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

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

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