Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Remotely sensing harmful algal blooms in the Red Sea

  • Elamurugu Alias Gokul,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

    Affiliation Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia

  • Dionysios E. Raitsos,

    Roles Conceptualization, Supervision, Writing – review & editing

    Affiliations Remote Sensing Group, Plymouth Marine Laboratory (PML), Plymouth, Devon, United Kingdom, Department of Biology, National and Kapodistrian University of Athens (NKUA), Athens, Greece

  • John A. Gittings,

    Roles Writing – review & editing

    Affiliation Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia

  • Abdulsalam Alkawri,

    Roles Data curation

    Affiliation Department of Marine Biology, Hodeidah University, Al Hodeidah, Yemen

  • Ibrahim Hoteit

    Roles Conceptualization, Supervision, Writing – review & editing

    ibrahim.hoteit@kaust.edu.sa

    Affiliation Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia

Remotely sensing harmful algal blooms in the Red Sea

  • Elamurugu Alias Gokul, 
  • Dionysios E. Raitsos, 
  • John A. Gittings, 
  • Abdulsalam Alkawri, 
  • Ibrahim Hoteit
PLOS
x

Abstract

Harmful Algal Blooms (HABs) are of global concern, as their presence is often associated with socio-economic and environmental issues including impacts on public health, aquaculture and fisheries. Therefore, monitoring the occurrence and succession of HABs is fundamental for managing coastal regions around the world. Yet, due to the lack of adequate in situ measurements, the detection of HABs in coastal marine ecosystems remains challenging. Sensors on-board satellite platforms have sampled the Earth synoptically for decades, offering an alternative, cost-effective approach to routinely detect and monitor phytoplankton. The Red Sea, a large marine ecosystem characterised by extensive coral reefs, high levels of biodiversity and endemism, and a growing aquaculture industry, is one such region where knowledge of HABs is limited. Here, using high-resolution satellite remote sensing observations (1km, MODIS-Aqua) and a second-order derivative approach, in conjunction with available in situ datasets, we investigate for the first time the capability of a remote sensing model to detect and monitor HABs in the Red Sea. The model is able to successfully detect and generate maps of HABs associated with different phytoplankton functional types, matching concurrent in situ data remarkably well. We also acknowledge the limitations of using a remote-sensing based approach and show that regardless of a HAB’s spatial coverage, the model is only capable of detecting the presence of a HAB when the Chl-a concentrations exceed a minimum value of ~ 1 mg m-3. Despite the difficulties in detecting HABs at lower concentrations, and identifying species toxicity levels (only possible through in situ measurements), the proposed method has the potential to map the reported spatial distribution of several HAB species over the last two decades. Such information is essential for the regional economy (i.e., aquaculture, fisheries & tourism), and will support the management and sustainability of the Red Sea’s coastal economic zone.

Introduction

Harmful Algal Blooms (HABs) in aquatic ecosystems are characterised by the rapid accumulation of algal biomass and/or the production of toxins and harmful metabolites by certain algal species. HAB events may have a broad range of ecological impacts, including, but not limited to, increased mortality of marine organisms (including fish and mammals), detrimental effects on public health and alterations to ecosystem trophic structure [1].

The Red Sea is a Large Marine Ecosystem (LME) and hosts extended coral reef complexes that support high levels of biological diversity and many endemic species. Coral reefs offer vital ecosystem services such as coastal protection, fisheries, tourism and recreation [29]. Previous research has demonstrated that HAB events may occur throughout the year in different regions of the Red Sea [1018]. The reported species during these HAB events include, but not limited to, Kryptoperidinium foliaceum, Noctiluca scintillans/miliaris, Heterosigma akashiwo, Cochlodinium polykrikoides, Ostreopsis sp. and the cyanobacterium Trichodesmium erythraeum. These phytoplankton species were categorized according to the functional group they belong to. For instance, K. foliaceum, N. scintillans/miliaris, Ostreopsis sp., and C. polykrikoides belong to dinoflagellates, H. akashiwo to raphidophytes and T. erythraeum to cyanobacteria. The aforementioned species were mainly responsible for HAB outbreaks previously reported in the Red Sea and have been occasionally associated with severe fish mortalities over the last two decades [1117].

The first record of the binucleate dinoflagellate K. foliaceum, associated with a diatom endosymbiont, was detected in the coastal waters of Al Salif (southern Red Sea) on 8th May 2013 [11]. N. scintillans/miliaris, a large heterotrophic dinoflagellate, was detected in the coastal waters of Al Hodeidah, in the southern Red Sea during March 2009 [14]. In the south-central coast of Saudi Arabia, large blooms of H. akashiwo and Ostreopsis sp. were observed during May 2010 and February 2012, respectively [15,17]. Winter blooms of the toxic cyanobacteria T. erythraeum, which may manifest as “red tides”, were observed off the coast of western Yemen (southern Red Sea) in December 2012 [12].

Previous studies have demonstrated that HABs can be detected and described using satellite remote sensing, which provides bio-optical measurements at spatial and temporal scales not attainable with traditional in situ approaches [19,20]. To date, several remote sensing algorithms based on bio-optical and ecological methods have been established to discriminate the aforementioned HABs in the global oceans, including regions adjacent to the Red Sea, such as the Arabian Sea, Indian Ocean and Mediterranean Sea [1933]. However, to the best of our knowledge no such technique has been utilized to discriminate these HABs in the Red Sea basin. In this study, we developed a remote sensing model that applies satellite and in situ datasets to a second-order derivative technique to examine the capability of HAB detection in the Red Sea using a remote sensing algorithm. We apply our method on MODIS-Aqua satellite observations and validate our results using field data collected during several sampling HAB campaigns in the Red Sea over the last two decades.

Materials and methods

Satellite data

MODIS-Aqua Level 1A (L1A, calibrated spectral radiance, 1km2 resolution) data were obtained from the NASA Goddard Space Flight Centre’s ocean color data archive system (https://oceancolor.gsfc.nasa.gov/). Daily satellite observations were selected to correspond with time periods when blooms of K. foliaceum, N. scintillans/miliaris, H. akashiwo, Ostreopsis sp., T. erythraeum and C. polykrikoides have been observed during field programs in the Red Sea [1015]. MODIS-Aqua datasets, corresponding to the time periods where previous in situ data are available, were acquired to: a) train the model, and b) validate the model results (Table 1). The satellite data were processed to Level 2 using the new atmospheric correction algorithm incorporated in the SeaDAS software (version 7.5) [19,34]. Data products that were extracted from the Level 2 MODIS-Aqua files included remote sensing reflectance (Rrs) (for wavelengths 412, 443, 488, 531, 547, 667, 678, and 748 nm), and the algal bloom index (ABI) derived Chl-a concentration. The algal bloom index (ABI) algorithm uses normalized water-leaving radiance (nLw) ratios as a key input parameter to estimate Chl-a values associated with HABs, and distinguish algal particles from other non-algal particles in optically complex coastal waters [35,36]. We note that remotely sensed Chl-a observations may be hindered in shallow optically complex case II waters where non-phytoplankton substances such as suspended sediments, particulate matter and/or colored dissolved organic matter (CDOM) do not covary in a predictable manner with Chl-a [8]. However, validation studies based on a suite of univariate statistical tests have shown a reasonable agreement between remotely sensed measurements and independent in situ Chl-a data in the Red Sea [37,38]. We are thus confident in the use of satellite-derived Chl-a concentrations for supporting the investigation of HABs in the Red Sea.

thumbnail
Table 1. Summary of the satellite datasets for the different types of harmful algal blooms (HABs) in the Red Sea.

https://doi.org/10.1371/journal.pone.0215463.t001

In situ data

Surface water samples were collected in the Red Sea during four different field programs. During the field program in November 2012 –August 2013, the dinoflagellate K. foliaceum and the cyanobacterium T. erythraeum were reported in the coastal waters of Al Salif and the coast of Al Hodeida City, in the southern Red Sea, respectively [11,12]. The field sampling program conducted in the fisheries landing center at Al Hodeida reported an intense bloom of N. scintillans/miliaris during March 2009 [14]. The in situ study conducted in the Red Sea off the Al Shouqyq coastline (southern Saudi Arabia) on 27th May 2010 reported a water discoloration caused by H. akashiwo blooms [17]. Catania et al. [15] reported the occurrence of benthic dinoflagellates Ostreopsis sp. on 27 February 2012 at a location off the coasts of Thuwal, Saudi Arabia (Fig 1b and Table 2). The oceanographic measurements that were recorded from the aforementioned field programs included temperature, salinity, and cell counts. The phytoplankton cells were identified and counted using a Sedgwick-Rafter plankton counting chamber. Cells were enumerated and expressed as cells L-1. Spatial matchups between the in situ measurements and satellite-derived HAB observations were acquired by locating the closest 1 km pixel (nearest latitude and longitude) to the in situ sampling location.

thumbnail
Fig 1. Bathymetry and the position of reported harmful algal blooms (HABs) in the Red Sea.

(a) Bathymetry of the Red Sea (acquired from the General Bathymetric Chart of the Oceans (GEBCO_2014 Grid, version 20150318, http://www.gebco.net)). (b) Map showing the in situ sampling locations associated with the different Harmful Algal Blooms (HABs) in the Red Sea; Ost: Ostreopsis sp.; HA: H. akashiwo; KF: K. foliaceum; TE: T. erythraeum; NS: N. scintillans/miliaris (We note that no in situ data were collected during the occurrence of C. polykrikoides blooms in the Red Sea, although their cyst life-stages were identified along the Red Sea, off the south-western coast of Saudi Arabia [16]).

https://doi.org/10.1371/journal.pone.0215463.g001

thumbnail
Table 2. Summary of the different types of harmful algal blooms (HABs) reported by various studies in the Red Sea.

https://doi.org/10.1371/journal.pone.0215463.t002

Approach

Our modeling approach is comprised of two parts: 1) Deriving Rrs from daily MODIS-Aqua data for the different HAB events that were previously reported in the Red sea waters, and 2) Developing an algorithm based on a derivative analysis to detect species-specific HABs in near-surface waters. Fig 2 highlights the major steps involved in the development and application of the model.

thumbnail
Fig 2. Summary of the steps involved in the development of our model.

Flow chart showing the major steps involved in the detection and classification of different Harmful Algal Blooms (HABs) in the Red Sea.

https://doi.org/10.1371/journal.pone.0215463.g002

Training data and spectral analysis

An independent training dataset was established from Rrs measurements of the different HAB events that were previously reported in the coastal and open waters of the Red Sea. Due to lack of in situ Rrs measurements, a set of training samples were constructed from MODIS-Aqua data concurrent with the in situ data. Examples of the Rrs spectra of these HABs in the MODIS-Aqua wavelengths are shown in Fig 3. We note that each HAB species has its unique absorption and backscattering characteristics [19, 3942] (see S1 and S2 Figs for particulate absorption and backscattering spectra). For instance, the raphidophyte H. akashiwo exhibits high Rrs values at 531 and 547 nm, which implies low absorption of pigment concentrations (including fucoxanthin and diadinoxanthin) with high backscattering coefficients at these wavelengths. The dinoflagellate Ostreopsis sp. has a significant Rrs peak at 547 nm, which is due to the minimum absorption by Chl-a and xanthophyll pigments (diadinoxanthin and peridinin) with, in consequence, backscattering by cells remaining the main factor governing Rrs spectra (Fig 3b) [42]. Moldrup and Garm [40] demonstrated that K. foliaceum is characterised by an Rrs peak in the green wavelengths due to the low absorption rate of photopigments associated with this species, including Chl-a and c, fucoxanthin, diadinoxanthin, β-carotene, β-zeacarotene and γ-carotene (Fig 3c). The Rrs spectra of the dinoflagellate N. scintillans/miliaris exhibits two distinct peaks: one at ~ 531 nm and the other at ~ 678 nm (Fig 3d) [41]. The first peak is due to low absorption and high backscattering caused by a green endosymbiont, whilst the latter is due to the combined effect of reduced absorption and enhanced backscattering [19,41]. In Fig 3e, the strong reflectance peak for C. polykrikoides at ~ 547 nm reflects absorption related to the presence of carotenoids [19,22]. As reported in previous studies [19,22,43], the cyanobacterium T. erythraeum contains both Chl-a and biliproteins (allophycocyanins, phycocyanins and phycoerythrins), which have unique characteristic absorption spectra. The peaks at 531 and 547 nm are due to high backscattering of these biliprotein pigments (Fig 3f). The prominent secondary peak in the far-red region of Rrs spectra for all of the aforementioned species (~ 678 nm) is caused by solar-stimulated Chl-a fluorescence (Fig 3a–3f).

thumbnail
Fig 3. Examples of Rrs derived from daily MODIS-Aqua data for the different HAB species in the Red Sea waters.

(a) H. akashiwo (b) Ostreopsis sp. (c) K. foliaceum (d) N. scintillans/miliaris (e) C. polykrikoides (f) T. erythraeum.

https://doi.org/10.1371/journal.pone.0215463.g003

Derivative analysis

Detection of species-specific HABs in the Red Sea depends on the ability to distinguish the unique optical properties associated with each species. Previous studies have demonstrated that band-ratio/difference algorithms can be effectively utilized for mapping HAB events [44,45]. The most commonly used band-ratio/difference algorithms are based on remote sensing reflectance in the blue and green bands (i.e., Rrs 443 nm and Rrs 547 nm) [46,47]. Besides the combination of band-ratio and band-difference algorithms, several other methods have also been used for mapping HABs. One popular technique is the derivative spectra method [48,49]. This method magnifies spectral inflections and enables the detection of small spectral variations. Thus, it can be utilised for the close examination of Rrs spectral patterns. By amplifying Rrs spectral shapes, it highlights features present in the spectra and does not add information that is not already contained in the Rrs spectra [50]. The Rrs spectral shapes of phytoplankton species were examined by calculating the second-order derivative and identifying local maxima (peaks) and minima (troughs) of the Rrs spectrum. The second derivative of Rrs (d2 Rrs (λ)) is numerically evaluated as follows: (1) where Δλ = (λiλi+1) is the finite band resolution.

The current remote sensing model combines both band-ratio/difference algorithms, and second-order derivative analysis, to detect the presence/absence and map the spatial distribution of HAB species. The second derivative of MODIS-Aqua derived Rrs was computed and plotted as a function of wavelength for different HAB species (Fig 4). We note that each HAB species has its unique derivative Rrs spectral signatures. For instance, the derivative Rrs spectra of H. akashiwo blooms exhibits two prominent peaks (~ 488 nm and ~ 667 nm) and two negative troughs (~ 547 nm and ~ 678 nm) (Fig 4a). The dinoflagellate Ostreopsis sp. has two maximum peaks (~ 443 nm and ~ 667nm) and two negative troughs (~547 and ~ 678 nm) of the derivative Rrs spectra (Fig 4b). Two negative troughs at 531 nm and 678 nm are present in the derivative spectra of K. foliaceum along with a maximum peak at 488 nm (Fig 4c). The derivative Rrs spectra of N. scintillans/miliaris exhibits two positive peaks (~ 443 nm and 547 nm) and two negative troughs (~ 531 nm and ~ 678 nm) (Fig 4d). In the derivative spectra of C. polykrikoides, a positive maximum peak is observed at 667 nm and a negative trough at 678 nm (Fig 4e). In addition, T. erythraeum exhibits two maximum peaks at 443 nm and 667 nm along with two minimum troughs (at ~ 547 nm and 678 nm, Fig 4f). Based on the aforementioned local maxima (peaks) and minima (troughs) of d2 Rrs in the entire visible spectrum, we derived different equations (referred to as ‘nequation’ in Table 3) by combining the blue-green band-ratio/difference algorithm and differences in the derivative spectra, to distinguish various HAB species.

thumbnail
Table 3. Different sets of nequations for each HAB species based on the band-ratios/difference algorithm and difference in their derivative spectra.

https://doi.org/10.1371/journal.pone.0215463.t003

thumbnail
Fig 4. Examples of second-order derivative Rrs from daily MODIS-Aqua data for the different HABs in the Red Sea waters.

(a) H. akashiwo (b) Ostreopsis sp. (c) K. foliaceum (d) N. scintillans/miliaris (e) C. polykrikoides (f) T. erythraeum.

https://doi.org/10.1371/journal.pone.0215463.g004

Remote sensing parameters for monitoring HABs in the Red Sea

Several different parameters have been used to investigate occurrence of HABs, including false color composite imagery, satellite-derived Chl-a maps, and spectral analysis-based bloom maps [51,52]. These parameters have enabled us to detect the presence/absence of HABs and map the spatial extent of different HAB species that were previously reported by various field programs in the Red Sea. A detailed synopsis of the HAB analysis is provided as follows.

The first step of this analysis was to visually interpret the abnormality of ocean color in the Red Sea during HAB events. To achieve this, we processed false-color composite MODIS images by combining Rrs in the NIR, green and blue wavelength bands (also known as the RGB band). Hu et al. [53] demonstrated that false color imagery can easily distinguish between dark features caused by high absorption of light related to the presence of Chl-a, and bright features caused by non-pigment materials such as sediment, corals and shallow bathymetry. Secondly, to investigate Chl-a concentrations during a HAB event, we examined satellite-derived Chl-a generated using the ABI algorithm. Gokul and Shanmugam [19] demonstrated that satellite-derived ABI Chl-a can effectively estimate Chl-a values associated with HABs, and discriminate algal bloom patches from other non-algal particles in optically complex coastal waters. In addition, based on the satellite derived Rrs spectra (training dataset) and its second-order derivatives of different phytoplankton functional types (PFTs), we produced bloom maps in order to detect the presence/absence and map the spatial distribution of different phytoplankton species that were known for HAB outbreaks in the Red Sea (see Fig 2). Finally, in situ measurements of the different phytoplankton cell counts (related to these HAB species) were used for the validation of the model results.

Results

The proposed remote sensing algorithm was applied to daily MODIS images that were selected during HAB events previously reported in the coastal and open waters of the Red sea (see Materials and methods). A scatterplot of the output from nequations versus the Rrs difference (Rrs 678−Rrs 443) is shown in Fig 5. Based on the derivative analysis, all these types of HABs were shown to be well clustered and distinguishable. For convenience, we categorized these different types of HAB species according to their regional locations in the Red Sea. For instance, Ostreopsis sp. and H. akashiwo blooms were detected in the central Red Sea, whilst the remaining blooms (K. foliaceum, N. scintillans/miliaris, T. erythraeum and C. polykrikoides) occurred in the southern Red Sea. The spatial distributions of these HAB events in the central and southern Red Sea are discussed in the following section.

thumbnail
Fig 5. Distinct cluster of different HAB species (based on training datasets) in the Red Sea waters.

Ost- Ostreopsis sp.; NS- N. scintillans/miliaris; TE- T. erythraeum; KF-K. foliaceum; HA-H. akashiwo; CP- C. polykrikoides.

https://doi.org/10.1371/journal.pone.0215463.g005

Regional examples

Central Red Sea.

To detect blooms of Ostreopsis sp. and H. akashiwo, we utilised high-resolution MODIS-Aqua images taken on 27th February 2012 and 27th May 2010 respectively, over the central Red Sea, along the south-central coast of Saudi Arabia. Figs 6 and 7 illustrate how the presence of these two blooms may be revealed by false color imagery, satellite-derived Chl-a and bloom maps generated using the remote sensing model. For instance, the bright features observed in Figs 6 and 7b were probably caused by suspended sediments or the presence of coral reefs. Such features limit the utility of false color imagery for the detection of water discoloration caused by HABs. However, high Chl-a concentrations (up to 3 mg m-3) occurred in the same areas (clearly seen as red features in the Figs 6 and 7c). Thus, in comparison to the false color imagery, ABI-derived values of Chl-a appear to be less sensitive to the presence of substances other than phytoplankton, thereby reducing the possibility that pixels characterised by non-algal substances being confused with those that are representative of Ostreopsis sp. and H. akashiwo blooms. Based on the current model, the presence of these two blooms (red patches in of Figs 6, 7d and 7e) appeared to coincide spatially with high ABI-derived Chl-a concentrations (Figs 6 and 7c), indicating that the model was able to detect the presence/absence of these blooms. The presence of Ostreopsis sp. and H. akashiwo blooms detected by the remote sensing model were in agreement with maximum in situ cell counts recorded along the south-central coast of Saudi Arabia during 27th February 2012 and 27th May 2010 (Figs 6 and 7f and Table 3), respectively.

thumbnail
Fig 6. Remotely sensing Ostreopsis sp. blooms in the central Red Sea and comparison with in situ measurements.

(a) and (b) False color composite image generated using the NIR, green and blue reflectances (i.e., R = Rrs (748), G = Rrs (547), B = Rrs (443)). (c) Satellite-derived Chl-a using the ABI algorithm. (d) and (e) MODIS-Aqua-derived maps of the Ostreopsis sp. blooms in the central Red Sea on 27th February 2012 [In (e) the red and white dots denote the presence and absence of HABs, respectively, as detected using satellite data. Also, the yellow circles and yellow triangles indicate the in situ sample points and highlight the presence and absence of Ostreopsis sp. blooms, respectively]. (f) Variations in Ostreopsis sp. cell counts recorded from the different sampling sites in the central Red Sea on 27th February 2012 [red squares and red circles denote satellite detection of HAB presence and absence, respectively].

https://doi.org/10.1371/journal.pone.0215463.g006

thumbnail
Fig 7. Remotely sensing H. akashiwo blooms in the central Red Sea and comparison with in situ measurements.

(a) and (b) False color composite image generated using the NIR, green and blue reflectances (i.e., R = Rrs (748), G = Rrs (547), B = Rrs (443)). (c) Satellite-derived Chl-a using ABI algorithm. (d) and (e) MODIS-Aqua-derived maps of the H. akashiwo blooms in the Red Sea on 27th May 2010 [In (e) the red and white dots denote the presence and absence of HABs, respectively, as detected using satellite data. Also, the yellow circle and yellow triangle indicates the in situ sample points and highlight the presence and absence of H. akashiwo blooms, respectively]. (f) Variations in H. akashiwo cell counts recorded from the two different sampling sites in the Red Sea off the southern coast of Saudi Arabia on 27th May 2010 [red square and red circle denotes the satellite detection of HAB presence and absence, respectively].

https://doi.org/10.1371/journal.pone.0215463.g007

Southern Red Sea.

Similarly, we applied the model to MODIS-Aqua images taken on 8th May 2013 and 27th December 2012 to investigate blooms of K. foliaceum and T. erythraeum, respectively, in the Yemeni coastal waters of the southern Red Sea. As reported by Alkawri [9,10], these two blooms occurred in the shallow waters, where reflective features (e.g. suspended sediments and the sea bed) may disrupt the signal of these phytoplankton blooms. This hindered the utility of false color composite imagery for the visual differentiation of these blooms from features associated with surrounding non-bloom and sediment dominated waters (Figs 8 and 9b). In contrast, N. scintillans/miliaris blooms on 3rd March 2009 were clearly observed as red features in the false color imagery, as a result of enhanced reflectance at red wavelengths (Fig 10b). Also, Chl-a associated with these patches was much higher than the surrounding non-bloom waters (Fig 10c). The spatial patterns of K. foliaceum, T. erythraeum and N. scintillans/miliaris blooms, as depicted by their bloom maps, were highly correlated with high Chl-a values (> 2 mg m-3) in those areas (Figs 810c and 10d). Though the field observations were spatially limited, when applied to MODIS-Aqua data, our remote sensing model was able to map the spatial patterns of these HAB species in the Yemeni coastal waters of the southern Red Sea (Figs 810d and 10e). The presence of K. foliaceum, T. erythraeum and N. scintillans/miliaris blooms detected by MODIS-Aqua were found to coincide with higher observed in situ cell counts that were collected along the Yemeni coastal waters on 8th May 2013, 27th December 2012 and 03rd March 2009 (Figs 810f and Table 3), respectively.

thumbnail
Fig 8. Remotely sensing K. foliaceum blooms in the southern Red Sea and comparison with in situ measurements.

(a) and (b) False color composite image generated using the NIR, green and blue reflectances (i.e., R = Rrs (748), G = Rrs (547), B = Rrs (443)). (c) Satellite-derived Chl-a using ABI algorithm. (d) and (e) MODIS-Aqua-derived maps of the K. foliaceum blooms in the Red Sea on 8th May 2013 [In (e) the red and white dots denote the presence and absence of HABs, respectively, as detected using satellite data. Also, the yellow circle and yellow triangles indicate the in situ sample points and highlight the presence and absence of K. foliaceum blooms, respectively]. (f) Variations in K. foliaceum cell counts recorded from the four different sampling stations in the coastal waters of Al Salif, southern Red Sea on 8th May 2013 [red squares and red circles denote satellite detection of HAB presence and absence, respectively].

https://doi.org/10.1371/journal.pone.0215463.g008

thumbnail
Fig 9. Remotely sensing T. erythraeum blooms in the southern Red Sea and comparison with in situ measurements.

(a) and (b) False color composite image generated using the NIR, green and blue reflectances (i.e., R = Rrs (748), G = Rrs (547), B = Rrs (443)). (c) Satellite-derived Chl-a using ABI algorithm. (d) and (e) MODIS-Aqua-derived maps of the T. erythraeum blooms in the Red Sea on 27th December 2012 [In (e) the red and white dots denote the presence and absence of HABs, respectively, as detected using satellite data. Also, the yellow circle and yellow triangles indicate the in situ sample points and highlight the presence and absence of T. erythraeum blooms, respectively]. (f) Variations in T. erythraeum cell counts recorded from the four different sampling stations in the Yemeni coastal waters off Red Sea, near Al Hodeida City on 27th December 2102 [red squares and red circles denote satellite detection of HAB presence and absence, respectively].

https://doi.org/10.1371/journal.pone.0215463.g009

thumbnail
Fig 10. Remotely sensing N. scintillans/miliaris blooms in the southern Red Sea and comparison with in situ measurements.

(a) and (b) False color composite image generated using the NIR, green and blue reflectances (i.e., R = Rrs (748), G = Rrs (547), B = Rrs (443)). (c) Satellite-derived Chl-a using ABI algorithm. (d) and (e) MODIS-Aqua-derived maps of the N. scintillans/miliaris blooms in the Red Sea on 3rd March 2009 [In (e) the red and white dots denote the presence and absence of HABs, respectively, as detected using satellite data. Also, the yellow circles and yellow triangles indicate the in situ sample points and highlight the presence and absence of N. scintillans/miliaris blooms, respectively]. (f) Variations in N. scintillans/miliaris cell counts recorded from the five different stations in the coastal waters of Yemen, southern Red Sea during 3rd March 2009 [red squares and circles denote satellite detection of HAB presence and absence, respectively].

https://doi.org/10.1371/journal.pone.0215463.g010

To assess the accuracy of the remote sensing model, four measures of accuracy were derived from an error matrix: the user’s accuracy, producer’s accuracy, overall accuracy and kappa coefficient (see definitions and formulations in S1 File). Based on the spatial matchups between in situ measurements and satellite-derived HAB observations, we constructed an error matrix to provide an accuracy assessment and error characterization of our model (Table 4). The results of the accuracy assessment for detecting the presence and absence of different HAB species in the Red Sea are outlined in Table 4 (footnote ‘a’). Overall, the results reveal that satellite-derived HAB observations exhibit a good agreement with the in situ datasets of individual HAB species.

thumbnail
Table 4. Statistical accuracy assessment results for detecting the presence and absence of different HAB species in the Red Sea watersa.

https://doi.org/10.1371/journal.pone.0215463.t004

Relationships between satellite-derived Chl-a and in situ phytoplankton cell counts

In order to assess the performance of the remote sensing model, we compared satellite-derived Chl-a and in situ phytoplankton cell counts data for each HAB species. As evident in Fig 11a, the in situ cell count increases in conjunction with satellite-derived Chl-a concentrations for each of the observed HAB species. In addition, we also computed the area density for each HAB species, based on the spatial coverage of bloom pixels (area covered by the HAB event) and their corresponding in situ phytoplankton cell counts. Fig 11b shows the variations in the area density of the different HAB species observed in the Red Sea. The area density of T. erythraeum was the lowest (0.3 kg m-2) amongst all of the species because of their low cell abundance (1.6* 104 L-1), whilst the highest was observed for H. akashiwo with an area density of 540 kg m-2. The second highest area density was attributed to K. foliaceum (29 kg m-2). The overall area density pattern of HAB species followed the trend H. akashiwo > K. foliaceum > N. scintillans/miliaris > Ostreopsis sp. > T. erythraeum.

thumbnail
Fig 11. Remotely sensing Chl-a concentrations associated with different HAB species in the Red Sea and comparison with in situ phytoplankton cell counts.

(a) Variations in total cell counts of harmful species and their satellite-derived Chl-a concentrations observed in the Red Sea. (b) Variations in the area density of remotely sensed HABs in the Red Sea. TE- Trichodesmium erythraeum; Ost- Ostreopsis sp.; NS- Noctiluca scintillans; KF-Kryptoperidinium foliaceum; HA-Heterosigma akashiwo.

https://doi.org/10.1371/journal.pone.0215463.g011

Discussion

Accuracy of the present HAB detection model in the Red Sea

Over the last two decades, an increase in the number of HAB events has been observed along the Red Sea coast [1117]. We note that many of these species have been reported to cause severe fish mortalities and numerous ecological impacts in the region [1014]. However, as the Red Sea remains relatively unexplored, in situ datasets are limited. Alternatively, in parallel with in situ measurements, satellite remote sensing can provide important information on the spatial distribution of HABs. Until recently, several bio-optical algorithms have been established to identify the dominant PFTs in the oceans [1933]. Brewin et al. [27] demonstrated that these algorithms use different PFT definitions and retrieve different variables including, but not limited to, multiple taxonomic groups and phytoplankton size fractions based on Chl-a or volume [25]. These PFT algorithms were mainly based on second-order anomalies of Rrs spectra [30], band-ratio Rrs spectra [33], absorption [31] and backscattering spectra [32]. Although the aforementioned algorithms have provided promising results for distinguishing PFTs from space, they have pointed out several limitations, one being their limited ability to distinguish between different HAB species that belong to the same PFT. To overcome this, we developed a model that exploits the spectral characteristics of different HAB species using a second-order derivative approach, along with the Rrs band-ratio/difference method, to detect and classify HABs in the Red Sea. The second-order derivative technique was trained based on the spectral curvature and shape of Rrs data—two factors used to distinguish species-specific HABs from other phytoplankton and non-phytoplankton features in the coastal and open waters of Red Sea. The prominent peaks in the Rrs spectra at green (~ 531 and/or 547nm) and red wavelengths (~ 678 nm) for these HAB species are represented by noticeable troughs in the respective wavelengths for the d2 Rrs spectra (see Figs 3 and 4). We note that the spatial patterns of these HABs species, inferred from bloom maps, generated via the remote sensing model, appeared to be consistent with satellite-derived Chl-a. Our results show that the current model detects the aforementioned HAB species with adequate accuracy, specifically at high Chl-a concentrations (> 1 mg m-3). In addition, our model was also capable of mapping the spatial extent of these HAB species in the Red Sea waters. Although HABs mainly occur locally, the dynamic regional circulation can transfer water masses hundreds of kilometers away [9,54] (see Fig 10d and 10e). Combining false color satellite imagery from spectral Rrs data, satellite Chl-a, and knowledge of local waters, our study has demonstrated that it is possible to differentiate HABs from the reflective feature associated with suspended sediments, corals and shallow bathymetry.

Limitations of the present HAB model

In this section, we acknowledge some limitations of the HAB model, identify and summarize the main issues, and wherever possible, suggest solutions. Firstly, the Red Sea is surrounded by deserts and is frequently influenced by dust storms and aerosols, which makes it challenging to apply atmospheric corrections [38]. Since our model utilises satellite ocean color data, the accuracy of the detection and classification of HABs may also depend on the atmospheric correction results (i.e., water leaving radiance (Lw)) [19]. Recent studies have demonstrated that the standard atmospheric correction scheme may result in erroneous and negative Lw across the entire visible wavelengths in optically complex oceanic waters, where dense HABs (e.g. N. scintillans/miliaris and C. polykrikoides) absorb strongly in the blue wavelengths and reflect at NIR bands [34,55]. Thus, the associated enhanced water signals may interfere with the standard aerosol correction scheme to produce negative Lw (and subsequently Rrs) for most of the HAB pixels [20,22,34]. This problem was circumvented by utilising an atmospheric correction algorithm [34] that was mainly developed for optically complex, oceanic waters dominated by suspended sediments and HABs [19]. This atmospheric correction algorithm, developed by Singh and Shanmugam [34], was adopted in the current model because the overestimation of aerosol radiance related to the iterative NIR method (i.e., standard aerosol correction scheme) [56], and its extrapolation to the visible spectrum in sediment-laden and algal bloom waters, leads to negative values of Rrs [34,38]. To overcome these issues, a dimensionless coefficient (κ) was introduced in this atmospheric algorithm to account for the non-zero water leaving radiance contribution in the NIR bands, and the extrapolation of the aerosol radiance to the visible bands [19,34], thereby leading to positive retrievals of Rrs across all visible wavelengths in HAB dominated waters (see Fig 3). This enabled the more accurate detection and discrimination of HABs by the current model, without any pixels being falsely masked out or eliminated by strict quality control criteria [19,34].

Secondly, the southern Red Sea is adversely affected by atmospheric conditions (haze and clouds) during the summer monsoon (June-September), which severely reduces the retrieval of satellite ocean color observations from MODIS-Aqua data [38, 5760]. This limited the ability of the present remote sensing model to assess Chl-a and HABs in the southern Red Sea during summer. For instance, Alkawri et al. [10,12] reported the occurrence of intense blooms of the toxic dinoflagellate Protoperidinium quinquecorne (up to 14.3 × 106 L-1) and Pyrodinium bahamense var. bahamense (up to 3.3 × 105 L-1) in the Yemeni coastal waters of the southern Red Sea during June 2012 and August 2013, respectively. However, the persistent presence of clouds, sun-glint, and sensor saturation during these months may hinder the ability of the model to detect the presence/absence of these HABs and map their spatial extent in the southern Red Sea. Due to these adverse atmospheric conditions over the Red Sea region, the current model can only utilize a limited number of daily MODIS-Aqua images for mapping the spatial distribution of HAB species in this basin.

There is also another limitation related to the spectral analysis adopted in the present HAB model. For instance, the Rrs spectra of these HAB species (excluding T. erythraeum) exhibits two significant peaks in the green and red wavelengths, due to different phytoplankton properties (e.g. absorption by Chl-a and other pigments, cell density and other associated optical properties). However, water dominated by cyanobacteria (e.g. Trichodesmium) exhibits relatively low values at 678 nm (see Fig 3f). This indicates a less-dense bloom (Chl-a concentration < 2 mg m-3; see Fig 9c) when compared to the Trichodesmium MODIS spectra reported in the global oceans, including regions adjacent to the Red Sea (such as Arabian Sea, Indian Ocean) [19,22,61]. In addition, highly reflective features (e.g. suspended sediments, corals, shallow bathymetry) were found to interfere with the spectral detection of Trichodesmium blooms [19,20]. In such conditions, it was hard to optically distinguish Trichodesmium blooms from other strong features using satellite ocean colour observations. Existing models require additional information (e.g. SST, wind and depth) to properly distinguish Trichodesmium blooms from other phytoplankton and non-phytoplankton features [19]. We also note that the intensity and position of Rrs peaks and spectral characteristics in the far-red wavelengths are influenced by both phytoplankton reflectance and the exponentially increasing absorption of water molecules [6264]. For instance, the higher the concentration of phytoplankton and/or nearer to surface water is, the stronger the Rrs peak. It is noted that for higher Chl-a concentrations (> 2 mg m-3), these HABs form surface scum and the corresponding Rrs around 678 nm are enhanced [19,3944]. Also, the peak in the red wavelength (around 678 nm) can be shifted into the NIR region (700–720 nm) at much higher Chl-a concentrations (>10 mg m-3) as a result of these species floating in the surface water [19,22].

Finally, the model developed in this study is currently restricted for the detection of Ostreopsis sp., H. akashiwo, K. foliaceum, N. scintillans/miliaris, T. erythraeum and C. polykrikoides in the Red Sea (see S1 File and S3 Fig for the spatial distribution of C. polykrikoides). Thus, future efforts could be directed towards strengthening training datasets and extending the model validation with more in situ datasets of other types of HABs that were not investigated in this study (for example, diatoms and other dinoflagellates species such as P. bahamense and P. quinquecorne). Furthermore, the inclusion of additional training datasets may allow the model to map the spatial extent of HAB species in other regions adjacent to the Red Sea, such as the Arabian Sea, Indian Ocean and Mediterranean Sea. In addition, we noticed some uncertainty in the accuracy of the present model when it detects blooms that are spatially less dense, and/or consist of mixed assemblages of different phytoplankton (especially cyanobacteria/diatoms with dinoflagellates). For example, the dinoflagellate N. scintillans/miliaris, which was observed at a low cell density (i.e., cell counts ≈ 4.4 ×103 cells L-1; see Fig 10f—“st1”), often co-occurred with cyanobacteria/diatoms [14,19]. Hence, the optical properties of the water were influenced by the presence of both organisms [19,41]. Despite the aforementioned limitations and uncertainty, our results suggest that the proposed remote sensing model provide an adequate and efficient tool to detect the presence/absence of HABs, and monitor their spatial extents in the Red Sea.

Conclusions

We presented a HAB detection model that combines satellite ocean color data and a second-order derivative technique to detect and map the spatial extent of different HAB events in the Red Sea. The performance of this model was evaluated with daily MODIS-Aqua images that were selected to coincide with HABs that have been previously reported in the basin. False color imagery and satellite-derived Chl-a maps were used to assess the spatial variation of these HAB species. Comparisons between modelled outputs and in situ field data further validated the ability of the remote sensing model to detect the presence/absence of HABs. At species concentrations greater than 1 mg m-3, the model was able to distinguish species-specific HABs in the coastal and open waters of the Red Sea. The accuracy of the model may vary depending on different spatio-temporal scales, as a result of the limited data used for training the second-order derivative technique. The model’s sensitivity is currently tailored towards the detection of ‘‘typical blooms”, on a specific day, corresponding to previously reported HAB events in the upper layer of the ocean. Thus, future efforts could be directed towards investigating the seasonality and interannual variability of HABs in the Red Sea over the last two decades. Such information is necessary for the implementation of aquaculture management strategies and the sustainability of regional economies in the coastal zone of the Red Sea.

Supporting information

S1 Fig. Examples of particulate absorption spectra derived from MODIS-Aqua data through existing bio-optical models for the six different HABs in the Red Sea waters.

(a) H. akashiwo (b) Ostreopsis sp. (c) K. foliaceum (d) N. scintillans/miliaris (e) C. polykrikoides (f) T. erythraeum.

https://doi.org/10.1371/journal.pone.0215463.s001

(TIF)

S2 Fig. Examples of particulate backscattering spectra derived from MODIS-Aqua data through existing bio-optical models for the different HABs in the Red Sea waters.

a) H. akashiwo (b) Ostreopsis sp. (c) K. foliaceum (d) N. scintillans/miliaris (e) C. polykrikoides (f) T. erythraeum.

https://doi.org/10.1371/journal.pone.0215463.s002

(TIF)

S3 Fig. Remotely sensing C. polykrikoides blooms in the southern Red Sea.

(a) False color composite image generated using the NIR, green and blue reflectances (i.e., R = Rrs (748), G = Rrs (547), B = Rrs (443)). (b) Satellite derived Chl-a using ABI algorithm. (c) MODIS-Aqua-derived maps of the C. polykrikoides blooms in the Red Sea on 13 March 2010.

https://doi.org/10.1371/journal.pone.0215463.s003

(TIF)

S1 File. Accuracy assessment, particulate absorption and backscattering spectral analysis, and spatial distribution of Cochlodinium polykrikoides blooms in the Red Sea.

https://doi.org/10.1371/journal.pone.0215463.s004

(DOCX)

Acknowledgments

The authors are grateful to the Ocean Biology Processing Group of NASA for the distribution of the MODIS-Aqua data and the development and support of the SeaDAS software. The authors also thank Dr. R.J.W. Brewin for his valuable input regarding the methodology of this study.

References

  1. 1. Anderson DM, Glibert PM, Burkholder JM. Harmful algal blooms and eutrophication: Nutrient sources, composition, and consequences. Estuaries. 2002; 25(4b): 562–584.
  2. 2. Berumen ML, Hoey AS, Bass WH, Bouwmeester J, Catania D, Cochran JE, et al. The status of coral reef ecology research in the Red Sea. Coral Reefs. 2013; 32(3): 737–748.
  3. 3. Belkin IM. Rapid warming of large marine ecosystems. Progress in Oceanography. 2009; 81(1–4): 207–213.
  4. 4. Yao F, Hoteit I, Pratt LJ, Bower AS, Zhai P, Köhl A, et al. Seasonal overturning circulation in the Red Sea: 1. Model validation and summer circulation. Journal of Geophysical Research: Oceans. 2014; 119(4): 2238–2262.
  5. 5. Yao F, Hoteit I, Pratt LJ, Bower AS, Köhl A, Gopalakrishnan G, et al. Seasonal overturning circulation in the Red Sea: 2. Winter circulation. Journal of Geophysical Research: Oceans. 2014; 119(4): 2263–2289.
  6. 6. Sofianos SS, Johns WE. An oceanic general circulation model (OGCM) investigation of the Red Sea circulation: 2. Three dimensional circulation in the Red Sea. Journal of Geophysical Research: Oceans. 2003; 108(C3).
  7. 7. Gittings JA, Raitsos DE, Krokos G, Hoteit I. Impacts of warming on phytoplankton abundance and phenology in a typical tropical marine ecosystem. Scientific reports. 2018; 8(1):2240. pmid:29396537
  8. 8. Raitsos DE, Pradhan Y, Brewin RJ, Stenchikov G, Hoteit I. Remote sensing the phytoplankton seasonal succession of the Red Sea. PloS one. 2013; 8(6): e64909. pmid:23755161
  9. 9. Raitsos DE, Brewin RJ, Zhan P, Dreano D, Pradhan Y, Nanninga GB, et al. Sensing coral reef connectivity pathways from space. Scientific Reports. 2017; 7(1): 9338. pmid:28839286
  10. 10. Alkawri A, Al-Areeki M, Alsharaby K. The first recorded bloom of Protoperidinium quinquecorne and its link to a massive fish kill in Yemeni coastal waters, Southern Red Sea. Plankton and Benthos Research. 2016; 11(2): 75–78.
  11. 11. Alkawri A. Seasonal variation in composition and abundance of harmful dinoflagellates in Yemeni waters, southern Red Sea. Marine pollution bulletin. 2016; 112(1–2): 225–234. pmid:27555482
  12. 12. Alkawri A, Abker M, Qutaei E, Alhag M, Qutaei N, Mahdy S. The first recorded bloom of Pyrodinium bahamense var bahamense plate in Yemeni coastal waters off Red Sea, near Al Hodeida City. Turkish Journal of Fisheries and Aquatic Sciences. 2016; 16(2): 275–282.
  13. 13. Mohamed ZA, Mesaad I. First report on Noctiluca scintillans blooms in the Red Sea off the coasts of Saudi Arabia: consequences of eutrophication. Oceanologia. 2007; 49(3).
  14. 14. Alkershi A, Menon N. Phytoplankton in polluted waters of the Red Sea coast of Yemen. J. Mar. Biol. Ass. India. 2011; 53(2): 161–166.
  15. 15. Catania D, Richlen ML, Mak YL, Morton SL, Laban EH, Xu Y, et al. The prevalence of benthic dinoflagellates associated with ciguatera fish poisoning in the central Red Sea. Harmful algae. 2017; 68: 206–216. pmid:28962981
  16. 16. Mohamed ZA, Al-Shehri AM. Occurrence and germination of dinoflagellate cysts in surface sediments from the Red Sea off the coasts of Saudi Arabia. Oceanologia. 2011; 53(1): 121–136.
  17. 17. Mohamed ZA, Al-Shehri AM. The link between shrimp farm runoff and blooms of toxic Heterosigma akashiwo in Red Sea coastal waters. Oceanologia. 2012; 54(2): 287–309.
  18. 18. Banguera-Hinestroza E, Eikrem W, Mansour H, Solberg I, Curdia J, Holtermann K, et al. Seasonality and toxin production of Pyrodinium bahamense in a Red Sea lagoon. Harmful algae. 2016; 55: 163–171. pmid:28073529
  19. 19. Gokul EA, Shanmugam P. An optical system for detecting and describing major algal blooms in coastal and oceanic waters around India. Journal of Geophysical Research: Oceans. 2016;121(6): 4097–4127.
  20. 20. Subramaniam A, Brown CW, Hood RR, Carpenter EJ, Capone DG. Detecting Trichodesmium blooms in SeaWiFS imagery. Deep Sea Research Part II: Topical Studies in Oceanography. 2001; 49(1–3): 107–121.
  21. 21. Zhao J, Ghedira H. Monitoring red tide with satellite imagery and numerical models: a case study in the Arabian Gulf. Marine pollution bulletin. 2014; 79(1–2): 305–313. pmid:24461701
  22. 22. Simon A, Shanmugam P. An algorithm for classification of algal blooms using MODIS-Aqua data in oceanic waters around India. Advances in Remote Sensing. 2012; 1(2).
  23. 23. Kostadinov TS, Cabré A, Vedantham H, Marinov I, Bracher A, Brewin RJ, et al. Inter-comparison of phytoplankton functional type phenology metrics derived from ocean color algorithms and earth system models. Remote sensing of environment. 2017; 190:162–77.
  24. 24. Navarro G, Alvain S, Vantrepotte V, Huertas IE. Identification of dominant phytoplankton functional types in the Mediterranean Sea based on a regionalized remote sensing approach. Remote Sensing of Environment. 2014; 152:557–75.
  25. 25. Sathyendranath S, Aiken J, Alvain S, Barlow R, Bouman H, Bracher A, et al. Phytoplankton functional types from Space. In (Reports of the International Ocean-Colour Coordinating Group (IOCCG); 15) 2014; 1–156
  26. 26. Bracher A, Bouman HA, Brewin RJ, Bricaud A, Brotas V, Ciotti AM, et al. Obtaining phytoplankton diversity from ocean color: a scientific roadmap for future development. Frontiers in Marine Science. 2017; 4:55.
  27. 27. Brewin RJ, Hardman-Mountford NJ, Lavender SJ, Raitsos DE, Hirata T, Uitz J, et al. An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing. Remote Sensing of Environment. 2011; 115(2):325–39.
  28. 28. Raitsos DE, Lavender SJ, Maravelias CD, Haralabous J, Richardson AJ, Reid PC. Identifying four phytoplankton functional types from space: An ecological approach. Limnology and oceanography. 2008; 53(2):605–13.
  29. 29. Werdell PJ, Roesler CS, Goes JI. Discrimination of phytoplankton functional groups using an ocean reflectance inversion model. Applied optics. 2014;53(22):4833–49. pmid:25090312
  30. 30. Alvain S, Moulin C, Dandonneau Y, Bréon FM. Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery. Deep Sea Research Part I: Oceanographic Research Papers. 2005;52(11):1989–2004.
  31. 31. Bracher A, Vountas M, Dinter T, Burrows JP, Röttgers R, Peeken I. Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data. Biogeosciences. 2009; 6:751–64.
  32. 32. Kostadinov TS, Siegel DA, Maritorena S. Retrieval of the particle size distribution from satellite ocean color observations. Journal of Geophysical Research: Oceans.;114(C9).
  33. 33. O’Reilly JE, Maritorena S, Siegel DA, O’Brien MC, Toole D, Mitchell BG, et al. Ocean color chlorophyll a algorithms for SeaWiFS, OC2, and OC4: Version 4. SeaWiFS postlaunch calibration and validation analyses, Part. 2000; 3:9–23.
  34. 34. Singh RK, Shanmugam P. A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters. Remote Sensing of Environment. 2014; 142: 188–206.
  35. 35. Shanmugam P. A new bio-optical algorithm for the remote sensing of algal blooms in complex ocean waters. Journal of Geophysical Research: Oceans. 2011;116(C4).
  36. 36. Shanmugam P. New models for retrieving and partitioning the colored dissolved organic matter in the global ocean: Implications for remote sensing. Remote Sensing of Environment. 2011; 115(6): 1501–1521.
  37. 37. Brewin RJ, Raitsos DE, Pradhan Y, Hoteit I. Comparison of Chl-a in the Red Sea derived from MODIS-Aqua and in vivo fluorescence. Remote Sensing of Environment. 2013; 136: 218–24.
  38. 38. Brewin RJ, Raitsos DE, Dall’Olmo G, Zarokanellos N, Jackson T, Racault MF, et al. Regional ocean-color Chl-a algorithms for the Red Sea. Remote Sensing of Environment. 2015; 165: 64–85.
  39. 39. Zhao D, Xing X, Liu Y, Yang J, Wang L. The relation of Chl-a-a concentration with the reflectance peak near 700 nm in algae-dominated waters and sensitivity of fluorescence algorithms for detecting algal bloom. International Journal of Remote Sensing. 2010; 5, 31(1): 39–48.
  40. 40. Moldrup M, Garm A. Spectral sensitivity of phototaxis in the dinoflagellate Kryptoperidinium foliaceum and their reaction to physical encounters. Journal of Experimental Biology. 2012; 215(13): 2342–2346.
  41. 41. Dwivedi R, Rafeeq M, Smitha BR, Padmakumar KB, Thomas LC, Sanjeevan VN, et al. Species identification of mixed algal bloom in the Northern Arabian Sea using remote sensing techniques. Environmental monitoring and assessment. 2015;187(2): 51. pmid:25638059
  42. 42. Pettersson LH, Pozdnyakov D. Potential of remote sensing for identification, delineation, and monitoring of harmful algal blooms. In Monitoring of Harmful Algal Blooms. Springer, Berlin, Heidelberg. 2013; 49–111.
  43. 43. Hu C, Cannizzaro J, Carder KL, Muller-Karger FE, Hardy R. Remote detection of Trichodesmium blooms in optically complex coastal waters: Examples with MODIS full-spectral data. Remote Sensing of Environment. 2010; 15, 114(9): 2048–2058.
  44. 44. Dupouy C, Petit M, Dandonneau Y. Satellite detected cyanobacteria bloom in the southwestern tropical Pacific Implication for oceanic nitrogen fixation. International Journal of Remote Sensing.1988; 9(3):389–396.
  45. 45. Sathyendranath S, Cota G, Stuart V, Maass H, Platt T. Remote sensing of phytoplankton pigments: a comparison of empirical and theoretical approaches. International Journal of Remote Sensing. 2001; 22(2–3):249–273.
  46. 46. Wynne TT, Stumpf RP, Tomlinson MC, Ransibrahmanakul V, Villareal TA. Detecting Karenia brevis blooms and algal resuspension in the western Gulf of Mexico with satellite ocean color imagery. Harmful Algae. 2005;4(6):992–1003.
  47. 47. Siegel H, Ohde T, Gerth M, Lavik G, Leipe T. Identification of coccolithophore blooms in the SE Atlantic Ocean off Namibia by satellites and in-situ methods. Continental Shelf Research. 2007;27(2):258–274.
  48. 48. Savitzky A, Golay MJ. Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry. 1964;36(8): 1627–1639.
  49. 49. Steinier J, Termonia Y, Deltour J. Smoothing and differentiation of data by simplified least square procedure. Analytical Chemistry. 1972; 44(11): 1906–1909. pmid:22324618
  50. 50. Talsky G. Derivative spectrophotometry. Wiley-VCH; 1994.
  51. 51. Zhao J, Temimi M, Kitbi SA, Mezhoud N. Monitoring HABs in the shallow Arabian Gulf using a qualitative satellite-based index. International Journal of Remote Sensing. 2016; 17, 37(8): 1937–1954.
  52. 52. Ahn YH, Shanmugam P. Detecting the red tide algal blooms from satellite ocean color observations in optically complex Northeast-Asia Coastal waters. Remote Sensing of Environment. 2006; 30,103(4): 419–437.
  53. 53. Hu C, Muller-Karger FE, Taylor CJ, Carder KL, Kelble C, Johns E, et al. Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters. Remote Sensing of Environment. 2005; 97(3): 311–321.
  54. 54. Zhan P, Subramanian AC, Yao F, Hoteit I. Eddies in the Red Sea: A statistical and dynamical study. Journal of Geophysical Research: Oceans. 2014; 119(6): 3909–3925.
  55. 55. Shanmugam P. CAAS: An atmospheric correction algorithm for the remote sensing of complex waters. Annales Geophysicae (09927689). 2012; 30(1).
  56. 56. Gordon HR, Wang M. Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm. Applied optics. 1994; 33(3): 443–452. pmid:20862036
  57. 57. Steinmetz F, Deschamps PY, Ramon D. Atmospheric correction in presence of sun glint: application to MERIS. Optics express. 2011; 19(10): 9783–9800. pmid:21643235
  58. 58. Racault MF, Raitsos DE, Berumen ML, Brewin RJ, Platt T, Sathyendranath S, et al. Phytoplankton phenology indices in coral reef ecosystems: Application to ocean-color observations in the Red Sea. Remote Sensing of Environment. 2015; 160: 222–234.
  59. 59. Dreano D, Raitsos DE, Gittings J, Krokos G, Hoteit I. The Gulf of Aden intermediate water intrusion regulates the southern Red Sea summer phytoplankton blooms. PloS One. 2016; 11(12): e0168440. pmid:28006006
  60. 60. Raitsos DE, Yi X, Platt T, Racault MF, Brewin RJ, Pradhan Y, et al. Monsoon oscillations regulate fertility of the Red Sea. Geophysical Research Letters. 2015; 42(3): 855–862.
  61. 61. Al Shehhi MR, Gherboudj I, Ghedira H. An overview of historical harmful algae blooms outbreaks in the Arabian Seas. Marine pollution bulletin. 2014; 86(1–2): 314–324. pmid:25038981
  62. 62. Dall’Olmo G, Gitelson AA. Effect of bio-optical parameter variability and uncertainties in reflectance measurements on the remote estimation of Chl-a-a concentration in turbid productive waters: Modeling results. Applied Optics. 2006; 45(15): 3577–3592. pmid:16708105
  63. 63. Dekker AG, Brando VE, Anstee JM, Pinnel N, Kutser T, Hoogenboom EJ, Peters S, et al. Imaging spectrometry of water. In Imaging spectrometry. Springer, Dordrecht. 2002; 307–359.
  64. 64. Kutser T. Quantitative detection of Chl-a in cyanobacterial blooms by satellite remote sensing. Limnology and Oceanography. 2004; 49(6): 2179–2189.