Analyzing the effects of particle size on remotely sensed spectra: a study on optical properties and spectral similarity scale of suspended particulate matters in water

Lu, Yingcheng; Zheng, Guang; Tian, Qingjiu; Lyu, Chunguang; Sun, Shaojie

doi:10.1364/ao.52.000879

Cited by 10 publications

(3 citation statements)

References 39 publications

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“…This suggests that the suspended sediment particle properties such as size distributions, compositions, and textures might be different for the Yellow River and Yangtze River. In fact, a recent study from Lake Taihu shows that particle size distribution plays an important role for the measured r wN (l) spectra (Lu et al 2013). Zhang et al (2008) also reported that the compositions of river sediments from Yellow River and Yangtze River are different.…”

Section: Methodsmentioning

confidence: 99%

Ocean reflectance spectra at the red, near‐infrared, and shortwave infrared from highly turbid waters: A study in the Bohai Sea, Yellow Sea, and East China Sea

Shi

Wang

2014

Limnology & Oceanography

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Normalized water-leaving radiance spectra nL w (l) at the red, near-infrared (NIR), and shortwave infrared (SWIR) are quantified and characterized in highly turbid waters of the western Pacific using 3 yr (2009)(2010)(2011) observations from the Moderate Resolution Imaging Spectroradiometer on the satellite Aqua. nL w (645; red), nL w (859; NIR), and nL w (1240; SWIR) were higher in the coastal region and river estuaries, with SWIR nL w (1240) reaching up to , 0.2 mW cm 22 mm 21 sr 21 in Hangzhou Bay during winter. The NIR ocean-reflectance spectral shape represented by the ratio of the normalized water-leaving reflectance r wN (l) at the two NIR bands r wN (748) : r wN (869) is highly dynamic and region-dependent. The NIR spectral feature associated with the sediment source from the Yellow River and Ancient Yellow River is noticeably different from that of the Yangtze River. There are non-negligible SWIR nL w (1240) contributions for waters with the NIR nL w (859) . , 2.5 mW cm 22 mm 21 sr 21 . Estimation of the NIR ocean reflectance with iterative approaches might only be accurate for turbid waters with nL w (859) , , 1.5 mW cm 22 mm 21 sr 21 . Thus, the SWIR atmospherics correction algorithm for satellite ocean-color data processing is indispensable to derive accurate nL w (l) for highly turbid waters. Current existing satellite algorithms for chlorophyll a, diffuse attenuation coefficient at the wavelength of 490 nm (K d (490)), total suspended matter, and inherent optical properties (IOPs) using nL w (l) at the red band for coastal waters are limited and can only be applied to turbid waters with nL w (859) , , 1.5 mW cm 22 mm 21 sr 21 . Thus, the NIR nL w (l) measurements are required to characterize water properties for highly turbid waters. Based on the fact that pure water absorption is significantly larger than other absorption components in the NIR wavelengths, we show that it is feasible to analytically derive accurate IOP data for turbid waters with combined satellite-measured visible-NIR nL w (l) spectra data.

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Section: Methodsmentioning

confidence: 99%

Ocean reflectance spectra at the red, near‐infrared, and shortwave infrared from highly turbid waters: A study in the Bohai Sea, Yellow Sea, and East China Sea

Shi

Wang

2014

Limnology & Oceanography

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“…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.…”

Section: Discussionmentioning

confidence: 99%

Algorithm to Calculate Suspended Sediment Concentration Using Landsat 8 Imagery

Hernández-Cruz

Vásquez-Ortiz²,

Miquel³

et al. 2019

Appl. Ecol. Env. Res.

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Satellite imagery was used to assess the suspended sediment concentration (SSC) of the plume at the Grijalva river mouth, Gulf of Mexico. The SSC was calculated with the algorithm of Topliss. The algorithm was created for Lansdast 5 MSS. For this study it was validated the results for Landsat 7 ETM+ and Landsat 8 OLI. The depth of the ocean floor was measured using the speedtech sounder. The spectral response was registered with a GER-1500 spectroradiometer (with a measurement range of 296.71-1092.08 nm). The spectral responses were used to determine the size of the suspended particles and to perform the atmospheric correction. Spectral responses indicate that silt size particles predominate near the river mouth, whereas clay overtakes further towards the open sea. The trend in the river when water meets seawater, means an increase of suspended sediment towards the surface due to the lower density of fresh water, and the amount of sediment rises in the open sea. The correlation between the measurements in situ and SSC values produced by the algorithm demonstrates that the operation is suitable to estimate suspended sediments.

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“…Turbidity in bentonite clay and black cotton soil was linearly related to reflectance, whereas the turbidity of kaoline and grey soil is curvilinearly related to reflectance (Bhargava and Mariam 1990). A number of studies have investigated the effect of particle size on the remote sensing of sediment concentration (Lu et al 2013;Tilstone et al 2012;Astoreca et al 2012;Bowers et al 2009), but few works have been done on the effect of the distribution of particle composition along a river. Sediment variability is significant in river waters because of the high sediment variety originating from the runoff from vast agricultural areas.…”

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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Analyzing the effects of particle size on remotely sensed spectra: a study on optical properties and spectral similarity scale of suspended particulate matters in water

Cited by 10 publications

References 39 publications

Ocean reflectance spectra at the red, near‐infrared, and shortwave infrared from highly turbid waters: A study in the Bohai Sea, Yellow Sea, and East China Sea

Ocean reflectance spectra at the red, near‐infrared, and shortwave infrared from highly turbid waters: A study in the Bohai Sea, Yellow Sea, and East China Sea

Algorithm to Calculate Suspended Sediment Concentration Using Landsat 8 Imagery

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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