This study aimed to develop an empirical model to predict the spatial distribution of Aphanizomenon using the Ridiyagama reservoir in Sri Lanka with a dual-model strategy. In December 2020, a bloom was detected with a high density of Aphanizomenon and chlorophyll-a concentration. We generated a set of algorithms using in situ chlorophyll-a data with surface reflectance of Sentinel-2 bands on the same day using linear regression analysis. The in situ chlorophyll-a concentration was better regressed to the reflectance ratio of (1 + R665)/(1–R705) derived from B4 and B5 bands of Sentinel-2 with high reliability (R2 = 0.81, p < 0.001). The second regression model was developed to predict Aphanizomenon cell density using chlorophyll-a as the proxy and the relationship was strong and significant (R2 = 0.75, p<0.001). Coupling the former regression models, an empirical model was derived to predict Aphanizomenon cell density in the same reservoir with high reliability (R2 = 0.71, p<0.001). Furthermore, the predicted and observed spatial distribution of Aphanizomenon was fairly agreed. Our results highlight that the present empirical model has a high capability for an accurate prediction of Aphanizomenon cell density and their spatial distribution in freshwaters, which helps in the management of toxic algal blooms and associated health impacts.

  • The Chl-a and Aphanizomenon prediction model was derived using Sentinel-2 imagery.

  • Modified reflectance ratios well regressed with in situ chl-a concentration.

  • Model predicts chl-a with high reliability (R2 = 0.83, p < 0.001).

  • Spatial distribution of Aphanizomenon cell density fairly agrees with field observations.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Deterioration of water quality in lakes, streams and reservoirs is a major global environmental and socioeconomic concern. In particular, freshwaters experience the unprecedented influence of nutrient enrichment, global warming and inorganic and organic pollution, and consequently become conducive for cyanobacterial bloom formation. Some of the cyanobacterial blooms are considered harmful (collectively referred to as harmful algal blooms, HABs) as they produce cyanotoxins. In addition, HABs reduce dissolved oxygen, alter the taste and odour of drinking water, reduce aesthetic value and impede the recreational value of the waterbody. Therefore, the presence of HABs stands as an indicator of water quality deterioration. Hence, frequent monitoring of their growth and development towards bloom formation is essential for the detection of the status of bloom and prevention of associated health risks.

Aphanizomenon is a filamentous, nitrogen-fixing cyanobacterial genera belonging to the order Nostocales. It is geographically widespread and commonly found in freshwaters at a wide range of temperatures (15–30 °C) (Cires & Ballot 2016). Globally, Aphanizomenon has been detected in several toxic cyanobacterial blooms including North American (Carmichael et al. 2000), European (Carrasco et al. 2007; Brient et al. 2009) and Asian reservoirs (Liu et al. 2006; Wu et al. 2010). Nostocales cyanobacteria are considered to be potentially invasive due to their inherent advantageous characteristics such as the presence of heterocyst for atmospheric nitrogen fixation, ecological plasticity, and development of resting and dispersal structures (akinetes) under adverse conditions (Sukenik et al. 2012). Aphanizomenon produces a range of cyanotoxins, namely anatoxin-a (ATX), cylindrospermopsin (CYN), microcystins (MCs) and saxitoxins (STX) (Table 1).

Table 1

Some of the previously reported incidences of Aphanizomenon and associated toxins

Toxin produced by AphanizomenonCountry reportedToxin levelReferences
Anatoxin-a Finland 1.1–11 μg/L Lepistö et al. (2005)  
New Zealand 1,430 μg/L Wood et al. (2007)  
Germany 2,354±273 μg/g Ballot et al. (2010)  
Cylindrospermopsin France 1.5–1.6 μg/L Brient et al. (2009)  
Italy 0.4–126 μg/L Messineo et al. (2010)  
Poland traces–3 μg/L Kokociński et al. (2013)  
Germany 2.3–6.6 mg/g Preußel et al. (2006)  
Australia 16–120 μg/L Shaw et al. (1999)  
Microcystin Finland 1.1–4,200 μg/L Lepistö et al. (2005)  
France 0.7 μg/L Brient et al. (2009)  
Russia 153.60 μg/L Šulčius et al. (2015)  
Lithuania 4.96 μg/L Šulčius et al. (2015)  
Saxitoxins Greece 17–42 μg/g Moustaka-Gouni et al. (2017)  
PSP (Paralytic Shellfish Poisoning) toxins Portugal 1.3 μg/L Pereira et al. (2004)  
France 5–7 μg/L Ledreux et al. (2010)  
Spain 26.1 μg/L Wörmer et al. (2011)  
Denmark 5.9 μg/L Kaas & Henriksen (2000)  
Toxin produced by AphanizomenonCountry reportedToxin levelReferences
Anatoxin-a Finland 1.1–11 μg/L Lepistö et al. (2005)  
New Zealand 1,430 μg/L Wood et al. (2007)  
Germany 2,354±273 μg/g Ballot et al. (2010)  
Cylindrospermopsin France 1.5–1.6 μg/L Brient et al. (2009)  
Italy 0.4–126 μg/L Messineo et al. (2010)  
Poland traces–3 μg/L Kokociński et al. (2013)  
Germany 2.3–6.6 mg/g Preußel et al. (2006)  
Australia 16–120 μg/L Shaw et al. (1999)  
Microcystin Finland 1.1–4,200 μg/L Lepistö et al. (2005)  
France 0.7 μg/L Brient et al. (2009)  
Russia 153.60 μg/L Šulčius et al. (2015)  
Lithuania 4.96 μg/L Šulčius et al. (2015)  
Saxitoxins Greece 17–42 μg/g Moustaka-Gouni et al. (2017)  
PSP (Paralytic Shellfish Poisoning) toxins Portugal 1.3 μg/L Pereira et al. (2004)  
France 5–7 μg/L Ledreux et al. (2010)  
Spain 26.1 μg/L Wörmer et al. (2011)  
Denmark 5.9 μg/L Kaas & Henriksen (2000)  

Among these toxins, neurotoxic ATX is the most dangerous form of cyanotoxin which can cause death within minutes or hours and is considered a Very Fast Death Factor (VFDF) (Trost & Oslob 1999). Importantly, ATX was found airborne during a harmful algal bloom where Aphanizomenon flos-aquae and Cuspidothrix issatschenkoi were present (Sutherland et al. 2021). This is an alarming new route of human exposure to ATX that has not been revealed before. Therefore, a predominance of multiple toxin-producing cyanobacterial species such as Aphanizomenon in the freshwaters need thorough attention. Under this context, early detection and timely public awareness have prime importance in minimizing potential health risks. Although the global warming and climate changes are considered as the prime factors for the high prevalence of algal blooms in general, actual triggers that drive toxic Aphanizomenon bloom formation remain unclear to date (Cires & Ballot 2016). Hence, frequent and long-term monitoring of water bodies are essential for understanding the dynamics of bloom formation, which will help to decide management criteria to control algal blooms. In this context, novel and robust water quality assessment techniques including satellite image-based modelling coupled with mathematical modelling would be better alternatives for the traditional water monitoring techniques.

In situ data collection and subsequent laboratory analysis are the most common approaches of monitoring water quality and algal blooms. However, owing to the laborious nature of currently practising traditional methods, nowadays in situ monitoring of water quality in combination with remote sensing is becoming a popular tool in global water quality monitoring programmes (Arabi et al. 2020; Chu et al. 2021). Remote sensing is a tool that can acquire a large amount of data with a sufficient spatial and spectral resolution to cover large geographical areas along with a high temporal coverage (Matthews et al. 2012). Furthermore, remote sensing can predict the dynamics of commonly used indicators of both cyanobacteria and HABs. Some of these indicators are chlorophyll-a, phycocyanin and water turbidity (Kudela et al. 2015). Chlorophyll-a (chl-a) is a spectrally active photosynthetic pigment present in both green algae and cyanobacteria. Therefore, chl-a is the most preferred pigment of bloom monitoring over phycocyanin which is an accessory pigment specific to freshwater cyanobacteria. Over the past few years, different algorithms have been developed to estimate the chl-a concentration using remote sensing (Boucher et al. 2018). Although significant relationships between in situ chl-a concentration with reflectance have been derived (Allan et al. 2011; Bresciani et al. 2011; Keith et al. 2012), the applicability of such relationships found limited accuracy outside the original area of the study and season of the year (Moses et al. 2012; Ligi et al. 2017; Boucher et al. 2018). Applicability of previously developed six models on 192 freshwater lakes across two states in the USA, New Hampshire and Maine, were tested for remote sensing of chl-a (Boucher et al. 2018). Those models were originally developed to apply in oceans, coastal and estuarine waters and large freshwater lakes. According to their findings, remotely predicted chl-a weakly correlated to the in situ measured chl-a while the strength of the correlation varied with the season and chl-a threshold. Therefore, regional-scale models which are developed by using best performing remote sensing algorithms for the implementation of efficient monitoring programmes is particularly important. As reviewed by Sebastiá-Frasquet et al. (2020), most of the existing remote sensing studies of algal blooms have an origin of either the USA, China or UK. However, this is an emerging research field where novel tools are being applied to achieve a higher accuracy. Recently, a novel interactive cloud-based dashboard called ‘CyanoKhoj’ has been developed by using Sentinel-3 Ocean and Land Colour Instrument (OLCI) data for rapid monitoring of cyanobacterial blooms across India (Maniyar et al. 2022). Other than that, remote sensing of algal blooms is not practised in the South Asian countries including Sri Lanka except for some publications originated from sporadic and short-term research programmes (Ha et al. 2017; Poddar et al. 2019; Wagle et al. 2019; Caballero & Navarro 2021; Gunawardana et al. 2021; Chusnah & Chu 2022; Tham et al. 2022).

Most of the existing cyanobacterial prediction algorithms fail to discriminate between cyanobacterial genera (Kutser et al. 2016). However, a separate monitoring of cyanobacterial genera is very important since some of these genera are more likely to be toxic than others (Paine et al. 2018). Even though there is scientific validity in this regard, only a limited effort has been made so far in this context. For example, Kudela et al. (2015) developed a model by using MODIS/ASTER (MASTER) Airborne sensor to distinguish Aphanizomenon and Microcystis in Pinto Lake, California. However, they were unable to do quantitative (cell density) prediction of either species, and suggested employing a different sensor having high spectral resolution and appropriate atmospheric corrections for separate monitoring of Aphanizomenon and Microcystis blooms. Apart from the limitations drawn by the airborne sensor used in the above study, the developed algorithms were applied only in eutrophic water, sparing a gap in the sensitivity of algorithms in monitoring Aphanizomenon in oligotrophic and mesotrophic waters. Therefore, here we highlight the necessity of developing Aphanizomenon index-derived algorithms that are capable of applying across a broad range of trophic status in water with high accuracy.

Among the different algorithms developed by using different sensors, empirical algorithms that directly correlate remote sensing data with in situ measured data have been successfully applied not only to monitor algal blooms but also to quantify algal pigments, such as chl-a, cyanobacteria-specific pigment, phycocyanin and turbidity (Chang et al. 2004; Ruiz-Verdú et al. 2008; Petus et al. 2010). Different satellite imagers, such as WorldView-2, Sentinel-2, Landsat-8, MODIS and MERIS have been widely used for remote sensing of algal bloom with a varying degree of success and those were reported in the literature with their limitations (Gitelson et al. 1993; Mayo et al. 1995; Duan et al. 2008; Chavula et al. 2009; Moses et al. 2009; Beck et al. 2016; Poddar et al. 2019; Sebastiá-Frasquet et al. 2020). Specifically, many of those sensors are less preferred at present due to low spatial and spectral resolution, low signal-to-noise ratio and inadequate repeat cycles for terrestrial satellites (Duan et al. 2008). Sentinel-2A and 2B launched in June 2015 and July 2016 by the European Space Agency complement many of those issues. Sentinel-2 has a high spatial resolution (10 m pixel), weekly revisit time and a cluster of narrow spectral bands making it an excellent option to monitor cyanobacterial blooms (Gascon et al. 2017; Caballero et al. 2020). Furthermore, the opportunity given by Sentinel-2 for fine-scale mapping with free public access has widened its applications. Therefore, Sentinel-2 sensors would replace most previously used sensors and may provide more accurate monitoring options for cyanobacterial blooms.

Many empirical algorithms have been derived either by using various bands or band ratios of the reflectance spectrum of the Sentinel-2 with their own sensitivity and detection range (Sebastiá-Frasquet et al. 2020). However, the majority of those algorithms were rarely evaluated outside the original place of development (Sebastiá-Frasquet et al. 2020), while those were restricted to a narrow range of chl-a concentrations (Ha et al. 2017; Yadav et al. 2019; Germán et al. 2021). The sensitivity of algorithms appeared dependent upon the selected spectral band combinations and ratios. For instance, Chen et al. (2017) obtained relatively low accuracy (R2 = 0.49) when B4 and B5 bands were used while Ha et al. (2017) obtained high accuracy (R2 = 0.68) with band ratio of B3 and B4, Yadav et al. (2019) obtained high accuracy (R2 = 0.76) with B2, B3, B4 and B8 band combinations and Germán et al. (2021) obtained even higher accuracy (R2 = 0.77) with B8/B4 band ratio. On the other hand, only a few Sentinel-2 algorithms specifically detect a particular species of bloom-forming cyanobacteria such as Microcystis (German et al. 2020) while few other algorithms focused on total cyanobacterial count or total biovolume of cyanobacteria (Beck et al. 2017; Pompêo et al. 2021). None of the Sentinel-2 algorithms were developed for the specific detection and quantification of Aphanizomenon density to date. Hence, there will still be a large room for novelty and improvement of existing models for robust algorithms to keep pace with the rapidly evolving field of remote sensing and detection of cyanobacterial blooms.

Our aim in this study was to develop an empirical model to predict spatial distribution of Aphanizomenon cell density by coupling in situ data with Sentinel-2 reflectance data to support the developing efficient monitoring strategies of algal blooms in the future.

Study area

Ridiyagama reservoir (6.203077°N, 80.983253°E) is situated in the Southern dry zone of Sri Lanka (Figure 1, left) which receive approximately 1,000 mm annual rainfall and the annual mean temperature is 27 °C. Ridiyagama is the second largest reservoir (8.8 km2 surface area) in the Southern province with a volume of 27×106 m3. The reservoir is mainly fed by the Walawa River and the terrain around the reservoir is mostly surrounded by paddy fields. Therefore, there is a high potential to receive nutrient-rich surface runoff to the reservoir. It is a multipurpose reservoir, which supplies water for irrigation, fisheries and drinking water treatment plants. Ridiyagama reservoir is the main water source that provides drinking water to the emerging Hambanthota Industrial City as well as the Hambanthota international port of Southern Sri Lanka. Hence, the reservoir is maintained at spill level throughout the year. A dense growth of water hyacinth (Pontederia crassipes, formerly known as Eichhornia crassipes) was dominant at inlets while Salvinia molesta, Pistia stratiotes and duckweed (Lemna spp.) were rarely observed. Utricularia was the only submerged vegetation that was observed, only at inlets. The other areas of the reservoir found free-from floating and submerged vegetation. Inlets containing aquatic vegetation were excluded during sampling.
Figure 1

Location of Ridiyagama reservoir (left) and sites of sample collection (right, indicated with dots).

Figure 1

Location of Ridiyagama reservoir (left) and sites of sample collection (right, indicated with dots).

Close modal

Field sampling and laboratory analysis

Field samplings were carried out on 7 December 2020 and 30 November 2021. The in situ chl-a concentrations were measured using a chlorophyll-a sensor (TAL-PC, YSI, USA). Overall, 90 data records from 30 sampling sites on 7 December 2020 and 36 data records from 18 sampling sites on 30 November 2021 were collected. Coordinates at each sampling site were recorded with a portable GPS (eTrex 22x, Garmin, UK).

For phytoplankton analysis, approximately 100 L of water sample was filtered through a plankton net (30 μm, Hydro-Bios, Germany) and planktons were collected into 500-mL amber high-density polyethylene bottles at each sampling site. Samples were observed with the aid of a bright-field microscope equipped with a digital camera (Nikon, Japan) to identify dominant phytoplankton. Their cell density was estimated following the method described in Intergovernmental Oceanographic Commission (IOC) Manuals and Guides No.55.

Satellite data acquisition and pre-processing

Terrain-corrected Sentinel-2 MSI images of the study area were downloaded from the USGS (United States Geological Survey) server using the Earth Explorer platform (http://glovis.usgs.gov/) from the nearest date to the field sampling. Accordingly, images dated 7 December 2020 and 3 December 2021 were downloaded. All images were selected specifically to have <20% cloud cover, and sampling sites near to a cloud were excluded. ESRI ArcGIS 10.6.1 (Esri Inc., USA) and QGIS 3.16 software were used to process acquired satellite data.

Initially, digital numbers were converted into radiance based on the sensor specific rescaling factors provided in the meta data file. Then, radiance was converted into top of atmosphere (TOA) reflectance with the aid of additional information such as the Earth–Sun distance, solar zenith angle and exo-atmospheric irradiance. Based on the atmospheric condition at the time of image acquisition, atmospheric correction was applied to TOA reflectance and finally, surface reflectance was determined as described by Moran et al. (1992) (Equation (1)).
formula
(1)
where ρ is the spectral reflectance, Lλ is the spectral radiance, Lp is the path radiance, d is the Earth–Sun distance in astronomical units, Tv is the atmospheric transmittance in the viewing direction, ESUNλ is the mean solar exo-atmospheric irradiances, θ is the angle of incidence of the direct solar flux onto the earth's surface, Tz is the atmospheric transmittance in the illumination direction and Edown is the downwelling diffuse irradiance.

Dark object subtraction (DOS), which is a quick and simple method for atmospheric correction was used to minimize atmospheric effects (Giardino et al. 2001; Urbanski et al. 2016). The scattering was removed by subtracting the darkest pixel value from every pixel in the band (Ruddick et al. 2000; Boucher et al. 2018).

Development of empirical models

For developing empirical models with dual-model strategy, regression analysis was carried out on the combined dataset of chl-a taken from the 30 sampling sites on 7 December 2020. Several empirical regression models were developed between in situ chl-a values and same day reflectance values. Direct and log transformed chl-a data were applied with reflectance values of various spectral band combinations of B2, B3, B4, B5, B6, B7 and B8. To determine the agreement of developed models, coefficients were determined using linear regression analysis and the best performing model was selected based on the regression coefficients of determination (R2) at p < 0.001.

To determine the relationship between the dominant cyanobacterial cell density (Aphanizomenon in here) and related surface reflectance, initially the relationship between Aphanizomenon cell density and chl-a concentration was determined by employing the linear regression analysis. Then, Aphanizomenon density was regressed with reflectance data through the relationship developed between chl-a concentration and surface reflectance (Figure 2).
Figure 2

Methodological scheme of the major steps in the empirical model development.

Figure 2

Methodological scheme of the major steps in the empirical model development.

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Model validation

For the validation of the developed model, in situ chl-a data collected on 30 November 2021 from 18 sampling sites to represent the entire reservoir and Sentinel-2 satellite images captured on 3 December 2021 were employed (Figure 2). Reflectance data from each sampling site were extracted from the pre-processed images. The chl-a concentration in each sampling site was estimated using the developed model equation and the extracted reflectance data. Finally, sensitivity of the model was determined according to the regression analysis carried out between in situ and predicted chl-a concentrations. Relationships resulting in the highest R2, the lowest root mean square error (RMSE) (Equation (2)) and the lowest percentage RMSE (Equation (3)) were employed as criteria to select the best performing models (Dube et al. 2014).
formula
(2)
formula
(3)
where is the measured chl-a concentrations, is the estimated chl-a concentrations and n is the number of observations.

Furthermore, based on the developed relationship between Aphanizomenon cell density and chl-a concentration, Aphanizomenon cell density of the Ridiyagama reservoir was predicted. Sensitivity of the model was determined according to the regression analysis. RMSE and percentage RMSE were also calculated to evaluate the model performance.

In situ chlorophyll-a and phytoplankton density

During the first field sampling (7 December 2020), a thin surface bloom was visible in several sampling sites near to the periphery of the reservoir (Figure 3(a)) while it was absent in the middle of the reservoir where high turbulence was present. This bloom was absent in the next sampling date on 30 November 2021. Aphanizomenon was found with high cell density in both sampling dates (Table 2). It was identified as A. issatschenkoi by morphology showing the characteristic features of solitary free-floating trichomes with cylindrical vegetative cells and long, pointed and hyaline apical cells, intercalary heterocysts and akinetes (Figure 3(b)) (Komárek & Komárková 2006). In both sampling dates, several other species of cyanobacteria such as Pseudanabaena, Leptolyngbya and Anabaenopsis were rarely observed in some of the sampling sites. Therefore, Aphanizomenon was likely to be the only contributor to the detected bloom. Other than cyanobacteria, Melosira diatom was also present in all sampling sites.
Table 2

Summary of chl-a and Aphanizomenon density in Ridiyagama reservoir

Sampling dateChl-a (μg/L)
Aphanizomenon cell density (cells/L) × 103
Mean ± SDMin–MaxMean ± SDMin–Max
7 December 2020 40.32 ± 14.07 19.19–85.80 7.54 ± 4.82 1.25–31.23 
30 November 2021 6.95 ± 1.73 3.86–10.03 3.92 ± 2.09 1.25–21.25 
Sampling dateChl-a (μg/L)
Aphanizomenon cell density (cells/L) × 103
Mean ± SDMin–MaxMean ± SDMin–Max
7 December 2020 40.32 ± 14.07 19.19–85.80 7.54 ± 4.82 1.25–31.23 
30 November 2021 6.95 ± 1.73 3.86–10.03 3.92 ± 2.09 1.25–21.25 
Figure 3

The bloom appeared on the surface of water in Ridiyagama reservoir (a) and filaments of Aphanizomenon (b) (400x, Nikon, Japan).

Figure 3

The bloom appeared on the surface of water in Ridiyagama reservoir (a) and filaments of Aphanizomenon (b) (400x, Nikon, Japan).

Close modal

It is well accepted that chl-a could be considered as a proxy of eutrophication in the lentic systems, where the chl-a concentration above 9 μg/L indicates eutrophication (Nürnberg 1996; Mamun et al. 2020). Accordingly, Ridiyagama reservoir on 7 December 2020 was eutrophic while it was oligotrophic on the other sampling date (Table 2). Aphanizomenon density was nearly 2 times higher in eutrophic water and in the presence of bloom compared to oligotrophic water (Table 2).

Development of empirical models

For modelling, 90 records of chl-a were used as the response variable and spectral bands of the visible light portion (B2 [blue], B3 [green], B4 [red]) and Vegetation Red Edge portion (B5, B6, B7) and NIR (Near Infra-Red [B8]) as predictor variables. Algorithms were developed for the estimation of chl-a concentration by considering all possible band regions of the reflectance spectrum. In total, 130 algorithms with different band combinations were developed and algorithms that were statistically significant (p < 0.001) and gave high R2 are listed in Table 3.

Table 3

Regression models of chl-a concentration derived from Sentinel-2 reflectance spectral data

Spectral indexFitted equationR2RMSE
(1 + R665)/(1 − R705y = −0.0023x + 1.2209 0.73 4.32 
(1 + R665)/(1 − R740y = −0.0017x + 1.1824 0.69 4.74 
(1 + R665)/(1 − R783y = −0.0018x + 1.1923 0.68 4.88 
(1 + R665)/(1 − R842y = −0.0015x + 1.1722 0.68 4.86 
R665/R490 y = −0.0005x + 1.3533 0.005 100.51 
(R490 − R665)/R560 y = −0.0004x + 0.2286 0.008 80.60 
(R665 − R490)/(R665 + R490y = −0.0002x + 0.148 0.008 78.53 
(R665/R705 − 1)/(R665/R705 + 1) y = −0.0036x + 0.228 0.20 53.72 
R665/R560 y = −0.002x + 0.9528 0.13 19.56 
(1 + R665)/(1 − R705) y = −0.1107 log x + 1.242 0.81 6.13 
(1 + R665)/(1 − R740y = −0.0844 log x + 1.2395 0.78 18.15 
(1 + R665)/(1 − R783y = −0.0892 log x + 1.256 0.78 17.32 
(1 + R665)/(1 − R842y = −0.0726 log x + 1.2209 0.76 19.41 
R665/R490 y = −0.0052 log x + 1.4976 0.025 96.54 
(R490 − R665)/R560 y = −0.0036 log x + 0.327 0.039 92.67 
(R665 − R490)/(R665 + R490y = −0.002 log x + 0.2016 0.034 87.58 
Spectral indexFitted equationR2RMSE
(1 + R665)/(1 − R705y = −0.0023x + 1.2209 0.73 4.32 
(1 + R665)/(1 − R740y = −0.0017x + 1.1824 0.69 4.74 
(1 + R665)/(1 − R783y = −0.0018x + 1.1923 0.68 4.88 
(1 + R665)/(1 − R842y = −0.0015x + 1.1722 0.68 4.86 
R665/R490 y = −0.0005x + 1.3533 0.005 100.51 
(R490 − R665)/R560 y = −0.0004x + 0.2286 0.008 80.60 
(R665 − R490)/(R665 + R490y = −0.0002x + 0.148 0.008 78.53 
(R665/R705 − 1)/(R665/R705 + 1) y = −0.0036x + 0.228 0.20 53.72 
R665/R560 y = −0.002x + 0.9528 0.13 19.56 
(1 + R665)/(1 − R705) y = −0.1107 log x + 1.242 0.81 6.13 
(1 + R665)/(1 − R740y = −0.0844 log x + 1.2395 0.78 18.15 
(1 + R665)/(1 − R783y = −0.0892 log x + 1.256 0.78 17.32 
(1 + R665)/(1 − R842y = −0.0726 log x + 1.2209 0.76 19.41 
R665/R490 y = −0.0052 log x + 1.4976 0.025 96.54 
(R490 − R665)/R560 y = −0.0036 log x + 0.327 0.039 92.67 
(R665 − R490)/(R665 + R490y = −0.002 log x + 0.2016 0.034 87.58 

R denotes reflectance at particular wavelength in nanometres. Bolded text indicates the spectral index having the highest R2.

Algorithms developed using the combinations of B4 (665 nm), B5 (705 nm), B6 (740 nm), B7 (783 nm) and B8 (842 nm) gave higher R2 values compared to B2 (490 nm) and B3 (560 nm) (Table 3). When chl-a values were log transformed, it gave higher R2 than the direct chl-a concentration (Table 3). Among the 16 algorithms, reflectance ratio of (1 + R665)/(1−R705) derived from B4 and B5 was highly correlated to the log transformed chl-a concentration (R2 = 0.81), gave comparatively low RMSE (6.13), and this relationship was statistically significant (p < 0.001) (Table 3, Figure 4).
Figure 4

Relationship between log chl-a and the reflectance ratio of [(1 + R665)/(1 − R705)] derived from B4 and B5 (Rr refers to the reflectance ratio, chl: Chl-a refers to the concentration).

Figure 4

Relationship between log chl-a and the reflectance ratio of [(1 + R665)/(1 − R705)] derived from B4 and B5 (Rr refers to the reflectance ratio, chl: Chl-a refers to the concentration).

Close modal
Based on the developed regression model, chl-a concentration of the reservoir water can be estimated using the following model equation (Equation (4)).
formula
(4)
where chl is chl-a concentration (μg/L), R665 is reflectance of Sentinel B4, R705 is reflectance of Sentinel B5, K1 = 1.242 and Km = 0.1107, respectively.

Model validation

The chl-a concentration in Ridiyagama reservoir in November 2021 was estimated using the developed model Equation (4). The in situ data showed that the average chl-a concentration had reduced nearly 6 times compared to the chl-a concentration of 2020, most probably due to the dilution effect of the rainfall that prevailed prior to the sampling. According to the prediction model, the estimated mean chl-a concentration was 6.76 ± 3.78 μg/L and it ranged from 1.59 to 13.76 μg/L at sampling sites. The relationship between in situ and predicted chl-a values displayed a correlation (R2) of 0.83, RMSE of 6.13 with very high significance (p < 0.001) (Figure 5).
Figure 5

The linear regression between in situ and predicted chl-a concentration in Ridiyagama reservoir (dotted line), solid line is the expected 1:1 line.

Figure 5

The linear regression between in situ and predicted chl-a concentration in Ridiyagama reservoir (dotted line), solid line is the expected 1:1 line.

Close modal
In addition to the linear regression, to reinforce the algorithm accuracy assessment, the variation of in situ chl-a and predicted chl-a in each sampling site in Ridiyagama reservoir were represented in Figure 6. Both in situ and estimated data exhibited the same chronological trend in the chl-a concentration indicating high prediction accuracy of the developed regression model.
Figure 6

In situ and predicted chl-a concentration in each sampling site in Ridiyagama reservoir.

Figure 6

In situ and predicted chl-a concentration in each sampling site in Ridiyagama reservoir.

Close modal
Using the developed prediction model, spatial variation of chl-a in Ridiyagama reservoir was determined and it was compared with the spatial variation determined by in situ chl-a measurements (Figure 7). Similar spatial pattern of chl-a distribution was observed in both maps indicating promising application of the predictive model on the assessment of in situ chl-a.
Figure 7

Spatial distribution pattern of (a) in situ chl-a (μg/L) and (b) predicted chl-a concentration (μg/L) on 30 November 2021 in Ridiyagama reservoir.

Figure 7

Spatial distribution pattern of (a) in situ chl-a (μg/L) and (b) predicted chl-a concentration (μg/L) on 30 November 2021 in Ridiyagama reservoir.

Close modal

Correlation between chlorophyll-a and Aphanizomenon cell density

Aphanizomenon was observed in all sampling sites while other cyanobacterial genera were rarely observed. The genus Aphanizomenon represented >75% of total phytoplankton in the majority of sampling sites with cell density range from 1.25–31.23 × 103 cells/L (Supplementary Material, Figure 1). In order to predict Aphanizomenon cell density, correlation between chl-a and Aphanizomenon cell density was determined. The data showed a strong correlation between chl-a concentration and Aphanizomenon cell density (R2 = 0.75, p < 0.001, Figure 8). Therefore, our results implicate the applicability of chl-a to estimate the cell density of Aphanizomenon at a reasonable accuracy.
Figure 8

Correlation between chl-a concentration and cell density of Aphanizomenon in the Ridiyagama reservoir (Aphcell refers to Aphanizomenon cell density; chl refers to Chl-a concentration).

Figure 8

Correlation between chl-a concentration and cell density of Aphanizomenon in the Ridiyagama reservoir (Aphcell refers to Aphanizomenon cell density; chl refers to Chl-a concentration).

Close modal
Based on the developed regression model (Figure 8), the Aphanizomenon cell density can be estimated using Equations (5) and (6).
formula
(5)
where chl represents chl-a concentration (μg/L) and Aphcell is the cell density of Aphanizomenon (cells/L).
formula
(6)
When the developed model was applied to predict Aphanizomenon in the same reservoir, the prediction was significantly reliable compared to its in situ data (R2 = 0.71, p < 0.001). We observed a similar spatial distribution pattern of either increasing or decreasing trends between in situ and predicted values (Figure 9) highlighting the application of the predictive model to assess Aphanizomenon density and its spatial and temporal distribution.
Figure 9

Spatial distribution pattern of (a) in situ Aphanizomenon cell density (cells/L) and (b) predicted Aphanizomenon cell density (cells/L) on 7 December 2020 in Ridiyagama reservoir and (c) the agreement between the observed and predicted Aphanizomenon cell density (dotted line), solid line is the expected 1:1 line.

Figure 9

Spatial distribution pattern of (a) in situ Aphanizomenon cell density (cells/L) and (b) predicted Aphanizomenon cell density (cells/L) on 7 December 2020 in Ridiyagama reservoir and (c) the agreement between the observed and predicted Aphanizomenon cell density (dotted line), solid line is the expected 1:1 line.

Close modal

Remote sensing is a promising technology for tracking large-scale high-frequency data compared to traditional water quality monitoring surveys which are often time-intensive, expensive, laborious and unable to provide data on a wider scale. In the present study, we showed successful application of Sentinel-2 data to predict chl-a and Aphanizomenon cell density in a freshwater reservoir. Although the developed models were derived using data collected from eutrophic (chl-a > 9.0 μg/L) condition, the same model could also be applicable for oligotrophic waters to predict chl-a concentrations and Aphanizomenon cell density with a high reliability.

Application of Sentinel-2 image data in water quality assessments is becoming popularly used in limnologic studies (Sòria-Perpinyà et al. 2020) as there is high spatial resolution compared to other remote sensing applications such as Landsat-8, MODIS, etc. The spectral range of Sentinel-2 bands spans over a wide range where B1, B2, B3 and B4 are coastal aerosol, blue, green and red bands, respectively. B5, B6 and B7 are vegetation red edge bands while B8 is the NIR band. Algorithms based on the red and NIR bands are more successful in predicting chl-a concentration (Tebbs et al. 2013). The absorption peak at the short wavelength region (450–500 nm) is given due to the combination effect of chl-a, carotenoids and dissolved organic matter (Gitelson et al. 2000). Reflectance of green peak (543–578 nm) represents the minimum pigment absorption of chlorophyll, phycocyanin and carotenoids and scattering by non-organic particles and phytoplankton cell walls (Gitelson et al. 2000; Schalles & Yacobi 2000). Small peak at 620 nm represents the reflectance caused by phycocyanin and the peak at 705 nm (red band) is caused by the suspended matter in water, including algal cells and absorption of water. (Gitelson et al. 2000). The next three bands in the spectrum are vegetation red edge bands and those bands are the newly included bands in order to enhance the applications of Sentinel-2 sensor. Therefore, algorithms developed using these red (B4), vegetation red edge (B5, B6 and B7) and NIR (B8) bands have promising applications in inland water studies.

The available empirical algorithms derived by Sentinel-2 data represent a varying degree of accuracy. The detection range of those empirical models depend upon the selected spectral bands and band ratios. Obtaining a high correlation between the predicted chl-a using remote sensing tools and in situ measured chl-a concentrations along with a broad detection range is a challenging task. Even with the same spectral band combinations, two algorithms are not identical as there are differences in the way that these data were incorporated into the algorithm. Similar to the present study, Chen et al. (2017) also used both B4 and B5 bands, while the level of accuracy of the present study (R2 = 0.81) was comparatively higher than the former study (R2 = 0.49). Although the same bands were selected in both studies, the applied band ratios were different. The ecological models are continuously modified to achieve better predictions together with a high accuracy either by replacing or adding new variables to the existing models and claims novelty. Chl-a and Aphanizomenon have a significant absorbance at λ665 compared to λ705 where both have a significant reflectance (Paine et al. 2018). Instead of applying original reflectance, the present study scaled up the reflectance at the significant absorbance phase of λ665 (1 + R665) while it down at the significant reflectance phase of λ705 (1−R705) to enhance the intended ratio of the two bands as a new approach. Modified reflectance ratios were well regressed compared to using their original values in our study (Table 3). On the contrary, moving more towards the NIR region hardly attain spectral distinction between species (Paine et al. 2018) and it was ascertained that B4 and B5 bands worked well in the present empirical model rather than conventional λ665 (B4) to λ842 (B8) ratio.

Generally, single reflectance band algorithms are preferred for in situ radiometry, when atmospheric correction is not an issue, whereas band ratios are preferred when atmospheric factors that affect reflectance are present such as sun glint and other atmospheric factors (Tebbs et al. 2013). Compared to single bands, a two-band ratio algorithm [(1 + R665)/(1 − R705)] derived from B4 and B5 of the spectrum performed best for the prediction of chl-a concentrations. Furthermore, the selected B4 and B5 regions of the reflectance spectrum permit separate detection of cyanobacteria from green algae that would interfere distinct monitoring of cyanobacterial blooms (Paine et al. 2018). According to the time window factor, decreasing the time between in situ chl-a measurement and the date of satellite image is better for the performance of the algorithms (Boucher et al. 2018). The most robust algorithms are those that were developed with matching time between in situ sampling and remote sensing data acquisition (Kabbara et al. 2008; Keith et al. 2012). In this study, we were able to capture satellite images within 3 days of in situ sampling and thereby increased the performance of predictive models. Being a tropical country, cloud cover is a major concern when obtaining satellite images especially for temporal data retrieval. In the present study, Sentinel images of sampling sites disturbed by cloud cover were excluded.

Different forms of algorithms have been developed worldwide and reported in the scientific literature to predict chl-a concentrations by using remote sensing techniques (Matthews et al. 2010, 2012; Tebbs et al. 2013; Keith et al. 2018). Some of those were only applicable to detect chl-a contents of oligotrophic waters (chl-a concentration <9 μg/L). Compared to those previously established models, present study was able to predict chl-a concentrations with a high accuracy (R2 = 0.83) together with wider detection range of chl-a (approximately 3–100 μg/L) extending its applicability for both oligotrophic and eutrophic waters. On the other hand, validation at a wide range of in situ data would minimize the false estimation. Therefore, the empirical algorithms developed in the present study would permit their applicability in freshwaters in other geographic regions with simple recalibration and validation.

Furthermore, we established a relationship between Aphanizomenon cell density and reflectance values to predict Aphanizomenon cell density. As some species of the genera are capable of producing cyanotoxins, frequent and timely monitoring is highly important to prevent health risks associated with algal blooms. In this context, remote sensing of Aphanizomenon provides a versatile tool for routing monitoring and surveillance of bloom development. It will facilitate timely implementation of public alerts on potential health risks. Previously, Aphanizomenon was remotely predicted in Pinto Lake in California using the data MASTER Airborne sensor (Kudela et al. 2015), and their detection was based on the spectral-shape algorithms which lacked Aphanizomenon index-derived quantification. Therefore, we attempted to predict Aphanizomenon cell density in freshwaters through the model.

As the formation of cyanobacterial blooms is increasing worldwide, empirical models can provide a significant contribution to minimize anticipated health impact by facilitating early warnings. Routing availability of such data would allow more reliable decision-making by authorities towards efficient management plans to ensure public health. Remote sensing of multiple toxin-producing Aphanizomenon is particularly important for Ridiyagama reservoir because of its multipurpose nature. In the future, applications could further be extended by coupling other proxies such as turbidity, dissolved oxygen, nitrogen, and phosphorus in the aquatic environment, for example, to predict trophic status, identification of bioindicators of water nutrient pollution, etc.

We developed robust algorithms for monitoring spatial variations in the distribution of chl-a and Aphanizomenon cell density in freshwaters. The study was based on the in situ measurements of chlorophyll-a that were correlated with reflectance data of Sentinel-2 images relevant to the study area. Our results indicate that chl-a and cyanobacteria density can be detected and quantified with significantly high accuracy by hyperspectral remote sensing. The best algorithm was obtained from the reflectance band ratio of (1 + R665)/1−R705) with an R2 of 0.81 (p < 0.001). The relationship between in situ and predicted chl-a values displayed a correlation (R2) of 0.83, RMSE of 6.13 with a very high significance (p < 0.001), confirming the reliable prediction of chlorophyll-a concentration. Furthermore, an empirical model was developed to predict cell density of Aphanizomenon which is a cyanotoxin-producing cyanobacterial genus that has global distribution. The models developed here could be successfully used to predict quantitative and spatial distribution of chl-a and Aphanizomenon and its potential for bloom formation in both eutrophic and oligotrophic water in Ridiyagama reservoir, and this approach can be adopted to freshwaters elsewhere in the world with simple recalibration and validation.

This research was supported by the Accelerating Higher Education Expansion and Development (AHEAD) Operation of the Ministry of Higher Education Sri Lanka funded by the World Bank. Furthermore, the support given by the National Aquaculture Development Authority of Sri Lanka during field data collection is highly acknowledged.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Allan
M. G.
,
Hamilton
D. P.
,
Hicks
B. J.
&
Brabyn
L.
2011
Landsat remote sensing of chlorophyll a concentrations in central North Island lakes of New Zealand
.
Int. J. Remote Sens.
32
(
7
),
2037
2055
.
doi:10.1080/01431161003645840
.
Arabi
B.
,
Salama
M. S.
,
Pitarch
J.
&
Verhoef
W.
2020
Integration of in-situ and multi-sensor satellite observations for long-term water quality monitoring in coastal areas
.
Remote Sensing Environ.
239
,
11632
.
doi.org/10.1016/j.rse.2020.111632
.
Beck
R.
,
Zhan
S.
,
Liu
H.
,
Tong
H.
,
Yang
B.
,
Xu
M.
,
Ye
Z.
,
Huang
Y.
,
Shu
S.
,
Wu
Q.
,
Wang
S.
,
Berling
K.
,
Murray
A.
,
Emery
E.
,
Reif
M.
,
Harwood
J.
,
Young
J.
,
Nietch
C.
,
Macke
D.
,
Martin
M.
,
Stillings
G.
,
Stump
R.
,
Su
H.
2016
Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations
.
Remote Sens. Environ.
178
,
15
30
.
doi:10.1016/j.rse.2016.03.002
.
Beck
R.
,
Xu
M.
,
Zhan
S.
,
Liu
H.
,
Johansen
R. A.
,
Tong
S.
,
Yang
B.
,
Shu
S.
,
Wu
Q.
&
Wang
S.
,
Berling
K.
,
Murray
A.
,
Emery
E.
,
Reif
M.
,
Harwood
J.
,
Young
J.
,
Martin
M.
,
Stillings
G.
,
Stumpf
R.
,
Su
H.
,
Ye
Z.
,
Huang
Y.
2017
Comparison of satellite reflectance algorithms for estimating phycocyanin values and cyanobacterial total biovolume in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations
.
Remote Sens.
9
,
538
.
doi:10.3390/rs9060538
.
Boucher
J.
,
Weathers
K. C.
,
Norouzi
H.
&
Steele
B.
2018
Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithms for regional freshwater monitoring
.
Ecol. Appl.
28
(
4
),
1044
1054
.
doi:10.1002/eap.1708
.
Bresciani
M.
,
Stroppiana
D.
,
Odermatt
D.
,
Morabito
G.
&
Giardino
C.
2011
Assessing remotely sensed chlorophyll-a for the implementation of the Water Framework Directive in European perialpine lakes
.
Sci. Total Environ.
409
(
17
),
3083
3091
.
doi:10.1016/j.scitotenv.2011.05.001
.
Brient
L.
,
Lengronne
M.
,
Bormans
M.
&
Fastner
J.
2009
First occurrence of cylindrospermopsin in freshwater in France
.
Environ. Toxicol: An Int. J.
24
(
4
),
415
420
.
doi:10.1002/tox.20439
.
Caballero
I.
,
Fernánde
R.
,
Escalante
O. M.
,
Mamán
L.
&
Navarro
G.
2020
New capabilities of Sentinel-2A/B satellites combined with in situ data for monitoring small harmful algal blooms in complex coastal waters
.
Sci. Rep.
10
(
1
),
1
14
.
doi:10.1038/s41598-020-65600-1
.
Carmichael
W. W.
,
Drapeau
C.
&
Anderson
D. M.
2000
Harvesting of Aphanizomenon flos-aquae Ralfs ex Born. & Flah. var. flos-aquae (Cyanobacteria) from Klamath Lake for human dietary use
.
J. Appl. Phycol.
12
(
6
),
585
595
.
doi:10.1023/A:102650671356
.
Carrasco
D.
,
Moreno
E.
,
Paniagua
T.
,
Hoyos
C. D.
,
Wormer
L.
,
Sanchis
D.
,
Cires
S.
,
Martín-del-Pozo
D.
,
Codd
G. A.
&
Quesada
A.
2007
Anatoxin-a occurrence and potential cyanobacterial anatoxin-a producers in Spanish reservoirs
.
J. Phycol.
43
(
6
),
1120
1125
.
doi:10.1111/j.1529-8817.2007.00402.x
.
Chang
K. W.
,
Shen
Y.
&
Chen
P. C.
2004
Predicting algal bloom in the Techi reservoir using Landsat TM data
.
Int. J. Remote Sens.
25
,
3411
3422
.
Chavula
G.
,
Brezonik
P.
,
Thenkabail
P.
,
Johnson
T.
&
Bauer
M.
2009
Estimating chlorophyll concentration in Lake Malawi from MODIS satellite imagery
.
Phys. Chem. Earth, Parts A/B/C.
34
(
13–16
),
755
760
.
doi:10.1016/j.pce.2009.07.015
.
Chen
J.
,
Zhu
W.
,
Tian
Y. Q.
,
Yu
Q.
,
Zheng
Y.
&
Huang
L.
2017
Remote estimation of colored dissolved organic matter and chlorophyll-a in Lake Huron using Sentinel-2 measurements
.
J. Appl. Remote Sens.
11
,
036007
.
Chu
H.-J.
,
He
Y.-C.
,
Chunah
W. N.
,
Jaelani
L. M.
&
Chang
C.-H.
2021
Multi-reservoir water quality mapping from remote sensing using spatial regression
.
Sustainability
13
(
11
),
6416
.
doi:10.3390/su13116416
.
Chusnah
W. N.
&
Chu
H.-J.
2022
Estimating chlorophyll-a concentrations in tropical reservoirs from band-ratio machine learning models
.
Remote Sens. Appl.: Society Environ.
25
,
100678
.
Duan
H.
,
Zhang
S.
&
Zhang
Y.
2008
Remote sensing monitoring method for cyanobacteria blooms in Taihu lake
.
J. Lake. Sci.
2
,
145
152
.
Dube
T.
,
Gumindoga
W.
&
Chawira
M.
2014
Detection of land cover changes around Lake Mutirikwi, Zimbabwe, based on traditional remote sensing image classification techniques
.
Afr. J. Aquat. Sci.
39
(
1
),
89
95
.
doi:10.2989/16085914.2013.870068
.
Gascon
F.
,
Bouzinac
C.
,
Thépaut
O.
,
Jung
M.
,
Francesconi
B.
,
Louis
J.
,
Lonjou
V.
,
Lafrance
B.
,
Massera
S.
&
Gaudel-Vacaresse
A.
2017
Copernicus Sentinel-2A calibration and products validation status
.
Remote Sens.
9
(
6
),
584
.
doi:10.3390/rs9060584
.
German
A.
,
Ferral
A.
,
Scavuzzo
C.
&
Shimoni
M.
2020
Multitemporal spectral analysis for algae detection in an eutrophic lake using Sentinel 2 images
.
ISPRS – Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
XLII
(
3/W12
),
147
152
.
Germán
A.
,
Shimoni
M.
,
Beltramone
G.
,
Rodríguez
M. I.
,
Muchiut
J.
,
Bonansea
M.
,
Scavuzzo
C. M.
&
Ferral
A.
2021
Space-time monitoring of water quality in an eutrophic reservoir using Sentinel-2 data-A case study of San Roque, Argentina
.
Remote Sens. Appl.: Soc. Environ.
24
,
100614
.
Giardino
C.
,
Pepe
M.
,
Brivio
P. A.
,
Ghezzi
P.
&
Zilioli
A.
2001
Detecting chlorophyll, Secchi disk depth and surface temperature in a sub-alpine lake using Landsat imagery
.
Sci. Total Environ.
268
(
1–3
),
9
29
.
doi:10.1016/S0048-9697(00)00692-6
.
Gitelson
A.
,
Garbuzov
G.
,
Szilagyi
F.
,
Mittenzwey
K.-H.
,
Karnieli
,
A.
&
Kaiser
A.
1993
Quantitative remote sensing methods for real-time monitoring of inland waters quality
.
Int. J. Remote Sens.
14
,
1269
1295
.
Gitelson
A.
,
Yacobi
Y.
,
Schalles
J.
,
Rundquist
D.
,
Han
L.
,
Stark
R.
&
Etzion
D.
2000
Remote estimation of phytoplankton density in productive waters
.
Advanc. Limnol.
55
,
121
136
.
Gunawardana
M. H. M. A. S. V.
,
Sanjaya
K.
,
Atapaththu
K. S. S.
,
Masakorala
K.
&
Widana Gamage
S. M. K.
2021
Development of an algal bloom prediction model for Sri Lankan reservoirs for LANDSAT-8 OLI data
. In:
Proceedings of International Conference on Environmental Governance
,
Colombo, Sri Lanka
, pp.
24
25
.
Kaas
H.
&
Henriksen
P.
2000
Saxitoxins (PSP toxins) in Danish lakes
.
Water Res.
34
,
2089
2097
.
Kabbara
N.
,
Benkhelil
J.
,
Awad
M.
&
Barale
V.
2008
Monitoring water quality in the coastal area of Tripoli (Lebanon) using high-resolution satellite data
.
ISPRS J. Photogramm. Remote Sens.
63
(
5
),
488
495
.
doi:10.1016/j.isprsjprs.2008.01.004
.
Keith
D. J.
,
Milstead
B.
,
Walker
H. A.
,
Snook
H.
,
Szykman
J.
,
Wusk
M.
,
Kagey
L.
,
Howell
C.
,
Mellanson
C.
&
Drueke
C.
2012
Trophic status, ecological condition, and cyanobacteria risk of New England lakes and ponds based on aircraft remote sensing
.
J. Appl. Remote Sens.
6
(
1
),
063577
.
doi:10.1117/1.JRS.6.063577
.
Keith
D. J.
,
Rover
J.
,
Green
J.
,
Zalewsky
B.
,
Charpentier
M.
,
Thursby
G.
&
Bishop
J.
2018
Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager
.
Int. J. Remote Sens.
39
(
9
),
2818
2846
.
doi:10.1080/01431161.2018.1430912
.
Kokociński
M.
,
Mankiewicz-Boczek
J.
,
Jurczak
T.
,
Spoof
L.
,
Meriluoto
J.
,
Rejmonczyk
E.
,
Hautala
H.
,
Vehniäinen
M.
,
Pawełczyk
J.
&
Soininen
J.
2013
Aphanizomenon gracile (Nostocales), a cylindrospermopsin-producing cyanobacterium in Polish lakes
.
Environ. Sci. Pollut. Res.
20
,
5243
5264
.
Komárek
J.
&
Komárková
J.
2006
Diversity of Aphanizomenon-like cyanobacteria
.
Fottea
6
(
1
),
1
32
.
Kudela
R. M.
,
Palacios
S. L.
,
Austerberry
D. C.
,
Accorsi
E. K.
,
Guild
L. S.
&
Torres-Perez
J.
2015
Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters
.
Remote Sens. Environ.
167
,
196
205
.
doi:10.1016/j.rse.2015.01.025
.
Kutser
T.
,
Paavel
B.
,
Verpoorter
C.
,
Ligi
M.
,
Soomets
T.
,
Toming
K.
&
Casal
G.
2016
Remote sensing of black lakes and using 810 nm reflectance peak for retrieving water quality parameters of optically complex waters
.
Remote Sens.
8
,
497
.
Ledreux
A.
,
Thomazeau
S.
,
Catherine
A.
,
Duval
C.
,
Yéprémian
C.
,
Marie
A.
&
Bernard
C.
2010
Evidence for saxitoxins production by the cyanobacterium Aphanizomenon gracile in a French recreational water body
.
Harmful Algae
10
,
88
97
.
Lepistö
L.
,
Rapala
J.
,
Lyra
C.
,
Berg
K. A.
,
Erkomaa
K.
&
Issakainen
J.
2005
Occurrence and toxicity of cyanobacterial blooms dominated by Anabaena lemmermannii P. Richter and Aphanizomenon spp. in boreal lakes in 2003
.
Algological Studies: Int. J. Phycol. Res.
117
,
315
328
.
https://doi.org/10.1127/1864-1318/2005/0117-0315
.
Ligi
M.
,
Kutser
T.
,
Kallio
K.
,
Attila
J.
,
Koponen
S.
,
Paavel
B.
,
Soomets
T.
&
Reinart
A.
2017
Testing the performance of empirical remote sensing algorithms in the Baltic Sea waters with modelled and in situ reflectance data
.
Oceanologia
59
(
1
),
57
68
.
doi:10.1016/j.oceano.2016.08.002
.
Liu
Y.
,
Chen
W.
,
Li
D.
,
Shen
Y.
,
Li
G.
&
Liu
Y.
2006
First report of aphantoxins in China – waterblooms of toxigenic Aphanizomenon flos-aquae in Lake Dianchi
.
Ecotoxicol. Environ. Saf.
65
(
1
),
84
92
.
doi:10.1016/j.ecoenv.2005.06.012
.
Maniyar
C. B.
,
Kumar
A.
&
Mishra
D. R.
2022
Continuous and synoptic assessment of Indian inland waters for harmful algae blooms
.
Harmful Algae.
111
,
102160
.
doi:10.1016/j.hal.2021.102160
.
Matthews
M. W.
,
Bernard
S.
&
Winter
K.
2010
Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS
.
Remote Sens. Environ.
114
(
9
),
2070
2087
.
doi:10.1016/j.rse.2010.04.013
.
Mayo
M.
,
Gitelson
A.
,
Yacobi
Y. Z.
&
Ben-avraham
Z.
1995
Chlorophyll distribution in Lake Kinneret determined from Landsat Thematic Mapper data
.
Int. J. Remote Sens.
16
(
1
),
175
182
.
doi:10.1080/01431169508954386
.
Messineo
V.
,
Melchiorre
S.
,
Di Corcia
A.
,
Gallo
P.
&
Bruno
M.
2010
Seasonal succession of Cylindrospermopsis raciborskii and Aphanizomenon ovalisporum blooms with cylindrospermopsin occurrence in the volcanic Lake Albano, Central Italy
.
Environ. Toxicol.
25
,
18
27
.
Moran
M. S.
,
Jackson
R. D.
,
Slater
P. N.
&
Teillet
P. M.
1992
Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output
.
Remote Sens. Environ.
41
(
2-3
),
169
184
.
doi:10.1016/0034-4257(92)90076-V
.
Moses
W. J.
,
Gitelson
A. A.
,
Berdnikov
S.
&
Povazhnyy
V.
2009
Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data-successes and challenges
.
Environ. Res. Lett.
4
,
045005
.
doi:10.1088/1748-9326/4/4/045005
.
Moses
W. J.
,
Gitelson
A. A.
,
Berdnikov
S.
,
Saprygin
V.
&
Povazhnyi
V.
2012
Operational MERIS-based NIR-red algorithms for estimating chlorophyll-a concentrations in coastal waters – The Azov Sea case study
.
Remote Sens. Environ.
121
,
118
124
.
doi:10.1016/j.rse.2012.01.024
.
Moustaka-Gouni
M.
,
Hiskia
A.
,
Genitsaris
S.
,
Katsiapi
M.
,
Manolidi
K.
,
Zervou
S.
,
Christophoridis
C.
,
Triantis
T. M.
,
Kaloudis
T.
&
Orfanidis
S.
2017
First report of Aphanizomenon favaloroi occurrence in Europe associated with saxitoxins and a massive fish kill in Lake Vistonis, Greece
.
Mar. Fresh. Res.
68
,
793
800
.
Paine
E. C.
,
Slonecker
E. T.
,
Simon
N. S.
,
Rosen
B. H.
,
Resmini
R. G.
&
Allen
D. W.
2018
Optical characterization of two cyanobacteria genera, Aphanizomenon and Microcystis, with hyperspectral microscopy
.
J. Appl. Remote Sens.
12
,
036013
.
Petus
C.
,
Chust
G.
,
Gohin
F.
,
Doxaran
D.
,
Froidefond
J. M.
&
Sagarminaga
Y.
2010
Estimating turbidity and total suspended matter in the Adour River plume (South Bay of Biscay) using MODIS 250-m imagery
.
Cont. Shelf Res.
30
,
379
392
.
Poddar
S.
,
Chacko
N.
&
Swain
D.
2019
Estimation of chlorophyll-a in Northern Coastal Bay of Bengal using Landsat-8 OLI and Sentinel-2 MSI sensors
.
Front. Mar. Sci.
6
,
598
.
doi: 10.3389/fmars.2019.00598
.
Pompêo
M.
,
Moschini-Carlos
V.
,
Bitencourt
M. D.
,
Sòria-Perpinyà
X.
,
Vicente
E.
&
Delegido
J.
2021
Water quality assessment using Sentinel-2 imagery with estimates of chlorophyll a, Secchi disk depth, and cyanobacteria cell number: the Cantareira System reservoirs (São Paulo, Brazil)
.
Environ. Sci. Pollut. Res. Int.
28
(
26
),
34990
35011
.
doi: 10.1007/s11356-021-12975-x
.
Ruddick
K. G.
,
Ovidio
F.
&
Rijkeboer
M.
2000
Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters
.
Appl. Optics
39
(
6
),
897
912
.
doi:10.1364/AO.39.000897
.
Ruiz-Verdú
A.
,
Simis
S. G. H.
,
de Hoyos
C.
,
Gons
H. J.
&
Peña-Martínez
R.
2008
An evaluation of algorithms for the remote sensing of cyanobacterial biomass
.
Remote Sens. Environ.
112
(
11
),
3996
4008
.
doi:10.1016/j.rse.2007.11.019
.
Schalles
J. F.
&
Yacobi
Y. Z.
2000
Remote detection and seasonal patterns of phycocyanin, carotenoid and chlorophyll pigments in eutrophic waters
.
Archiv fur Hydrobiologie Special Issues Advances in Limnology
55
,
153
168
.
Sebastiá-Frasquet
M.-T.
,
Aguilar-Maldonado
J.-A.
,
Herrero-Durá
I.
,
Santamaría-del-Ángel
E.
,
Morell-Monzó
S.
&
Estornell
J.
2020
Advances in the monitoring of algal blooms by remote sensing: a bibliometric analysis
.
Appl. Sci.
10
,
7877
.
doi:10.3390/app10217877
.
Shaw
G. R.
,
Sukenik
A.
,
Livne
A.
,
Chiswell
R. K.
,
Smith
M. J.
,
Seawright
A. A.
,
Norris
R. L.
,
Eaglesham
G. K.
&
Moore
M. R.
1999
Blooms of the cylindrospermopsin containing cyanobacterium, Aphanizomenon ovalisporum (Forti), in newly constructed lakes, Queensland, Australia
.
Environ. Toxicol.
14
,
167
177
.
Sòria-Perpinyà
X.
,
Vicente
E.
,
Urrego
P.
,
Pereira-Sandoval
M.
,
Ruíz-Verdú
A.
,
Delegido
J.
,
Soria
J. M.
&
Moreno
J.
2020
Remote sensing of cyanobacterial blooms in a hypertrophic lagoon (Albufera of València, Eastern Iberian Peninsula) using multitemporal Sentinel-2 images
.
Sci. Total Environ.
698
,
134305
.
doi:10.1016/j.scitotenv.2019.134305
.
Šulčius
S.
,
Pilkaitytė
R.
,
Mazur-Marzec
H.
,
Kasperovičienė
J.
,
Ezhova
E.
,
Błaszczyk
A.
&
Paškauskas
R.
2015
Increased risk of exposure to microcystins in the scum of the filamentous cyanobacterium Aphanizomenon flos-aquae accumulated on the western shoreline of the Curonian Lagoon
.
Marine Pollut. Bullet.
99
,
264
270
.
Sutherland
J. W.
,
Turcotte
R. J.
,
Molden
E.
,
Moriarty
V.
,
Kelly
M.
,
Aubel
M.
&
Foss
A.
2021
The detection of airborne anatoxin-a (ATX) on glass fiber filters during a harmful algal bloom
.
Lake Reserv. Manage.
37
,
113
119
.
Tham
T. T.
,
Hung
T. L.
,
Thuy
T. T.
,
Mai
V. T.
,
Trinh
L. T.
,
Hai
C. V.
&
Minh
T. B.
2022
Assessment of some water quality parameters in the Red River downstream, Vietnam by combining field monitoring and remote sensing method
.
Environ. Sci. Pollut. Res.
29
,
41992
42004
.
Urbanski
J. A.
,
Wochna
A.
,
Bubak
I.
,
Grzybowski
W.
,
Lukawska-Matuszewska
K.
,
Łącka
M.
,
Śliwińska
S.
,
Wojtasiewicz
B.
&
Zajączkowski
M.
2016
Application of Landsat 8 imagery to regional-scale assessment of lake water quality
.
Int. J. Appl. Earth Obs. Geoinf.
51
,
28
36
.
doi:10.1016/j.jag.2016.04.004
.
Wagle
N.
,
Pote
R.
,
Shahi
R.
,
Lamsal
S.
,
Thapa
S.
&
Acharya
T.
2019
Estimating and mapping Chlorophyll-a concentration of Phewa Lake of Kaski district using landsat imagery
.
ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci.
IV
(
5/W2
),
127
132
.
Wood
S. A.
,
Rasmussen
J. P.
,
Holland
P. T.
,
Campbell
R.
&
Crowe
A. L.
2007
First report of the cyanotoxin Anatoxin-A from Aphanizomenon Issatschenkoi (Cyanobacteria)
.
J. Phycol.
43
,
356
365
.
Wörmer
L.
,
Cirés
S.
,
Agha
R.
,
Verdugo
M.
,
De Hoyos
C.
&
Quesada
A.
2011
First detection of cyanobacterial PSP (paralytic shellfish poisoning) toxins in Spanish freshwaters
.
Toxicon
57
,
918
921
.
Yadav
S.
,
Yamashiki
Y.
,
Susaki
J.
,
Yamashita
Y.
&
Ishikawa
K.
2019
Chlorophyll estimation of lake water and coastal water using Landsat-8 and Sentinel-2A satellite
.
Int. Arch. Photogram. Remote Sens. Spatial Info. Sci.
12
(
14
),
77
82
.
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