Comparison of in situ Estimates of Chlorophyll a Obtained with Whatman GF/F and GF/C Glass-Fiber Filters in Mesotrophic to Hypereutrophic Lakes

1988 ◽  
Vol 45 (5) ◽  
pp. 910-914 ◽  
Author(s):  
E. E. Prepas ◽  
M. E. Dunnigan ◽  
A. M. Trimbee
Keyword(s):  
Glass Fiber ◽  
Pore Size ◽  
Chlorophyll A ◽  
Filter Paper ◽  
Euphotic Zone ◽  
Chl A ◽  
Recent Attention ◽  
Zone Water

In response to the recent attention given to picoplankton, many researchers have switched from GF/C to GF/F glass-fiber filters for chlorophyll a (Chl a) analyses; GF/F filters have a smaller pore size and are more expensive than GF/C filters. In this study, Chl a was measured with both Whatman GF/F and GF/C glass-fiber filters on euphotic zone water from 28 mesotrophic to hypereutrophic lakes (Chl a ranged from 2 to 175 μg∙L−1) in central Alberta. The differences between paired Chl a estimates with the two grades of filter paper were remarkably small; there was no evidence that GF/F filters collected more Chl a than GF/C filters (P > 0.2). Hence there is no justification for switching from GF/C to GF/F filters for routine Chl a analyses on productive lakes.

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

scholarly journals Improved MODIS-Aqua Chlorophyll-a Retrievals in the Turbid Semi-Enclosed Ariake Bay, Japan

2018 ◽  
Vol 10 (9) ◽  
pp. 1335 ◽  
Author(s):  
Meng Meng Yang ◽  
Joji Ishizaka ◽  
Joaquim I. Goes ◽  
Helga do R. Gomes ◽  
Elígio de Raús Maúre ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Red Tide ◽  
Atmospheric Correction ◽  
Ariake Bay ◽  
Band Ratio ◽  
Simple Method ◽  
Chl A ◽  
Switching Algorithm ◽  
Economic Significance

The accurate retrieval of chlorophyll-a concentration (Chl-a) from ocean color satellite data is extremely challenging in turbid, optically complex coastal waters. Ariake Bay in Japan is a turbid semi-enclosed bay of great socio-economic significance, but it suffers from serious water quality problems, particularly due to red tide events. Chl-a derived from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on satellite Aqua in Ariake Bay was investigated, and it was determined that the causes of the errors were from inaccurate atmospheric correction and inappropriate in-water algorithms. To improve the accuracy of MODIS remote sensing reflectance (Rrs) in the blue and green bands, a simple method was adopted using in situ Rrs data. This method assumes that the error in MODIS Rrs(547) is small, and MODIS Rrs(412) can be estimated from MODIS Rrs(547) using a linear relation between in situ Rrs(412) and Rrs(547). We also showed that the standard MODIS Chl-a algorithm, OC3M, underestimated Chl-a, which was mostly due to water column turbidity. A new empirical switching algorithm was generated based on the relationship between in situ Chl-a and the blue-to-green band ratio, max(Rrs(443), Rrs(448)/Rrs(547), which was the same as the OC3M algorithm. The criterion of Rrs(667) of 0.005 sr−1 was used to evaluate the extent of turbidity for the switching algorithm. The results showed that the switching algorithm performed better than OC3M, and the root mean square error (RMSE) of estimated Chl-a decreased from 0.414 to 0.326. The RMSE for MODIS Chl-a using the recalculated Rrs and the switching algorithm was 0.287, which was a significant improvement from the RMSE of 0.610, which was obtained using standard MODIS Chl-a. Finally, the accuracy of our method was tested with an independent dataset collected by the local Fisheries Research Institute, and the results revealed that the switching algorithm with the recalculated Rrs reduced the RMSE of MODIS Chl-a from 0.412 of the standard to 0.335.

scholarly journals Bromocarbons in the tropical coastal and open ocean atmosphere during the 2009 Prime Expedition Scientific Cruise (PESC-09)

2014 ◽  
Vol 14 (15) ◽  
pp. 8137-8148 ◽  
Author(s):  
M. S. Mohd Nadzir ◽  
S. M. Phang ◽  
M. R. Abas ◽  
N. Abdul Rahman ◽  
A. Abu Samah ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Statistical Tests ◽  
Open Ocean ◽  
Common Source ◽  
Sampling Point ◽  
Coastal Regions ◽  
Chl A ◽  
June July ◽  
Strait Of Malacca

Abstract. Atmospheric concentrations of very short-lived species (VSLS) bromocarbons, including CHBr3, CH2Br2, CHCl2Br, CHClBr2, and CH2BrCl, were measured in the Strait of Malacca and the South China and Sulu–Sulawesi seas during a two-month research cruise in June–July 2009. The highest bromocarbon concentrations were found in the Strait of Malacca, with smaller enhancements in coastal regions of northern Borneo. CHBr3 was the most abundant bromocarbon, ranging from 5.2 pmol mol−1 in the Strait of Malacca to 0.94 pmol mol−1 over the open ocean. Other bromocarbons showed lower concentrations, in the range of 0.8–1.3 pmol mol−1 for CH2Br2, 0.1–0.5 pmol mol−1 for CHCl2Br, and 0.1–0.4 pmol mol−1 for CHClBr2. There was no significant correlation between bromocarbons and in situ chlorophyll a, but positive correlations with both MODIS and SeaWiFS satellite chlorophyll a. Together, the short-lived bromocarbons contribute an average of 8.9 pmol mol−1 (range 5.2–21.4 pmol mol−1) to tropospheric bromine loading, which is similar to that found in previous studies from global sampling networks (Montzka et al., 2011). Statistical tests showed strong Spearman correlations between brominated compounds, suggesting a common source. Log–log plots of CHBr3/CH2Br2 versus CHBr2Cl/CH2Br2 show that both chemical reactions and dilution into the background atmosphere contribute to the composition of these halocarbons at each sampling point. We have used the correlation to make a crude estimate of the regional emissions of CHBr3 and to derive a value of 32 Gg yr−1 for the Southeast (SE) Asian region (10° N–20° S, 90–150° E). Finally, we note that satellite-derived chlorophyll a (chl a) products do not always agree well with in situ measurements, particularly in coastal regions of high turbidity, meaning that satellite chl a may not always be a good proxy for marine productivity.

scholarly journals Bromocarbons in the tropical coastal and open ocean atmosphere during the Prime Expedition Scientific Cruise 2009 (PESC 09)

2014 ◽  
Vol 14 (1) ◽  
pp. 953-984 ◽  
Author(s):  
M. S. Mohd Nadzir ◽  
S. M. Phang ◽  
M. R. Abas ◽  
N. Abdul Rahman ◽  
A. Abu Samah ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Statistical Tests ◽  
Open Ocean ◽  
Common Source ◽  
Sampling Point ◽  
The South ◽  
Coastal Regions ◽  
Chl A ◽  
Strait Of Malacca

Abstract. Atmospheric concentrations of very short-lived species (VSLS) bromocarbons, including CHBr3, CH2Br2, CHCl2Br, CHClBr2, CH2BrCl, were measured in the Strait of Malacca and the South China and Sulu-Sulawesi Seas during a two month research cruise in June/July 2009. The highest bromocarbon concentrations were found in the Strait of Malacca, with smaller enhancements in coastal regions of Northern Borneo. CHBr3 was the most abundant bromocarbon, ranging from 5.2 pmol mol−1 in the Strait of Malacca to 0.94 pmol mol−1 over the open ocean. Other bromocarbons showed lower concentrations, in the range of 0.8–1.3 pmol mol−1 for CH2Br2, 0.1–0.5 pmol mol−1 (CHCl2Br) and 0.1–0.4 pmol mol−1 (CHClBr2). There was no significant correlation between bromocarbons and in situ chlorophyll a. Together the short-lived bromocarbons contribute an average of 8.9 pmol mol−1 (range 5.2–21.4 pmol mol−1) to tropospheric bromine load, which is similar to that found in previous studies (Montzka et al., 2011). Statistical tests showed strong Spearman correlations amongst brominated compounds suggesting a common source. Log-log plots of CHBr3/CH2Br2 vs. CHBr2Cl/CH2Br2 show that both chemical reactions and dilution into the background atmosphere contribute to the composition of these halocarbons at each sampling point. We have used the correlation to make a crude estimate of the regional emissions of CHBr3 and derive a value of 63 Gg yr−1 for the South East (S.E.) Asian region (10° N–20° S, 90–150° E). Finally, we note that satellite-derived chlorophyll a (chl a) products do not always agree well with in situ measurements, particularly in coastal regions of high turbidity, meaning that satellite chl a may not always be a good proxy for marine productivity.

scholarly journals ESTIMATING AND MAPPING CHLOROPHYLL-A CONCENTRATION OF PHEWA LAKE OF KASKI DISTRICT USING LANDSAT IMAGERY

2019 ◽  
Vol IV-5/W2 ◽  
pp. 127-132
Author(s):  
N. Wagle ◽  
R. Pote ◽  
R. Shahi ◽  
S. Lamsal ◽  
S. Thapa ◽  
...  
Keyword(s):  
Regression Model ◽  
Chlorophyll A ◽  
Algal Blooms ◽  
Landsat Imagery ◽  
Measurement Data ◽  
Landsat 8 ◽  
Chl A ◽  
Gis Techniques ◽  
The One

Abstract. Water is a major component in the living ecosystem. As water quality is degrading due to human intervention, continuous monitoring is necessary. One of the indicators is Chlorophyll-a (Chl-a) which indicates algal blooms which are often driven by eutrophication phenomena in freshwater. Lakes should be monitored for Chl-a because Chla-a is related to eutrophication phenomena which are an enrichment of water by nutrients salt. When the environment becomes enriched with nutrients the excessive growth can lead to the death of fish. In this study, the Remote Sensing (RS) and Geographic Information System (GIS) techniques were utilized to determine Chl-a concentration of Phewa Lake of Kaski district. We used Landsat 8 satellite imagery for estimation and mapping of the Chl-a concentration. In-situ measurements from different sample points were taken and used to form a regression model for Chl-a and its concentration over the water body was calculated. The preceding year’s (2016) in situ measurement data of Chl-a concentration at a specific location were assessed with the one evaluated from the regression model thus produced for the succeeding year (2017) using Root Mean Square Error (RMSE) technique. As a result, we concluded that the estimation and mapping of Chl-a of a lake in Nepal can be done with the help of RS and GIS techniques.

scholarly journals An Optimal Two Bands Ratio Model to Monitor Chlorophyll-a in Urban Lake Using Landsat 8 Data

2020 ◽  
Vol 143 ◽  
pp. 02003
Author(s):  
Qi Chen ◽  
Mutao Huang ◽  
Kaiyuan Bai ◽  
Xiaojuan Li
Keyword(s):  
Chlorophyll A ◽  
Environmental Issue ◽  
Inland Waters ◽  
Landsat 8 ◽  
Ratio Model ◽  
Landsat 8 Oli ◽  
Band Ratio ◽  
Chl A ◽  
Quality Indexes

Chlorophyll-a (Chl-a) estimation in inland waters is an essential environmental issue. This study aimed to identify a band ratio model for Chl-a simulation using Landsat 8 OLI data and in situ Chl-a measuring in Lake Donghu. The band B1and B2, respectively at the wavelength of 443 nm and 483 nm, in the band ratio model [B1/B2] performed best in Chl-a estimation with the R2 of 0.6215. K-means cluster analysis based on water quality indexes (Chl-a, pH, DO, TN, TP, COD, Turbidity) was conducted to further improve the accuracy of inversion model. The MAPE of the optimal [B1/B2] algorithm has decreased by 4.81% and 39.87% respectively for 17 December 2017 (R2=0.7669, N=42) and 26 March 2018 (R2=0.9156, N=45).

Contribution of Particulate Phosphorus (> 250 μm) to the Total Phosphorus Pool in Lake Water

1984 ◽  
Vol 41 (2) ◽  
pp. 351-363 ◽  
Author(s):  
E. E. Prepas ◽  
J. Vickery
Keyword(s):  
Chlorophyll A ◽  
Total Phosphorus ◽  
Lake Water ◽  
Euphotic Zone ◽  
Particulate Phosphorus ◽  
Lake Productivity ◽  
Chl A ◽  
Relative Contribution ◽  
Residual Variation ◽  
Large Particles

Particulate phosphorus (PP) > 250 μm was concentrated in the euphotic zone of 17 lakes in central Alberta. When the euphotic zone extended below the epilimnion, PP >250 μm was concentrated deep in the euphotic zone. PP > 250 μm was a significant but variable portion of the total phosphorus (TP) pool in individual lakes; thus, samples should be collected from the euphotic zone on several dates to estimate the contribution of PP > 250 μm to the TP pool. As well, the contribution of this fraction varied among lakes: average summer values for the euphotic zone ranged from 3 to 19%. Among lakes, the contribution of large particles to the TP pool decreased proportionally as lake productivity (estimated by chlorophyll a (Chl a)) increased. The relative contribution of PP > 250 μm in summer accounted for a significant portion of the residual variation in the spring TP-summer Chl a relationship but not the summer TP-summer Chl a relationship in the study lakes. These apparently contradictory results can be explained by differences between lakes that mixed intermittently throughout the summer and those that remained permanently thermally stratified during this time.

scholarly journals Comparison of In-Situ Chlorophyll-a Time Series and Sentinel-3 Ocean and Land Color Instrument Data in Slovenian National Waters (Gulf of Trieste, Adriatic Sea)

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1903
Author(s):  
El Khalil Cherif ◽  
Patricija Mozetič ◽  
Janja Francé ◽  
Vesna Flander-Putrle ◽  
Jana Faganeli-Pucer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Adriatic Sea ◽  
Global Ocean ◽  
Learning Approach ◽  
Chl A ◽  
Gulf Of Trieste ◽  
Machine Learning Approach

While satellite remote sensing of ocean color is a viable tool for estimating large-scale patterns of chlorophyll-a (Chl-a) and global ocean primary production, its application in coastal waters is limited by the complex optical properties. An exploratory study was conducted in the Gulf of Trieste (Adriatic Sea) to assess the usefulness of Sentinel-3 satellite data in the Slovenian national waters. OLCI (Ocean and Land Colour Instrument) Chl-a level 2 products (OC4Me and NN) were compared to monthly Chl-a in-situ measurements at fixed sites from 2017 to 2019. In addition, eight other methods for estimating Chl-a concentration based on reflectance in different spectral bands were tested (OC3M, OC4E, MedOC4, ADOC4, AD4, 3B-OLCI, 2B-OLCI and G2B). For some of these methods, calibration was performed on in-situ data to achieve a better agreement. Finally, L1-regularized regression and random forest were trained on the available dataset to test the capabilities of the machine learning approach. The results show rather poor performance of the two originally available products. The same is true for the other eight methods and the fits to the measured values also show only marginal improvement. The best results are obtained with the blue-green methods (OC3, OC4 and AD4), especially the AD4SI (a designated fit of AD4) with R = 0.56 and RMSE = 0.4 mg/m³, while the near infrared (NIR) methods show underwhelming performance. The machine learning approach can only explain 30% of the variability and the RMSE is of the same order as for the blue-green methods. We conclude that due to the low Chl-a concentration and the moderate turbidity of the seawater, the reflectance provided by the Sentinel-3 OLCI spectrometer carries little information about Chl-a in the Slovenian national waters within the Gulf of Trieste and is therefore of limited use for our purposes. This requires that we continue to improve satellite products for use in those marine waters that have not yet proven suitable. In this way, satellite data could be effectively integrated into a comprehensive network that would allow a reliable assessment of ecological status, taking into account environmental regulations.

scholarly journals ESTIMATION OF CHLOROPHYLL-A CONCENTRATION IN SAMPALOC LAKE USING UAS MULTISPECTRAL REMOTE SENSING AND REGRESSION ANALYSIS

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 297-303
Author(s):  
R. M. G. Maravilla ◽  
J. P. Quinalayo ◽  
A. C. Blanco ◽  
C. G. Candido ◽  
E. V. Gubatanga ◽  
...  
Keyword(s):  
Water Quality ◽  
Regression Analysis ◽  
Chlorophyll A ◽  
Linear Regression Analysis ◽  
Quality Data ◽  
Water Quality Data ◽  
Microalgal Biomass ◽  
Chlorophyll A Concentration ◽  
Chl A

Abstract. Sampaloc Lake is providing livelihood for the residents through aquaculture. An increase in the quantity of fish pens inside the lake threatens its water quality condition. One parameter being monitored is microalgal biomass by measuring Chlorophyll-a concentration. This study aims to generate a chlorophyll-a concentration model for easier monitoring of the lake. In-situ water quality data were collected using chl-a data logger and water quality meter at 357 and 12 locations, respectively. Using Parrot Sequoia+ Multispectral Camera, 1496 of 2148 images were acquired and calibrated, producing 18x18cm resolution Green (G), Red(R), Red Edge (RE) and Near Infrared (NIR) reflectance images. NIR was used to mask out non-water features, and to correct sun glint. The in-situ data and the pixel values extracted were used for Simple Linear Regression Analysis. A model with 5 variables – R/NIR, RE2, NIR2, R/NIR2, and NIR/RE2, was generated, yielding an R2 of 0.586 and RMSE of 0.958 μg/l. A chlorophyll-a concentration map was produced, showing that chl-a is higher where fish pens are located and lowers as it moves away from the pens. Although there are apparent fish pens on certain areas of the lake, it still yields low chlorophyll-a because of little amount of residential area or establishments adjacent to it. Also, not all fish pens have the same concentration of Chlorophyll-a due to inconsistent population per fish pen. The center of the lake has low chlorophyll-a as it is far from human activities. The only outlet, Sabang Creek, also indicates high concentration of Chlorophyll-a.

scholarly journals Modelling Ocean Colour Derived Chlorophyll-a

2017 ◽  
Author(s):  
Stephanie Dutkiewicz ◽  
Anna E. Hickman ◽  
Oliver Jahn
Keyword(s):  
Chlorophyll A ◽  
Real World ◽  
World Ocean ◽  
Model Output ◽  
Ocean Colour ◽  
The Real ◽  
Chl A ◽  
Absolute Bias ◽  
Water Constituents

Abstract. This article provides a proof-of-concept for using a biogeochemical/ecosystem/optical model with radiative transfer component as a laboratory to explore aspects of ocean colour. We focus here on the satellite ocean colour Chlorophyll-a (Chl-a) product provided by the often-used blue/green reflectance ratio algorithm. The model produces output that can be compared directly to the real world ocean colour remotely sensed reflectance. This model output can then be used to produce an ocean colour satellite-like Chl-a product using an algorithm linking the blue versus green reflectance similar to that used for the real world. Given that the model includes complete knowledge of the (model) water constituents, optics and reflectance, we can explore uncertainties and their causes in this proxy for Chl-a (called derived Chl-a in this paper). We compare the derived Chl-a the actual model Chl-a field. In the model we find that the mean absolute bias due to the algorithm is 22 % between derived and actual Chl-a. The real world algorithm is found using concurrent in situ measurement of Chl-a and radiometry. We ask whether increased in situ measurements to train the algorithm would improve the algorithm, and find a mixed result. There is a global overall improvement, but at the expense of some regions, especially in lower latitudes where the biases increase. We do find that regional specific algorithms provide a significant improvement. However, in the model, we find that no matter how the algorithm coefficients are found there can be a temporal mismatch between the derived Chl-a and the actual Chl-a. These mismatches stem from temporal decoupling between Chl-a and other optically important water constituents (such as coloured dissolved organic matter and detrital matter). The degree of decoupling differs regionally and over time. For example, in many highly seasonal regions, the timing of initiation and peak of the spring bloom in the derived Chl-a lags the actual Chl-a by days and sometimes weeks. This result indicate care should also be taken when studying phenology through satellite derived products of Chl-a. This study also re-emphasises that ocean colour derived Chl-a is not the same as the real in situ Chl-a. In fact the model derived Chl-a compares better to real world Chl-a than the model actual Chl-a. Modellers should keep this is mind when evaluating model output with ocean colour Chl-a and in particular when assimilating this product. Our study spans several disciplines: Our goal is to illustrate the use of numerical laboratory that a) helps users of ocean colour, particularly modellers, gain further understanding of the products they use; and b) the ocean colour community could use to explore other ocean colour products, their biases and uncertainties, as well as to aid in future algorithm development.

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