Analysis of within- and between-day chlorophyll-a dynamics in Mantua Superior Lake, with a continuous spectroradiometric measurement

2013 ◽  
Vol 64 (4) ◽  
pp. 303 ◽  
Author(s):  
M. Bresciani ◽  
M. Rossini ◽  
G. Morabito ◽  
E. Matta ◽  
M. Pinardi ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Continuous Monitoring ◽  
Phytoplankton Growth ◽  
Key Factors ◽  
Eutrophic Lakes ◽  
Chl A ◽  
Metabolic Adaptations ◽  
Horizontal Migration ◽  
Radiometric System ◽  
Growth Distribution

Eutrophic lakes display unpredictable patterns of phytoplankton growth, distribution, vertical and horizontal migration, likely depending on environmental conditions. Monitoring chlorophyll-a (Chl-a) concentration provides reliable information on the dynamics of primary producers if monitoring is conducted frequently. We present a practical approach that allows continuous monitoring of Chl-a concentration by using a radiometric system that measures optical spectral properties of water. We tested this method in a shallow, nutrient-rich lake in northern Italy, the Mantua Superior Lake, where the radiometric system collected data all throughout the day (i.e. every 5 min) for ~30 days. Here, specifically developed algorithms were used to convert water reflectance to Chl-a concentration. The best performing algorithm (R2 = 0.863) was applied to a larger dataset collected in September 2011. We characterised intra- and inter-daily Chl-a concentration dynamics and observed a high variability; during a single day, Chl-a concentration varied from 20 to 130 mg m–3. Values of Chl-a concentration were correlated with meteo-climatic parameters, showing that solar radiance and wind speed are key factors regulating the daily phytoplankton growth and dynamics. Such patterns are usually determined by vertical migration of different phytoplankton species within the water column, as well as by metabolic adaptations to changes in light conditions.

Comparison of Chlorophyll a Extractions with Ethanol and Dimethyl Sulfoxide/Acetone, and a Concern about Spectrophotometric Phaeopigment Correction

1992 ◽  
Vol 49 (11) ◽  
pp. 2331-2336 ◽  
Author(s):  
D. J. Webb ◽  
B. K. Burnison ◽  
A. M. Trimbee ◽  
E. E. Prepas
Keyword(s):  
High Pressure ◽  
Dimethyl Sulfoxide ◽  
Chlorophyll A ◽  
Green Algae ◽  
Natural Populations ◽  
Eutrophic Lakes ◽  
North Central ◽  
Chl A ◽  
Blue Green Algae ◽  
Hplc Analyses

Chlorophyll a (Chl a) in water samples from three mesotrophic to eutrophic lakes in north-central Alberta was extracted with one of three solvents (95% ethanol, 90% ethanol, or a 2:3 mixture of dimethyl sulfoxide and 90% acetone (DMSO/acetone)) and analyzed by two techniques (spectrophotometry and high pressure liquid chromatography (HPLC). The dominant phytoplankton were blue-green algae and diatoms. Total Chl a concentrations (i.e. no correction for phaeopigments (Pha)) were not significantly different among solvents (P > 0.5). Total Chl a concentrations from spectrophotometric analyses were significantly higher than those from HPLC analyses (4.2 ± 0.88 and 2.6 ± 0.50 μg∙L−1 respectively, P < 0.05). Pha concentrations derived by spectrophotometry were 64 times higher than those derived by HPLC (1.7 ± 0.52 and 0.025 ± 0.01 μg∙L−1 respectively, P < 0.005). Thus, spectrophotometry appears to dramatically overestimate Pha concentrations and may overestimate total Chl a (i.e. no correction for Pha). Therefore, ethanol and DMSO/acetone are equally suitable for Chl a extraction from natural populations dominated by blue-green algae and/or diatoms, but if information on Pha and/or accessory pigments is required, HPLC analyses are the appropriate route rather than spectrophotometry.

Survival of Microcystis aeruginosa and Scenedesmus obliquus under dark anaerobic conditions

2007 ◽  
Vol 58 (7) ◽  
pp. 634 ◽  
Author(s):  
X. L. Shi ◽  
F. X. Kong ◽  
Y. Yu ◽  
Z. Yang
Keyword(s):  
Chlorophyll A ◽  
Microcystis Aeruginosa ◽  
Metabolic Activity ◽  
Scenedesmus Obliquus ◽  
Anaerobic Conditions ◽  
Eutrophic Lakes ◽  
Cell Metabolic Activity ◽  
Chl A ◽  
Cell Concentrations ◽  
Fda Hydrolysis

The cyanobacterium Microcystis aeruginosa and the green alga Scenedesmus obliquus were incubated individually and together in the dark and under anaerobic conditions created by adding the reducing agent cysteine. Flow cytometry was used to monitor cell concentrations, fluorescence of chlorophyll-a (chl-a), and cell metabolic activity measured with an esterase-sensitive probe to detect fluorescein diacetate (FDA) hydrolysis of the two species. M. aeruginosa showed a slight increase in cell metabolic activity, no conspicuous death of cells, and absence of decay of chlorophyll-a fluorescence in individual and competition cases under dark anaerobic conditions. Cell metabolic activity and fluorescence of S. obliquus, on the contrary, decreased sharply, and cell concentrations fluctuated markedly with time in the unialgal cultures, but showed only a slight decline in the mixed cultures. M. aeruginosa appeared to be more tolerant to dark anaerobic conditions than S. obliquus, which may arise in eutrophic lakes beneath thick surface scums in the water column, or in the bottom sediments. Tolerance of these conditions may be important to the dominance of M. aeruginosa in eutrophic lakes.

scholarly journals Spatio-Temporal Variability of Chlorophyll-A and Environmental Variables in the Panama Bight

2020 ◽  
Vol 12 (13) ◽  
pp. 2150
Author(s):  
Andrea Corredor-Acosta ◽  
Náyade Cortés-Chong ◽  
Alberto Acosta ◽  
Matias Pizarro-Koch ◽  
Andrés Vargas ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Temporal Variability ◽  
Phytoplankton Growth ◽  
Sea Surface ◽  
Chl A ◽  
Physical Variables ◽  
Spatio Temporal

The analysis of synoptic satellite data of total chlorophyll-a (Chl-a) and the environmental drivers that influence nutrient and light availability for phytoplankton growth allows us to understand the spatio-temporal variability of phytoplankton biomass. In the Panama Bight Tropical region (PB; 1–9°N, 79–84°W), the spatial distribution of Chl-a is mostly related to the seasonal wind patterns and the intensity of localized upwelling centers. However, the association between the Chl-a and different physical variables and nutrient availability is still not fully assessed. In this study, we evaluate the relationship between the Chl-a and multiple physical (wind, Ekman pumping, geostrophic circulation, mixed layer depth, sea level anomalies, river discharges, sea surface temperature, and photosynthetically available radiation) and chemical (nutrients) drivers in order to explain the spatio-temporal Chl-a variability in the PB. We used satellite data of Chl-a and physical variables, and a re-analysis of a biogeochemical product for nutrients (2002–2016). Our results show that at the regional scale, the Chl-a varies seasonally in response to the wind forcing and sea surface temperature. However, in the coastal areas (mainly Gulf of Panama and off central-southern Colombia), the maximum non-seasonal Chl-a values are found in association with the availability of nutrients by river discharges, localized upwelling centers and the geostrophic circulation field. From this study, we infer that the interplay among these physical-chemical drivers is crucial for supporting the phytoplankton growth and the high biodiversity of the PB region.

scholarly journals Simulation and Assessment of the Capabilities of Orbita Hyperspectral (OHS) Imagery for Remotely Monitoring Chlorophyll-a in Eutrophic Plateau Lakes

2021 ◽  
Vol 13 (14) ◽  
pp. 2821
Author(s):  
Runfei Zhang ◽  
Zhubin Zheng ◽  
Ge Liu ◽  
Chenggong Du ◽  
Chao Du ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll A ◽  
Spatial Resolution ◽  
Atmospheric Correction ◽  
Dianchi Lake ◽  
Inland Waters ◽  
Eutrophic Lakes ◽  
Correction Model ◽  
Chl A

The chlorophyll-a (Chl-a) concentration of eutrophic lakes fluctuates significantly due to the disturbance of wind and anthropogenic activities on the water body. Consequently, estimation of the Chl-a concentration has become an immense challenge. Due to urgent demand and rapid development in high-resolution earth observation systems, it has become crucial to assess hyperspectral satellite imagery capabilities on inland water monitoring. The Orbita hyperspectral (OHS) satellite is the latest hyperspectral sensor with both high spectral and spatial resolution (2.5 nm and 10 m, respectively), which could provide great potential for remotely estimating the concentration of Chl-a for inland waters. However, there are still some deficiencies that are mainly manifested in the Chl-a concentration remote sensing retrieval model assessment and accuracy validation, as well as signal-to-noise ratio (SNR) estimation of OHS imagery for inland waters. Therefore, the radiometric performance of OHS imagery for water quality monitoring is evaluated in this study by comparing different atmospheric correction models and the SNR with several remote sensing images. Several crucial findings can be drawn: (1) the three-band model ((1/B15-1/B17)B19) developed by OHS imagery is most suitable for estimating the Chl-a concentration in Dianchi Lake, with the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE) of 15.55 µg/L and 16.31%, respectively; (2) the applicability of the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model for OHS imagery in a eutrophic plateau lake (Dianchi Lake) was better than the 6S (Second Simulation of Satellite Signal in the Solar Spectrum) model, and QUAC (Quick Atmospheric Correction) model, as well as the dark pixel method; (3) the SNR of the OHS imagery was similar to that of Hyperion imagery and was significantly higher than SNR of the HSI imagery; (4) the spatial resolution showed slight influence on the SNR of the OHS imagery. The results show that OHS imagery could be applied to remote sensing retrieval of Chl-a in eutrophic plateau lakes and presents a new tool for dynamic hyperspectral monitoring of water quality.

scholarly journals The Use of Sentinel-2 for Chlorophyll-a Spatial Dynamics Assessment: A Comparative Study on Different Lakes in Northern Germany

2021 ◽  
Vol 13 (8) ◽  
pp. 1542
Author(s):  
Igor Ogashawara ◽  
Christine Kiel ◽  
Andreas Jechow ◽  
Katrin Kohnert ◽  
Thomas Ruhtz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Spectral Band ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
Spatial Dynamics ◽  
Eutrophic Lakes ◽  
Chl A ◽  
Spatial Coverage ◽  
Sentinel 2

Eutrophication of inland waters is an environmental issue that is becoming more common with climatic variability. Monitoring of this aquatic problem is commonly based on the chlorophyll-a concentration monitored by routine sampling with limited temporal and spatial coverage. Remote sensing data can be used to improve monitoring, especially after the launch of the MultiSpectral Instrument (MSI) on Sentinel-2. In this study, we compared the estimation of chlorophyll-a (chl-a) from different bio-optical algorithms using hyperspectral proximal remote sensing measurements, from simulated MSI responses and from an MSI image. For the satellite image, we also compare different atmospheric corrections routines before the comparison of different bio-optical algorithms. We used in situ data collected in 2019 from 97 sampling points across 19 different lakes. The atmospheric correction assessment showed that the performances of the routines varied for each spectral band. Therefore, we selected C2X, which performed best for bands 4 (root mean square error—RMSE = 0.003), 5 (RMSE = 0.004) and 6 (RMSE = 0.002), which are usually used for the estimation of chl-a. Considering all samples from the 19 lakes, the best performing chl-a algorithm and calibration achieved a RMSE of 16.97 mg/m3. When we consider only one lake chain composed of meso-to-eutrophic lakes, the performance improved (RMSE: 10.97 mg/m3). This shows that for the studied meso-to-eutrophic waters, we can reliably estimate chl-a concentration, whereas for oligotrophic waters, further research is needed. The assessment of chl-a from space allows us to assess spatial dynamics of the environment, which can be important for the management of water resources. However, to have an accurate product, similar optical water types are important for the overall performance of the bio-optical algorithm.

scholarly journals Spatial Variability and Detection Levels for Chlorophyll-A Estimates in High Latitude Lakes Using Landsat Imagery

2020 ◽  
Author(s):  
Filipe Lisboa ◽  
Vanda Brotas ◽  
Filipe Duarte Santos ◽  
Sakari Kuikka ◽  
Laura Kaikkonen ◽  
...  
Keyword(s):  
Time Series ◽  
Chlorophyll A ◽  
Spatial Resolution ◽  
High Latitude ◽  
Trophic State ◽  
Linear Models ◽  
Eutrophic Lakes ◽  
Algae Biomass ◽  
Chl A ◽  
Long Time

Monitoring lakes in high-latitude areas can provide a better understanding of freshwater systems sensitivity and accrete knowledge on climate change impacts. Phytoplankton are sensitive to various conditions: warmer temperatures, earlier ice-melt and changing nutrient sources. Satellite imagery can monitor algae biomass over large areas. The detection of chlorophyll a (chl-a) concentrations in small lakes is hindered by the low spatial resolution of conventional ocean colour satellites. The short time-series of the newest generation of space-borne sensors (e.g. Sentinel-2) is a bottleneck for assessing long-term trends. Although previous studies have evaluated the use of high-resolution sensors for assessing lakes' chl-a, it is still unclear how the spatial and temporal variability of chl-a concentration affect the performance of satellite estimates. We discuss the suitability of Landsat (LT) 30-m resolution imagery to assess lakes' chl-a concentrations under varying trophic conditions, across extensive high-latitude areas in Finland. We use in situ data obtained from field campaigns in 19 lakes and generate remote sensing estimates of chl-a, taking advantage of the long-time span of the LT 5 and 7 archives, from 1984 to 2017. Our results show that linear models based on LT data can explain approximately 50 % of the chl-a interannual variability. However, we demonstrate that the accuracy of the estimates is dependent on the lake's trophic state, with models performing in average twice as better in lakes with higher chl-a concentration (&gt; 20 &micro;g/l) in comparison with less eutrophic lakes. Finally, we demonstrate that linear models based on LT data can achieve high accuracy (R2 = 0.9; p-value &lt; 0.05) in determining lakes' annual mean chl-a concentration, allowing the mapping of the trophic state of lakes across large regions. Given the long time-series and high spatial resolution, LT-based estimates of chl-a provide a tool for assessing the impacts of environmental change.

scholarly journals The Effect of Mineral Sediments on Satellite Chlorophyll-a Retrievals from Line-Height Algorithms Using Red and Near-Infrared Bands

2019 ◽  
Vol 11 (19) ◽  
pp. 2306 ◽  
Author(s):  
Chuiqing Zeng ◽  
Caren Binding
Keyword(s):  
Chlorophyll A ◽  
False Positive ◽  
Near Infrared ◽  
Eutrophic Lakes ◽  
Chl A ◽  
Inherent Optical Properties ◽  
Turbid Waters ◽  
Novel Approach ◽  
On Line ◽  
The Impact

Red and near-infrared line-height algorithms such as the maximum chlorophyll index (MCI) are often considered optimal for remote sensing of chlorophyll-a (Chl-a) in turbid eutrophic waters, under the assumption of minimal influence from mineral sediments. This study investigated the impact of mineral turbidity on line-height algorithms using MCI as a primary example. Inherent optical properties from two turbid eutrophic lakes were used to simulate reflectance spectra. The simulated results: (1) confirmed a non-linear relationship between Chl-a and MCI; (2) suggested optimal use of the MCI at Chl-a < ~100 mg/m3 and saturation of the index at Chl-a ~300 mg/m3; (3) suggested significant variability in the MCI:Chl-a relationship due to mineral scattering, resulting in an RMSE in predicted Chl-a of ~23 mg/m3; and (4) revealed elevated Chl a retrievals and potential false positive algal bloom reports for sediment concentrations > 20 g/m3. A novel approach combining both MCI and its baseline slope, MCIslope reduced the RMSE to ~5 mg/m3. A quality flag based on MCIslope was proposed to mask erroneously high Chl-a retrievals and reduce the risk of false positive bloom reports in highly turbid waters. Observations suggest the approach may be valuable for all line-height-based Chl-a algorithms.

scholarly journals Spatiotemporal Variability of Surface Phytoplankton Carbon and Carbon-to-Chlorophyll a Ratio in the South China Sea Based on Satellite Data

2020 ◽  
Vol 13 (1) ◽  
pp. 30
Author(s):  
Wenlong Xu ◽  
Guifen Wang ◽  
Long Jiang ◽  
Xuhua Cheng ◽  
Wen Zhou ◽  
...  
Keyword(s):  
South China Sea ◽  
Chlorophyll A ◽  
South China ◽  
Satellite Data ◽  
Phytoplankton Biomass ◽  
The South China Sea ◽  
Spatiotemporal Variability ◽  
The South ◽  
Chl A ◽  
China Sea

The spatiotemporal variability of phytoplankton biomass has been widely studied because of its importance in biogeochemical cycles. Chlorophyll a (Chl-a)—an essential pigment present in photoautotrophic organisms—is widely used as an indicator for oceanic phytoplankton biomass because it could be easily measured with calibrated optical sensors. However, the intracellular Chl-a content varies with light, nutrient levels, and temperature and could misrepresent phytoplankton biomass. In this study, we estimated the concentration of phytoplankton carbon—a more suitable indicator for phytoplankton biomass—using a regionally adjusted bio-optical algorithm with satellite data in the South China Sea (SCS). Phytoplankton carbon and the carbon-to-Chl-a ratio (θ) exhibited considerable variability spatially and seasonally. Generally, phytoplankton carbon in the northern SCS was higher than that in the western and central parts. The regional monthly mean phytoplankton carbon in the northern SCS showed a prominent peak during December and January. A similar pattern was shown in the central part of SCS, but its peak was weaker. Besides the winter peak, the western part of SCS had a secondary maximum of phytoplankton carbon during summer. θ exhibited significant seasonal variability in the northern SCS, but a relatively weak seasonal change in the western and central parts. θ had a peak in September and a trough in January in the northern and central parts of SCS, whereas in the western SCS the minimum and maximum θ was found in August and during October–April of the following year, respectively. Overall, θ ranged from 26.06 to 123.99 in the SCS, which implies that the carbon content could vary up to four times given a specific Chl-a value. The variations in θ were found to be related to changing phytoplankton community composition, as well as dynamic phytoplankton physiological activities in response to environmental influences; which also exhibit much spatial differences in the SCS. Our results imply that the spatiotemporal variability of θ should be considered, rather than simply used a single value when converting Chl-a to phytoplankton carbon biomass in the SCS, especially, when verifying the simulation results of biogeochemical models.

scholarly journals Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 664
Author(s):  
Yun Xue ◽  
Lei Zhu ◽  
Bin Zou ◽  
Yi-min Wen ◽  
Yue-hong Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Chlorophyll A ◽  
Language Processing ◽  
Water Bodies ◽  
Inversion Effect ◽  
Least Squares Regression ◽  
Chlorophyll A Concentration ◽  
Chl A

For Case-II water bodies with relatively complex water qualities, it is challenging to establish a chlorophyll-a concentration (Chl-a concentration) inversion model with strong applicability and high accuracy. Convolutional Neural Network (CNN) shows excellent performance in image target recognition and natural language processing. However, there little research exists on the inversion of Chl-a concentration in water using convolutional neural networks. Taking China’s Dongting Lake as an example, 90 water samples and their spectra were collected in this study. Using eight combinations as independent variables and Chl-a concentration as the dependent variable, a CNN model was constructed to invert Chl-a concentration. The results showed that: (1) The CNN model of the original spectrum has a worse inversion effect than the CNN model of the preprocessed spectrum. The determination coefficient (RP2) of the predicted sample is increased from 0.79 to 0.88, and the root mean square error (RMSEP) of the predicted sample is reduced from 0.61 to 0.49, indicating that preprocessing can significantly improve the inversion effect of the model.; (2) among the combined models, the CNN model with Baseline1_SC (strong correlation factor of 500–750 nm baseline) has the best effect, with RP2 reaching 0.90 and RMSEP only 0.45. The average inversion effect of the eight CNN models is better. The average RP2 reaches 0.86 and the RMSEP is only 0.52, indicating the feasibility of applying CNN to Chl-a concentration inversion modeling; (3) the performance of the CNN model (Baseline1_SC (RP2 = 0.90, RMSEP = 0.45)) was far better than the traditional model of the same combination, i.e., the linear regression model (RP2 = 0.61, RMSEP = 0.72) and partial least squares regression model (Baseline1_SC (RP2 = 0.58. RMSEP = 0.95)), indicating the superiority of the convolutional neural network inversion modeling of water body Chl-a concentration.

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