Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: A quasi-analytical approach

Mishra, Sachidananda; Mishra, Deepak R.; Lee, Zhongping; Tucker, Craig S.

doi:10.1016/j.rse.2013.02.004

Cited by 120 publications

(100 citation statements)

References 34 publications

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“…The water for all catfish aquacultures in northwest Mississippi comes from wells pumping the Mississippi River Alluvial Aquifer [12]. It is also known that commercial pond culture of channel catfish is practical only in regions with the proper temperature regime-around 25-30°C-for reproduction and rapid growth.…”

Section: Study Sitesmentioning

confidence: 99%

“…For remote sensing techniques, phycocyanin (PC) which is a characteristic photosynthetic pigment in inland BG algae, has been used as a proxy due to its distinct optical characteristic (absorption peak at ~620 nm) [5,[10][11][12]. Initially, cyanobacterial biomass was estimated mostly from chlorophyll-a (chl-a) concentration since it is the primary and dominant photosynthetic pigment in BG algae [13].…”

Section: Introductionmentioning

confidence: 99%

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A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters

Ogashawara

Mishra

Mishra³

et al. 2013

Remote Sensing

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Abstract:We evaluated the accuracy and sensitivity of six previously published reflectance based algorithms to retrieve Phycocyanin (PC) concentration in inland waters. We used field radiometric and pigment data obtained from two study sites located in the United States and Brazil. All the algorithms targeted the PC absorption feature observed in the water reflectance spectra between 600 and 625 nm. We evaluated the influence of chlorophyll-a (chl-a) absorption on the performance of these algorithms in two contrasting environments with very low and very high cyanobacteria content. All algorithms performed well in low to moderate PC concentrations and showed signs of saturation or decreased sensitivity for high PC concentration with a nonlinear trend. MM09 was found to be the most accurate algorithm overall with a RMSE of 15.675%. We also evaluated the use of these algorithms with the simulated spectral bands of two hyperspectral space borne sensors including Hyperion and Compact High-Resolution Imaging Spectrometer (CHRIS) and a hyperspectral air borne sensor, Hyperspectral Infrared Imager (HyspIRI). Results showed that the sensitivity for chl-a of PC retrieval algorithms for Hyperion simulated data were less noticable than using the spectral bands of CHRIS; HyspIRI results show that SC00 could be used for this sensor with low chl-a influence. This review of reflectance OPEN ACCESS Remote Sens. 2013, 5 4775 based algorithms can be used to select the optimal approach in studies involving cyanobacteria monitoring through optical remote sensing techniques.

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Section: Study Sitesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters

Ogashawara

Mishra

Mishra³

et al. 2013

Remote Sensing

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“…The minimum, observed around 625 nm, is related to phycocyanin absorption due to the presence of cyanobacteria [34,64,65]. In Manguaba lagoon, this feature was more prominent than in Mundaú lagoon, which may be explained by two factors: (a) the backscattering caused by the high TSS concentration in Mundaú lagoon may have masked the effect of phycocyanin absorption; and/or (b) the biomass of cyanobacteria was relatively low in Mundaú lagoon.…”

Section: Discussionmentioning

confidence: 95%

Assessment of Chlorophyll-a Remote Sensing Algorithms in a Productive Tropical Estuarine-Lagoon System

et al. 2017

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Remote estimation of chlorophyll-a in turbid and productive estuaries is difficult due to the optical complexity of Case 2 waters. Although recent advances have been obtained with the use of empirical approaches for estimating chlorophyll-a in these environments, the understanding of the relationship between spectral reflectance and chlorophyll-a is based mainly on temperate and subtropical estuarine systems. The potential to apply standard NIR-Red models to productive tropical estuaries remains underexplored. Therefore, the purpose of this study is to evaluate the performance of several approaches based on multispectral data to estimate chlorophyll-a in a productive tropical estuarine-lagoon system, using in situ measurements of remote sensing reflectance, R rs . The possibility of applying algorithms using simulated satellite bands of modern and recent launched sensors was also evaluated. More accurate retrievals of chlorophyll-a (r 2 > 0.80) based on field datasets were found using NIR-Red three-band models. In addition, enhanced chlorophyll-a retrievals were found using the two-band algorithm based on bands of recently launched satellites such as Sentinel-2/MSI and Sentinel-3/OLCI, indicating a promising application of these sensors to remotely estimate chlorophyll-a for coming decades in turbid inland waters. Our findings suggest that empirical models based on optical properties involving water constituents have strong potential to estimate chlorophyll-a using multispectral data from satellite, airborne or handheld sensors in productive tropical estuaries.

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“…In the past decades, many vegetation indices were used to quantify the biological variation of aquatic plants, for example Simple Ratio (SR) is often used to estimate the biological variation, such as cyanobacteria, leucocyan etc. Normalized Difference Vegetation Index (NDVI) is an effective index that can monitor vegetation and ecotope (Ekstran, 1992;MA Ronghua et al, 2005;Sachidananda Mishra, 2013;Wesley J.Moses, 2012;Changchun Huang, 2014). In spite of the same vegetation indices, there also would be discrepancy in quantitative estimation precision, since the band location and width of satellite spectral channel are different.…”

Section: Introductionmentioning

confidence: 99%

Spectral Feature Analysis for Quantitative Estimation of Cyanobacteria Chlorophyll-A

Lin

Zhang

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In recent years, lake eutrophication caused a large of Cyanobacteria bloom which not only brought serious ecological disaster but also restricted the sustainable development of regional economy in our country. Chlorophyll-a is a very important environmental factor to monitor water quality, especially for lake eutrophication. Remote sensed technique has been widely utilized in estimating the concentration of chlorophyll-a by different kind of vegetation indices and monitoring its distribution in lakes, rivers or along coastline. For each vegetation index, its quantitative estimation accuracy for different satellite data might change since there might be a discrepancy of spectral resolution and channel center between different satellites. The purpose of this paper is to analyze the spectral feature of chlorophyll-a with hyperspectral data (totally 651 bands) and use the result to choose the optimal band combination for different satellites. The analysis method developed here in this study could be useful to recognize and monitor cyanobacteria bloom automatically and accurately.In our experiment, the reflectance (from 350nm to1000nm) of wild cyanobacteria in different consistency (from 0 to 1362.11ug/L) and the corresponding chlorophyll-a concentration were measured simultaneously. Two kinds of hyperspectral vegetation indices were applied in this study: simple ratio (SR) and narrow band normalized difference vegetation index (NDVI), both of which consists of any two bands in the entire 651 narrow bands. Then multivariate statistical analysis was used to construct the linear, power and exponential models. After analyzing the correlation between chlorophyll-a and single band reflectance, SR, NDVI respectively, the optimal spectral index for quantitative estimation of cyanobacteria chlorophyll-a, as well as corresponding central wavelength and band width were extracted. Results show that: Under the condition of water disturbance, SR and NDVI are both suitable for quantitative estimation of chlorophyll-a, and more effective than the traditional single band model; the best regression models for SR, NDVI with chlorophyll-a are linear and power, respectively. Under the condition without water disturbance, the single band model works the best. For the SR index, there are two optimal band combinations, which is comprised of infrared (700nm-900nm) and blue-green range (450nm-550nm), infrared and red range (600nm-650nm) respectively, with band width between 45nm to 125nm. For NDVI, the optimal band combination includes the range from 750nm to 900nm and from 700nm to 750nm, with band width less than 30nm.For single band model, band center located between 733nm-935nm, and its width mustn't exceed the interval where band center located in.This study proved, as for SR or NDVI, the centers and widths are crucial factors for quantitative estimating chlorophyll-a. As for remote sensor, proper spectrum channel could not only improve the accuracy of recognizing cyanobacteria bloom, but reduce the redundancy of hyperspectral ...

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Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: A quasi-analytical approach

Cited by 120 publications

References 34 publications

A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters

A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters

Assessment of Chlorophyll-a Remote Sensing Algorithms in a Productive Tropical Estuarine-Lagoon System

Spectral Feature Analysis for Quantitative Estimation of Cyanobacteria Chlorophyll-A

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