Determination of absorption coefficients for chlorophyll a, phycocyanin, mineral matter and CDOM from three central Indiana reservoirs

Nguy-Robertson, Anthony L.; Li, Lin; Tedesco, Lenore P.; Wilson, Jeffrey S.; Soyeux, E.

doi:10.1016/j.jglr.2013.04.004

Cited by 9 publications

(5 citation statements)

References 47 publications

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“…3). The negative correlations between a ph * (k) and Chla were also ascertained in previous studies [77,82,87,90,91]. Increased Chla can be associated with an increase in intracellular pigment concentration rather than cell number, which causes an absorption efficiency loss in the package effect [92].…”

Section: Absorption Properties Of Cyanobacterial Pigmentssupporting

confidence: 71%

“…Compared with the absorption blue peak, the red peak is narrower and smaller, and this peak is primarily attributed to Chla [75,80,81]. In addition, there is a less obvious peak at 620 nm, which is induced by PC [76,82,83].…”

Section: Absorption Properties Of Cyanobacterial Pigmentsmentioning

confidence: 99%

“…was much weaker than a chla * (620), which may be attributed to considerable noise when moving away from the PC absorption peak at approximately 620 nm [82]. The primary reason for the change in PC-specific absorption coefficients needs to be further elaborated through the controlled in situ and laboratory studies on the bio-optical properties of the cyanobacterial community.…”

Section: Absorption Properties Of Cyanobacterial Pigmentsmentioning

confidence: 99%

See 2 more Smart Citations

Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges

et al. 2019

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Section: Absorption Properties Of Cyanobacterial Pigmentssupporting

confidence: 71%

Section: Absorption Properties Of Cyanobacterial Pigmentsmentioning

confidence: 99%

Section: Absorption Properties Of Cyanobacterial Pigmentsmentioning

confidence: 99%

See 1 more Smart Citation

Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges

et al. 2019

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“…The specific light absorption coefficient (or phytoplankton absorption coefficient) may be estimated from data regarding the in situ pigment distribution characteristics of the given algal species and their in vivo weight-specific absorption properties. The latter may be obtained through spectrophotometric measurement of the absorption characteristics of pure pigment standards, followed by adjusting their absorption maxima to correspond to in vivo conditions [ 135 , 136 , 137 , 138 ].…”

Section: Resultsmentioning

confidence: 99%

Comparative Analysis of Laboratory-Based and Spectroscopic Methods Used to Estimate the Algal Density of Chlorella vulgaris

Fekete,

Sebők,

Klátyik

et al. 2024

Microorganisms

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Chlorella vulgaris is of great importance in numerous exploratory or industrial applications (e.g., medicals, food, and feed additives). Rapid quantification of algal biomass is crucial in photobioreactors for the optimization of nutrient management and the estimation of production. The main goal of this study is to provide a simple, rapid, and not-resource-intensive estimation method for determining the algal density of C. vulgaris according to the measured parameters using UV–Vis spectrophotometry. Comparative assessment measurements were conducted with seven different methods (e.g., filtration, evaporation, chlorophyll a extraction, and detection of optical density and fluorescence) to determine algal biomass. By analyzing the entire spectra of diluted algae samples, optimal wavelengths were determined through a stepwise series of linear regression analyses by a novel correlation scanning method, facilitating accurate parameter estimation. Nonlinear formulas for spectrometry-based estimation processes were derived for each parameter. As a result, a general formula for biomass concentration estimation was developed, with recommendations for suitable measuring devices based on algae concentration levels. New values for magnesium content and the average single-cell weight of C. vulgaris were established, in addition to the development of a rapid, semiautomated cell counting method, improving efficiency and accuracy in algae quantification for cultivation and biotechnology applications.

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“…Water transparency is a significant indication of lake water quality and the ecosystem of waterbodies, and it directly reflects the light transmittance of waterbodies, which is widely employed in water quality monitoring and management [376,377]. The presence of chl-α, TSM, and CDOM collectively affects water clarity..…”

Section: Water Transparency (Secchi Disk Depth (Sdd))mentioning

confidence: 99%

A Review of Remote Sensing Applications for Determining Lake Water Quality

Batina,

Krtalić

2023

Preprint

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Remote sensing methods have the potential to improve lake water quality monitoring and deci-sion-making in water management. This reviews introduces novel findings in the field of opti-cally active water quality parameters using remote sensing. It summarizes existing retrieval methods (analytical, semi-analytical, empirical, semi-empirical, and artificial intelli-gence/machine learning (AI/ML)), examines measurement methods used to determine concen-tration of specific water quality parameters, summarizes satellite systems that enable temporal data for understanding the state of the lake with focus on water quality parameters, and pro-poses enhancements for future research of lake water quality using remote sensing. As part of this review, eight optically active biological and physical water quality parameters were ana-lyzed, including chlorophyll-α (chl-α), transparency (Secchi disk depth (SDD)), colored dis-solved organic matters (CDOM), turbidity (TUR), electrical conductivity (EC), surface salinity (SS), total suspended matter (TSM), and water temperature (WT). The research proposes a shift from point-based data representation to a more reliable raster representation and encourages optimizing grid selection for in situ measurements by combining hydrodynamic model with re-mote sensing methods. This review presents a comprehensive summary of the bands, band combinations, and band equations per sensor for eight optically active water quality parameters as listed in Tables A1-A8. The review’s findings indicate that use of remotely sensed data is an effective method for estimating water quality parameters in lakes, with a significant increase in global utilization. The review highlights potential solutions and limitations to the challenges of remote sensing water quality determination in lakes.

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Determination of absorption coefficients for chlorophyll a, phycocyanin, mineral matter and CDOM from three central Indiana reservoirs

Cited by 9 publications

References 47 publications

Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges

Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges

Comparative Analysis of Laboratory-Based and Spectroscopic Methods Used to Estimate the Algal Density of Chlorella vulgaris

A Review of Remote Sensing Applications for Determining Lake Water Quality

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