Detection Of Blooms Research Articles

Deep-Learning-Based Retrieval of an Orange Band Sensitive to Cyanobacteria for Landsat-8/9 and Sentinel-2

The lack of an orange band (∼620 nm) in the imagery captured by Landsat-8/9 and Sentinel-2 restricts the detection and quantification of harmful cyanobacterial blooms in inland waters. A recent study suggested the retrieval of orange remote sensing reflectance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_rs</i> (620), by assuming green, red, and panchromatic (Pan) bands of Landsat-8 as predictors through a linear model. However, this method is not applicable to Sentinel-2 imagery lacking a Pan band. Moreover, the Pan-based method does not account for the nonlinear relationships among the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_rs</i> data at different wavelengths. We propose a deep-learning model called DOABLE-Net (Deep OrAnge Band LEarning Network) that leverages a large training set of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_rs</i> data from radiative transfer simulations and in-situ measurements. The proposed DOABLE-Net is structured as five fully-connected layers and implemented either with or without the Pan band as an input feature, which only the latter applies to Sentinel-2. DOABLE-Net provided more accurate and robust retrievals than the Pan-based method on a wide range of independent validation datasets. The performance of DOABLE-Net on Landsat-8/9 data was minimally impacted by including the Pan band. The results from Sentinel-2 data analysis also confirmed that the DOABLE-Net provides promising results without using a Pan band.

Classification With Unbalanced Samples by Self-Sampling and Semicorrelated Co-Training— An Application to Algal Bloom Detection

Classification With Unbalanced Samples by Self-Sampling and Semicorrelated Co-Training— An Application to Algal Bloom Detection

Spatio–Temporal Water Quality Determines Algal Bloom Occurrence and Possibly Lesser Flamingo (Phoeniconaias minor) Presence in Momella Lakes, Tanzania

Eutrophication and algal blooms have sparked worldwide concern because of their widespread effects on water-dependent species. Harmful algal blooms can cause fatal effects to lesser flamingos (Phoeniconaias minor), obligatory filter feeders and vital bio-indicators in soda lakes. Thus, early detection of algal blooms and potential indicators in water quality is critical, but general tools are lacking in eastern African soda lakes. We monitored algal biomass changes and related water physico–chemical variables for 12 consecutive months in the lakes Big Momella and Rishateni in northern Tanzania. We used chlorophyll-a to measure algal biomass and quantified water physico–chemical variables that might influence algae growth. We also monitored lesser flamingo numbers to understand trends across the year and according to algal bloom occurrence. Algal biomass was strongly related to water nitrogen (r = 0.867; p &lt; 0.001) and phosphorus (r = 0.832; p &lt; 0.001). Monthly patterns showed significant differences in water quality and algal biomass (F = 277, p &lt; 0.001) but not across sampling sites (F = 0.029, p = 0.971). Lesser flamingo numbers seemed to be related to algal biomass at Lake Big Momella (r = 0.828; p &lt; 0.001) and shortly after algal biomass peaked high (i.e., March and April 2021), flamingo numbers declined. Lake Rishateni showed similar patterns. Our findings can provide a basis towards understanding the factors contributing to temporal changes in lesser flamingo abundance due to spatio–temporal water quality variations, which is important for optimising conservation efforts for the species in these unique Momella lakes.

A Fully Unsupervised Machine Learning Framework for Algal Bloom Forecasting in Inland Waters Using MODIS Time Series and Climatic Products

Progressively monitoring water quality is crucial, as aquatic contaminants can pose risks to human health and other organisms. Machine learning can support the development of new effective tools for water monitoring, including the detection of algal blooms from remotely sensed image series. Therefore, in this paper, we introduce the Algal Bloom Forecast (ABF) framework, a fully automated framework for algal bloom prediction in inland water bodies. Our approach combines machine learning, time series of remotely sensed products (i.e., Moderate-Resolution Imaging Spectroradiometer (MODIS) images), environmental data and spectral indices to build anomaly detection models that can predict the occurrence of algal bloom events in the posterior period. Our assessments focused on the application of the ABF framework equipped with the support vector machine (SVM), random forest (RF), and long short-term memory (LSTM) methods, the outcomes of which were compared through different evaluation metrics such as global accuracy, the kappa coefficient, F1-Score and R2-Score. Case studies covering the Erie (USA), Chilika (India) and Taihu (China) lakes are presented to demonstrate the effectiveness and flexibility of our learning approach. Based on comprehensive experimental tests, we found that the best algal bloom predictions were achieved by bringing together the ABF design with the RF model.

Long-term spatial variation of algal blooms extracted using the U-net model from 10 years of GOCI imagery in the East China Sea

Long-term satellite missions could help to provide insights into spatial and temporal variations in algal blooms. However, the traditional reflectance-based method has limitations in regards to determining the available threshold for algal bloom detection among the time-varying observation conditions. In terms of extracting useful information from long-term data series precisely and efficiently, the deep learning method has shown its superiority over traditional algorithms in batch data processing. In this study, a U-net model for algal bloom extraction along the coast of the East China Sea was developed using GOCI images. The U-net model was trained with two different datasets that were constructed with six-band channels (all visible bands from GOCI imagery) and RGB-band channels (bands of 443, 555, and 680 nm from GOCI imagery). The quantitative assessment from the U-net models suggests that the U-net model trained with the six-band channel datasets outperformed the RGB-band channel datasets, with increases of 23.6%, 18.1%, and 12.5% in terms of accuracy, precision, and F-score, respectively. The validation map derived from the U-net model trained with six-band channel datasets also showed considerable matching with the ground-truth maps. By using the U-net model, the occurrence of algal blooms was automatically extracted from GOCI images. A 10-year time series of GOCI data collected between 2011 and 2020 was derived using an output-trained U-net model to explore spatial variation along the coast of the ECS. It was found that the most affected areas of the algal blooms varied by year, but were mainly located in the Zhoushan and Zhejiang coasts. Additionally, by performing principal component analysis on the daily meteorological data during April and August 2011–2020, factors related to algal bloom occurrence were discussed.

Effects of satellite temporal resolutions on the remote derivation of trends in phytoplankton blooms in inland waters

Satellite temporal resolutions are vital for the timely detection of phytoplankton blooms in inland waters using satellite observations due to rapid phytoplankton migration and reproduction. However, the effects of satellite temporal resolutions on the remote derivation of trends in phytoplankton have not yet been assessed. To address this issue, we first used the daily Moderate–resolution Imaging Spectroradiometer (MODIS)–derived phytoplankton blooms in a typical bloom–frequent lake (Lake Taihu, China) between 2001 and 2020 to simulate a series of phytoplankton bloom data sets with temporal resolutions of 2 days to 30 days. We then revealed the differences in the trends of the phytoplankton blooms and their responses to the climatic factors derived from these data sets; we subsequently quantified the relationships between the differences and the temporal resolutions at daily, monthly, and yearly scales. We found that there were significant inconsistencies in the trends in the phytoplankton bloom areas derived from the simulated series data sets with different temporal resolutions; the relations of the MODIS–derived phytoplankton bloom areas to those derived from the simulated series data sets became weaker as the temporal resolutions of the simulated series data sets decreased; the responses of the phytoplankton blooms to the climatic factors quantified by the simulated series data sets were also varied with the temporal resolutions at daily, monthly, and yearly scales. These results from simulated data were also validated by satellite data of Lake Taihu, Lake Chaohu, and Lake Dianchi, three eutrophic lakes in China. In brief, our results cautioned against the use of satellite data with low temporal resolutions to reveal long–term phytoplankton–bloom dynamics.

Mapping algal bloom dynamics in small reservoirs using Sentinel-2 imagery in Google Earth Engine

Freshwater algal blooms have caused ecological damage and public health concerns throughout the world. Monitoring such blooms via in situ sampling is both costly and time-consuming, and satellite imagery provides a rapid and relatively inexpensive way to supplement these techniques. Sentinel-2 MultiSpectral Imager data have effectively detected chlorophyll-a, a proxy for algal biomass, in large bodies of water, but few studies have shown the applicability in small (<10 km2) reservoirs, which are critically important for aquatic species, drinking water, irrigation, cultural activities, and recreation. This study provides a test of the use of Sentinel-2 imagery in Google Earth Engine for algal bloom detection in two small freshwater reservoirs in northern California, USA, from October 2015 to December 2020. Google Earth Engine’s cloud computing allows for the analysis of extensive datasets and time series, expanding the capacity to analyze the spatial and temporal heterogeneity of floating algal blooms. Here we analyzed four spectral indices - Normalized Difference Vegetation Index (NDVI), Normalized Difference Chlorophyll Index (NDCI), B8AB4, and B3B2 - to retrieve chlorophyll-a data for algal bloom identification in two highly dynamic freshwater systems. We assessed the relationship between spectral indices and monthly in situ water samples that were collected at three sites within the reservoirs using cubic polynomial regression equations. NDCI, which leverages the red-edge wavelength, most accurately identified chlorophyll-a across all study sites (highest adjusted R2 = 0.84, lowest RMSE = 0.02 µg/l), followed by NDVI. We demonstrate that Sentinel-2 imagery can capture greater spatial and temporal heterogeneity of algal blooms than typical in situ sampling. This suggests that remote sensing may be an increasingly important tool in monitoring algal bloom dynamics in small reservoirs and other aquatic environments.

Remote sensing of the cyanobacteria life cycle: A mesocosm temporal assessment of a Microcystis sp. bloom using coincident unmanned aircraft system (UAS) hyperspectral imagery and ground sampling efforts

Remote sensing technologies offer a consistent, spatiotemporal approach to assess water quality, which includes the detection, monitoring, and forecasting of cyanobacteria harmful algal blooms. In this study, a series of ex-situ mesoscale experiments were conducted to first develop and then monitor a Microcystis sp. bloom using a hyperspectral sensor mounted on an unmanned aircraft system (UAS) along with coincident ground sampling efforts including laboratory analyses and in-situ field probes. This approach allowed for the simultaneous evaluation of both bloom physiology (algal growth stages/life cycle) and data collection method on the performance of a suite of 41 spectrally-derived water quality algorithms across three water quality indicators (chlorophyll a, phycocyanin and turbidity) in a controlled environment. Results indicated a strong agreement between Lab and Field-based methods for all water quality indicators independent of growth phase, with regression R2-values above 0.73 for mean absolute percentage error (MAPE) and 0.87 for algorithm R2 values. Three of the 41 algorithms evaluated met predetermined performance criteria (MAPE and algorithm R2 values); however, in general, algal growth phase had a substantial impact on algorithm performance, especially those with blue and violet wave bands. This study highlights the importance of co-validating sensor technologies with appropriate ground monitoring methods to gain foundational knowledge before deploying new technologies in large-scale field efforts.

Emergent marine toxins risk assessment using molecular and chemical approaches.

Cyanobacteria harmful blooms represent a deviation to the normal equilibrium in planktonic communities involving a rapid and uncontrolled growth. Owing to the capacity to produce toxins as secondary metabolites, cyanobacteria may cause huge economic losses in the fishing and aquaculture industries and poisoning incidents to humans due to their accumulation in the food chain. The conditions which promote toxic blooms have not yet been fully understood, but climate change and anthropogenic intervention are pointed as significant factors. For the detection of toxins in edible marine organisms, the establishment of international regulations and compulsory surveillance has been probed as exceptionally effective. However, not regulation nor monitoring have been settled concerning emergent marine toxins. In the light of this scenario, it becomes essential to apply fast and reliable surveillance methodologies for the early detection of cyanobacterial blooms as well as the occurrence of emergent marine toxins. Shotgun metagenomic sequencing has potential to become a powerful diagnostic tool in the fields of food safety and One Health surveillance. This culture‐independent approach overcomes limitations of traditional microbiological techniques; it allows a quick and accurate assessment of a complex microbial community, including quantitative identification and functional characterisation, in a single experiment. In the framework of the EU‐FORA fellowship, with the final goal of evaluate metagenomics as a promising risk assessment tool, the fellow worked on the development of an innovative workflow through state‐of‐the‐art molecular and chemical analytical procedures. This work programme aims to evaluate the occurrence of emergent marine toxins and the producing organisms in Cabo Verde coastal cyanobacteria blooms. Our results show the outstanding potential of a holistic metagenomic approach for the risk assessment of emergent marine toxins and the producing organisms. Additionally, we have also highlighted its value for the identification and evaluation of secondary metabolites as natural bioactive compounds with biotechnological and industrial interest.

Frame Rhythm: A new cost-effective approach for semi-automatic microalgal imaging and enumeration

Quantifying cell abundance is of paramount importance for monitoring the quality and production of microalgal growth, as well as for early detection of harmful algal blooms (HAB) events, which cause economic losses and societal impacts around the globe. Although manual quantification using counting chambers and microscopy is a well-established method to count microalgae, it is very time-consuming and requires an advanced level of expertise. In last decades, a variety of commercial instruments has emerged as an alternative to automate this process. However, their high cost and the need for specialized technical support hamper their dissemination, particularly in developing countries. This study presents a simple, fast, reliable and cost-effective method for semi-automatic microalgae quantification. The so-called Frame Rhythm (FR) method relies on a handcrafted simple counting chamber imaged with a digital camera coupled to a regular microscope. An in-house software subsequently detects and quantifies microalgal cells in an image sequence, preventing repetitive counts and static noise. The proposed approach was validated by comparison with well-established methods, while tracking different growth phases of the chlorophycean Tetraselmis gracilis, a widely cultured microalga. Results indicated a strong positive correlation (r = 0.99, R2 = 98.31%, p ≤ 0.01) and high agreement between methods, as well as low intra (1–11%) and inter-operator (2–12%) variance for the proposed technique. The FR method provided a reduction of at least 14× in sample processing time in relation to traditional microscopy, at a much lower implementation and maintenance cost compared to commercially available automatic and semi-automatic counting equipment. The new method represents an affordable alternative to acquire microalgal abundance data with a similar degree of accuracy compared to traditional counting chambers and sophisticated benchtop instrumentation.

Modelling Phytoplankton Behaviour in the North and Irish Sea with Transformer Networks

Climate change will affect how water sources are managed and monitored. Continuous monitoring of water quality is crucial to detect pollution, to ensure that various natural cycles are not disrupted by anthropogenic activities and to assess the effectiveness of beneficial management measures taken under defined protocols. One such disruption is algal blooms in which population of phytoplankton increase rapidly affecting biodiversity in marine environments. The frequency of algal blooms will increase with climate change as it presents favourable conditions for reproduction of phytoplankton. Machine learning has been used for early detection of algal blooms previously, with the focus mostly on single closed bodies of water in Far East Asia with short time ranges. In this work, we study four locations around the North Sea and the Irish Sea with different characteristics predicting activity with longer time-spans and explaining the importance of the input with regard to the output of the prediction model. This work aids domain experts to monitor potential changes to the ecosystem over longer time ranges and to take action when necessary.

HY-1C ultraviolet imager captures algae blooms floating on water surface

Some species of algae such as cyanobacteria can vertically migrate through water during a day, which is a notable floating feature of harmful algae blooms. To date, this process has been observed and quantified using visible and near-infrared (VNIR) bands from spaceborne sensors with high temporal resolution (i.e., Geostationary Ocean Color Imager; GOCI). In this study, we conducted an in-situ measurement at Taihu Lake in China to investigate the ultraviolet (UV) reflection spectra of floating cyanobacteria blooms, and identified that they have significant UV reflection features (higher than that of background water) associated with their floating status. This has been demonstrated using spaceborne UV images at both 355 and 385 nm from the Ultraviolet Imager (UVI) onboard Haiyang-1C (HY-1C) of China. Compared with synchronous optical images from the Chinese Ocean Color and Temperature Scanner (COCTS), we found that UVI has a special ability to distinguish cyanobacteria floating on water surface. Additionally, the intensity of the UV signals obtained is positively correlated with the cyanobacterial equivalent density. Ultraviolet remote sensing, therefore, can work as a new approach for the detection of harmful algae blooms and help determine the floating status of them, which deserves further research.

Investigating optimal unmanned aircraft systems flight plans for the detection of marine ingress

From the shutting down of coastal tourism industries, the mass destruction of aquaculture, to the clogging of power station water intakes, marine ingress events have the potential to cause widespread disruption along our coastlines. To gain the ability to respond to such events, efforts are being made to advance the understanding of bloom events which predominantly present as large aggregations of jellyfish, or detached aquatic macroalgaes in the water column. This paper investigates the optimal flight search patterns with a focus on marine ingress bloom detection from unmanned aircraft systems (UAS). The detection performance of four flight search patterns are examined against five different bloom shapes. Monte-Carlo simulations are deployed to assess probable performance of flight search pattern against variable bloom shapes. A total of 50,000 simulated flights were conducted, offering a maximum of 500 million marine ingress objects for possible detection. A two phased flight approach is proposed, with first phase flights conducted as area search strategies, and second phase flights as datum searches for scenarios where some information of possible bloom location is available. Parallel sweep was found to be the best performing generalist flight search pattern, closely followed by the phase two search pattern expanding square. Crossing barrier was found to be competitive but appeared to lend itself towards specific detection scenarios with sector search being a consistently poor performing flight search pattern. This paper also investigates the comparative performance of visual line of sight (VLOS), extended visual line of sight (EVLOS), and beyond visual line of sight (BVLOS) operations. Increase of total survey area was found to increase bloom detection frequency, with BVLOS operations the highest performer successfully increasing bloom detection by a factor of 3.7. This paper exhibits the first assessment of flight search patterns within the context of drone-based detection of marine ingress bloom events. This should facilitate the development of an early warning detection system that can provide reliable warning to coastal industries prior to a marine ingress event occurring.

Environmental Impact on Harmful Species Pseudo-nitzschia spp. and Phaeocystis globosa Phenology and Niche

Global environmental change modifies the phytoplankton community, which leads to variations in their phenology and potentially causes a temporal mismatch between primary producers and consumers. In parallel, phytoplankton community change can favor the appearance of harmful species, which makes the understanding of the mechanisms involved in structuring phytoplankton ecological niches paramount for preventing future risk. In this study, we aimed to assess for the first time the relationship between environmental conditions, phenology and niche ecology of harmful species Phaeocystis globosa and the complex Pseudo-nitzschia along the French coast of the eastern English Channel. A new method of bloom detection within a time-series was developed, which allowed the characterization of 363 blooms by 22 phenological variables over 11 stations from 1998 to 2019. The pairwise quantification of asymmetric dependencies between the phenological variables revealed the implication of different mechanisms, common and distinct between the taxa studied. A PERMANOVA helped to reveal the importance of seasonal change in the environmental and community variables. The Outlying Mean and the Within Outlying Mean indexes allowed us to position the harmful taxa niche among the rest of community and quantify how their respective phenology impacted the dynamic of their subniches. We also discussed the possible hypothesis involved and the perspective of predictive models.

Noctiluca blooms in the East China Sea bounded by ocean fronts

A recent study demonstrated the possibility of satellite-based detection of surface blooms of the heterotrophic dinoflagellate, red Noctiluca scintillans (RNS). The study further documented RNS bloom patterns in the East China Sea (ECS) between 2000 and 2017. Here, complemented by more recent satellite observations between 2018 and 2020, the 21-year bloom record shows that while bloom distributions vary in different years and annual cumulative bloom footprint shows an increasing trend, the 21-year cumulative bloom footprint is bounded by major ocean fronts such as the Kuroshio Front. Of all observations, 2020 is a critical year to “complete” the footprint as extra discharge from the 2020 Yangtze River flood event, combined with ocean currents, transports the bloom to the most northeast location of the footprint although the riverine influence reaches at least 128°E, well beyond the RNS footprint.

Detection of Surface Water Bodies and Cyanobacterial Blooms using Satellite based Remote Sensing

For many environmental applications, accurate surface water maps are crucial. It is possible to construct surface water maps by merging data from multiple sources. Due to sensor limitations, complex land cover, geography, and atmospheric conditions, accurate surface water estimation using satellite imagery is still a difficult undertaking. In this study, Sentinel 1 images from Google Earth Engine (GEE) was used to derive a high-resolution water mask. To overcome the limitation of visible satellite images which is the presence of clouds, on this research was focused on using Synthetic-Aperture Radar (SAR) sensors, like Sentinel-1, which penetrate clouds and permitting to get a view of the earth surface, even when clouds are presence. To validate the proposed method, it was applied to three study cases which are located in different climate zones in Sri Lanka. In the meanwhile, this research implemented a time series of Sentinel 2 satellite images from GEE to monitor the distribution of cyanobacterial blooms on those lakes. Recent years have seen an increase in the frequency and severity of cyanobacteria in recreational lakes and reservoirs, which has become a serious concern for the public’s health and a serious environmental danger. Monitoring cyanobacterial blooms serves as the foundation for early detection and treatment. Therefore, the results of this study could be used as a benchmark for future environmental monitoring and management of these Sri Lankan lakes and reservoirs.

Remote Sensing Detection of Algal Blooms in a Lake Impacted by Petroleum Hydrocarbons

The purpose of this study was to combine all available information on the state of Lake Pertusillo (Basilicata, Italy), both in the field and published, which included Sentinel-2A satellite data, to understand algal blooms in a lacustrine environment impacted by petroleum hydrocarbons. Sentinel-2A data was retrospectively used to monitor the state of the lake, which is located near the largest land-based oil extraction plant in Europe, with particular attention to chlorophyll a during algal blooms and petroleum hydrocarbons. In winter 2017, a massive dinoflagellate bloom (10.4 × 106 cell/L) of Peridinium umbonatum and a simultaneous presence of hydrocarbons were observed at the lake surface. Furthermore, a recent study using metagenomic analyses carried out three months later identified a hydrocarbonoclastic microbial community specialized in the degradation aromatic and nitroaromatic hydrocarbons. In this study, Sentinel-2A imagery was able to detect the presence of chlorophyll a in the waters, while successfully distinguishing the signal from that of hydrocarbons. Remotely sensed results confirmed surface reference measurements of lacustrine phytoplankton, chlorophyll a, and the presence of hydrocarbons during algal blooms, thereby explaining the presence of the hydrocarbonoclastic microbial community found in the lake three months after the oil spill event. The combination of emerging methodologies such as satellite systems and metagenomics represent an important support methodology for describing complex contaminations in diverse ecosystems.

Optical Detection of Harmful Algal Blooms in the Belgian Coastal Zone: A Cautionary Tale of Chlorophyll c3

Phaeocystis globosa is a nuisance haptophyte species that forms annual blooms in the southern North Sea and other coastal waters. At high biomass concentration, these are considered harmful algal blooms due to their deleterious impact on the local ecosystems and economy, and are considered an indicator for eutrophication. In the last two decades, methods have been developed for the optical detection and quantification of these blooms, with potential applications for autonomous in situ or remote observations. However, recent experimental evidence suggests that the interpretation of the optical signal and its exclusive association with P. globosa may not be accurate. In the North Sea, blooms of P. globosa are synchronous with those of the diatom Pseudo-nitzschia delicatissima, another harmful bloom-forming species with similar pigmentation and optical signature. Here we combine new and published measurements of pigmentation composition and inherent optical properties from pure cultures of several algal and cyanobacterial groups, together with environmental spectroscopy data, to identify the pigments generating the optical signals captured by two established algorithms. We further evaluate the association of those pigments and optical signals with P. globosa. We found that the interpretation of the pigment(s) generating the optical signals were incorrect and that previous methods are not specific to P. globosa, even in the context of the phytoplankton assemblage of the southern North Sea. Additionally, we found that the optical and pigment signatures of Phaeocystis species are part of a broad pigmentation trend across unrelated taxonomic groups related to chlorophyll c3 presence, with important consequences for the interpretation of pigment and optical data. We then develop and evaluate an algorithm to detect this pigmentation pattern with minimal influence of co-occurring species and elaborate general recommendations for the future development of algorithms.

Utility practices and perspectives on monitoring and source control of cyanobacterial blooms

AbstractThirty‐five utilities across the United States (54%), Australia (26%), and Canada (20%) were surveyed to identify their experiences with early warning monitoring and source control of cyanobacteria. All utilities experience pelagic blooms, but only 20% monitor for benthic cyanobacteria. Most utilities (86%) have early warning monitoring programs. However, monitoring frequencies and long analytical turnaround times negatively impacted the effective use of monitoring data for rapid bloom detection and prompt implementation of reactive measures to control blooms/bloom‐related issues. Thus, a tiered monitoring approach is recommended: Tier 1–event detection, Tier 2–cyanobacteria confirmation, and Tier 3–metabolite confirmation. Most utilities (68%) implement source control strategies for cyanobacteria, with algaecides and aeration being the most frequently used (36%). Utilities relied on manufacturer recommendations to design source control strategies, although site‐specific optimization is needed based on water quality/bloom conditions. Control strategies were restricted by source geometry, limited optimization, metabolite generation, and environmental impacts. Successful source control of cyanobacteria was further negatively impacted by external nutrient loading. Therefore, source control strategies should be implemented jointly with external nutrient control initiatives.

Cyanobacterial pigment concentrations in inland waters: Novel semi-analytical algorithms for multi- and hyperspectral remote sensing data

Cyanobacteria are notorious for producing harmful algal blooms that present an ever-increasing serious threat to aquatic ecosystems worldwide, impacting the quality of drinking water and disrupting the recreational use of many water bodies. Remote sensing techniques for the detection and quantification of cyanobacterial blooms are required to monitor their initiation and spatiotemporal variability. In this study, we developed a novel semi-analytical approach to estimate the concentration of cyanobacteria-specific pigment phycocyanin (PC) and common phytoplankton pigment chlorophyll a (Chl a) from hyperspectral remote sensing data. The PC algorithm was derived from absorbance-concentration relationship, and the Chl a algorithm was devised based on a conceptual three-band structure model. The developed algorithms were applied to satellite imageries obtained by the Hyperspectral Imager for the Coastal Ocean (HICO™) sensor and tested in Lake Kinneret (Israel) during strong cyanobacterium Microcystis sp. bloom and out-of-bloom times. The sensitivity of the algorithms to errors was evaluated. The Chl a and PC concentrations were estimated with a mean absolute percentage difference (MAPD) of 16% and 28%, respectively. Sensitivity analysis shows that the influences of backscattering and other water constituents do not affect the estimation accuracy of PC (~2% MAPD). The reliable PC/Chl a ratios can be obtained at PC concentrations above 10 mg m−3. The computed PC/Chl a ratio depicts the contribution of cyanobacteria to the total phytoplankton biomass and permits investigating the role of ambient factors in the formation of a complex planktonic community. The novel algorithms have extensive practical applicability and should be suitable for the quantification of PC and Chl a in aquatic ecosystems using hyperspectral remote sensing data as well as data from future multispectral remote sensing satellites, if the respective bands are featured in the sensor.