Spectral reflectance of suspended solid sediments: a laboratory experiment.

Author(s):  
Martina Carlino ◽  
Silvia Di Francesco

<p>Ocean color remote sensing proved to be a good alternative to traditional methods for total suspended solids concentration (TSS) monitoring purposes: numerous sensors have been developed for ocean color applications and different algorithms to retrieve TSS from remotely sensed data already exist.</p><p>Nevertheless, their application is generally limited by site-specific factors, and presently there is no uniform remote sensing model to estimate TSS.</p><p>The present study is focused in the development, evaluation and validation of different algorithms to estimate total suspended solids concentration based on laboratory reflectance data.</p><p>At this aim, a laboratory experiment was designed to collect the spectral reflectance of water containing fixed suspended particulate matter in terms of its concentration.</p><p>During the experiment, a total of 10 silty clay loam sediment samples were introduced into a tank filled with clear water up to a depth of 22 cm, illuminated by two 45 W lamps focused on center of water surface. After sieving, sediments were weighed so that TSS concentration ranging from 150 up to 2000 mg/L were obtained in the tank, being soil sediments suspension guaranteed by means of a mechanical pump-driven device.</p><p>Optical data were collected few minutes after each sediment introduction, using an Ocean Optics Jaz spectroradiometer mounted on a platform above the tank.</p><p>In accordance with previous studies, collected reflectance spectra of water containing sediments showed that, whatever is sediment concentration in water, reflectance in the red region is larger than that in the NIR region. Furthermore, reflectance spectra generally present two peaks: one between 550 nm and 750 nm, and the other between 750 nm and 850 nm, being the second peak insignificant for samples with very small TSS (e.g., SSC=150 mg/L), due to strong absorption of water.</p><p>After collection, laboratory reflectance spectra were integrated over the bandpass of different sensors’ selected bands, modulated by their relative response functions (RSR).</p><p>The basic principle of using a specific band, or band ratios to estimate a water parameter is to select spectral bands representative of its scattering/absorption features.</p><p>Band selection was achieved testing some previously formulated ocean color algorithms for the estimation of water quality parameters.</p><p>After band selection, linear regression model was applied to estimate the relationship between sensors’ reflectance at these bands and suspended solids concentration.</p><p>Results showed high correlation between selected sensors’ spectral red band and total suspended solids concentration higher than 500 mg/L up to 1360 mg/L, while less accuracy was observed for TSS concentrations higher than 1360 mg/L. Furthermore, the ratio between spectral red and green bands better estimates TSS in waters where total suspended concentration is not higher than 500 mg/L.</p><p> </p>

1991 ◽  
Vol 36 (1) ◽  
pp. 67-72 ◽  
Author(s):  
Evlyn Márcia Leão Moraes Novo ◽  
Carlos Alberto Steffen ◽  
Cláudia Zuccari Fernandes Braga

2019 ◽  
Vol 11 (9) ◽  
pp. 2580 ◽  
Author(s):  
Tainá T. Guimarães ◽  
Maurício R. Veronez ◽  
Emilie C. Koste ◽  
Eniuce M. Souza ◽  
Diego Brum ◽  
...  

The concentration of suspended solids in water is one of the quality parameters that can be recovered using remote sensing data. This paper investigates the data obtained using a sensor coupled to an unmanned aerial vehicle (UAV) in order to estimate the concentration of suspended solids in a lake in southern Brazil based on the relation of spectral images and limnological data. The water samples underwent laboratory analysis to determine the concentration of total suspended solids (TSS). The images obtained using the UAV were orthorectified and georeferenced so that the values referring to the near, green, and blue infrared channels were collected at each sampling point to relate with the laboratory data. The prediction of the TSS concentration was performed using regression analysis and artificial neural networks. The obtained results were important for two main reasons. First, although regression methods have been used in remote sensing applications, they may not be adequate to capture the linear and/or non-linear relationships of interest. Second, results show that the integration of UAV in the mapping of water bodies together with the application of neural networks in the data analysis is a promising approach to predict TSS as well as their temporal and spatial variations.


1994 ◽  
Vol 30 (8) ◽  
pp. 107-116 ◽  
Author(s):  
Kenneth F. Cacossa ◽  
David A. Vaccari

A compressive gravity thickening model (Kos and Adrian, 1974; Kos, 1978) was calibrated from a single batch settling curve. This model was originally formulated in terms of the total suspended solids concentration, C, the dynamic pressure gradient, ∂P/∂z, and the gradient corresponding to the compressive yield strength, ∂σy/∂z. Fitch (1975) demonstrated that the model could be formulated in terms of C and the solids concentration gradient, ∂C/∂z. Utilizing this formulation the model was calibrated with data generated from elementary quantitative analysis of the steady-state conditions attained in continuous thickening experiments (Vaccari, 1984; Vaccari and Uchrin, 1989). In this investigation the model was calibrated from a single batch settling curve. This was done using the down-hill simplex optimization method proposed by Nelder and Mead (1965) in a curve-fitting capacity. The optimization routine adjusted the model coefficients in order to reduce the discrepancy between the simulated results of the model and the corresponding experimental batch settling data. When calibrated by this method the model was found to accurately predict experimental continuous thickening behavior observed for the sludge.


2012 ◽  
Vol 66 (6) ◽  
pp. 1310-1316
Author(s):  
Frederik W. Oudyn ◽  
David J. Lyons ◽  
M. J. Pringle

Many scientific laboratories follow, as standard practice, a relatively short maximum holding time (within 7 days) for the analysis of total suspended solids (TSS) in environmental water samples. In this study we have subsampled from bulk water samples stored at ∼4 °C in the dark, then analysed for TSS at time intervals up to 105 days after collection. The nonsignificant differences in TSS results observed over time demonstrates that storage at ∼4 °C in the dark is an effective method of preserving samples for TSS analysis, far past the 7-day standard practice. Extending the maximum holding time will ease the pressure on sample collectors and laboratory staff who until now have had to determine TSS within an impractically short period.


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