Passive optical remote sensing of cyanobacteria and other intense phytoplankton blooms in coastal and inland waters

Review Article

Passive optical remote sensing of cyanobacteria and other intensephytoplankton blooms in coastal and inland waters

TIIT KUTSER*

Estonian Marine Institute, University of Tartu, Tallinn 12618, Estonia

(Received 30 August 2007; in final form 12 June 2008)

Increased frequency and extent of potentially harmful blooms in coastal and

inland waters world-wide require the development of methods for operative and

reliable monitoring of the blooms over vast coastal areas and a large number of

lakes. Remote sensing could provide the tool. An overview of the literature in this

field suggests that operative monitoring of the extent of some types of blooms (i.e.

cyanobacteria) is relatively straightforward. Operative monitoring of inland

waters is currently limited to larger lakes or using airborne and hand-held remote

sensing instruments as there are no satellite sensors with sufficient spatial resolu-

tion to provide daily coverage. Extremely high spatial and vertical variability in

biomass during blooms of some phytoplankton species and the strong effects of

this on the remote sensing signal suggest that water sampling techniques and

strategies have to be redesigned for highly stratified bloom conditions, especially

if the samples are collected for algorithm development and validation of remote

sensing data. Comparing spectral signatures of different bloom-forming species

with the spectral resolution available in most satellites and taking into account

variability in optical properties of different water bodies suggests that developing

global algorithms for recognizing and quantitative mapping of (harmful) algal

blooms is questionable. On the other hand some authors cited in the present paper

have found particular cases where satellites with coarse spectral and spatial resolu-

tion can be used to recognize phytoplankton blooms even at species level. Thus, the

algorithms and methods to be used depend on the optical complexity of the water

to which they will be applied. The aim of this paper is to summarize different

methods and algorithms available in an attempt to assist in selecting the most

appropriate method for a particular site and problem under investigation.

1. Introduction

The frequency and extent of intense phytoplankton blooms has increased in inlandand coastal waters around the world (Hallegraeff 2003, Sellner et al. 2003, Glibert

et al. 2005b). Potentially harmful effects of the blooms (Edler et al. 1985, Horner et al.

1997, Landsberg 2002, Backer and McGillicuddy 2006) on human and animal health,

drinking water quality and recreational use of water bodies have raised the awareness

of the general public, environmental agencies and water authorities. Reliable mon-

itoring of potentially harmful blooms is needed. Conventional monitoring networks,

based on infrequent sampling in a few fixed monitoring stations, cannot provide the

information needed as the blooms are very heterogeneous, both spatially and tempo-rally. Moreover, the information is often needed over vast coastal areas or large

*Email: [email protected]

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2009 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160802562305

International Journal of Remote Sensing

Vol. 30, No. 17, 10 September 2009, 4401–4425

numbers of lakes. Only remote sensing can provide the spatial and temporal coverage

needed. The usefulness of airborne (Wrigley and Horne 1974) and satellite (Ostrom

1976) remote sensing in detecting phytoplankton blooms was demonstrated more

than three decades ago. However, the question is how much useful information about

the blooms can remote sensing provide? The term ‘remote sensing’ is used here in thebroadest sense as the scale of the water bodies under investigation determines the

instrumentation used. At one end of the scale are hand-held instruments, desired by

water management authorities, which could rapidly detect the presence of potentially

harmful blooms in small ponds and lakes. At the other end of the scale are ocean

colour satellites that are the only viable option in the case of large water bodies or

basin-scale studies.

The term ‘bloom’ is not very scientific, especially in describing phytoplankton

biomass (Smayda 1997b). For example chlorophyll-a concentrations less than1 mg m-3 are often described as bloom conditions in open ocean waters

(Lampert et al. 2002). In contrast, there are several lake remote sensing studies

(Hoogenboom et al. 1998a, Schalles et al. 1998, Koponen et al. 2002) where

concentrations of chlorophyll-a occur in several tens to several hundreds of mg

m-3, but the authors do not even mention specifically that they have studied

bloom conditions. Thus, the term bloom is relative and it is better to be careful

when comparing remote sensing methods and algorithms obtained in different

types of bloom conditions.Intensive blooms of phytoplankton are often called red tides or harmful algal

blooms (HABs); both terms are actually misleading. First, the red tides have nothing

to do with tides. Some red tide species contain red pigment but it has been shown that

reddish pigments cannot solely be responsible for the red colour of red tides (Millie

et al. 1997, Schofield et al. 1999). Diersen et al. (2006) have shown that the red colour

of phytoplankton blooms is caused by the human visual system, namely by spectral

response curves of red and green receptors. Modelling results by Diersen et al. (2006)

showed that almost any phytoplankton species in high enough concentration cancolour the water red. Moreover, coloured dissolved organic matter (CDOM) or

suspended sediments can also produce red or brown surface waters. On the other

hand, some toxic algal species can cause serious damage to the ecosystem in concen-

trations which do not cause discoloration of water (Anderson 2003). Therefore, the

red tide is not a very useful term.

About 60–80 of the 300 bloom-forming species of phytoplankton (Alexandrinum

spp., Gymnodinium spp., Dinophysis spp., Pseudo-nitzchia spp., Nodularia spp., etc.)

produce toxins (Smayda 1997a, Landsberg 2002) and calling their blooms HABs isabsolutely correct. Syndromes the phytoplankton toxins cause include: Amnesic

Shellfish Poisoning, Neurotoxic Shellfish Poisoning, Paralytic Shellfish Poisoning,

Diarrhetic Shellfish Poisoning and Ciguatera Shellfish Poisoning. Cyanobacteria

produce blooms and some of the cyanobacteria produce toxins, but they are not

algae, strictly speaking. Some of the phytoplankton blooms are not toxic but cause

problems in other ways. For example, the decaying biomass of non-toxic blooms can

cause oxygen depletion and widespread mortality of plants and animals in the affected

area (Anderson 2003). Extensive blooms of phytoplankton can reduce light penetra-tion to the bottom, dramatically decreasing densities of submerged aquatic vegeta-

tion. Benthic macroalgal blooms can also cause oxygen depletion and both

phytoplankton and macroalgal blooms can have devastating effects on the tourism

and beach industry as decaying biomass on beaches is not very pleasant. Thus, the

4402 T. Kutser

term ‘harmful algal bloom’ is slightly misleading but is, at present, used to describe all

phytoplankton and macroalgae blooms that cause problems due to their toxicity or in

other ways.

Remote sensing could be useful for ecosystems studies, besides operative mon-

itoring, provided quantitative remote sensing of phytoplankton blooms is feasible.Increased frequency of harmful algal blooms is usually associated with eutrophi-

cation caused by anthropogenic stress (Glibert et al. 2005a, b). On the other hand

there is also the hypothesis that better wastewater treatment may increase blooms

of potentially harmful cyanobacteria as better removal of nitrogen from waste-

water will limit the growth of phytoplankton and will give competitive advantage

to cyanobacteria that can fix nitrogen from the atmosphere (Vahtera et al. 2007).

This hypothesis may also be true for other water bodies besides the Baltic Sea,

where plenty of phosphorus is available and nitrogen limits the growth of phyto-plankton. It has also been estimated (Wasmund et al. 2005) that the nitrogen load

to the Baltic Sea due to nitrogen fixation of cyanobacteria is 300 000–790 000 tons

per year (up to 70% of the annual load of all rivers to the Baltic Sea), making it

the biggest single source of nitrogen. Ecological assumptions, such as those

mentioned above, can only be controlled by time-series of satellite data, provided

mapping of phytoplankton biomass by remote sensing is possible. This kind of

long time-series data has already been used in ecological studies (Kahru et al.

2007). Use of remote sensing data also helps in understanding bloom developmentprocesses (Stumpf et al. 2008).

The HAB remote sensing problem may be divided into three main areas: (1)

detecting and mapping of the extent of the blooms; (2) quantification of bloom

biomass; (3) discriminating potentially harmful blooms from other blooms. Some

aspects of algal bloom remote sensing have been summarized by Richardson (1996),

Kahru and Brown (1997), as well as Stumpf and Tomlinson (2005). However, devel-

opments in this field have been rapid during recent years. Therefore, this paper

attempts to give an overview of the latest achievements in these fields.

2. Qualitative mapping of algal blooms

The most obvious task in mapping algal blooms is detecting the location and

extent of the bloom. Mapping of the extent of algal blooms is relatively straight-

forward in many cases as the blooms are usually associated with increased or

decreased brightness of water bodies and occur in offshore regions where other

causes of dramatic change in water colour are rare or impossible. For example,the red tide species Karenia brevis or cyanobacteria Trichodesmium spp. form

blooms in oligotrophic Case I waters (Subramaniam et al. 2002, Stumpf et al.

2008). Karenia brevis blooms are dark due to the low backscattering efficiency of

cells of this species (Cannizzaro et al. 2008), while cyanobacteria blooms are

brighter than surrounding water areas due to high backscattering associated

with gas vesicles in cyanobacterial cells. Blooms of cyanobacteria in the Baltic

Sea are formed in open parts of the sea. Such blooms are so distinct that the

extent of the blooms can be mapped using almost any remote sensing instrument.Coccolithophore blooms are also very distinct and detectable by many optical

sensors (Dupouy et al. 1988, Balch et al. 1991, Siegel et al. 2007).

Broadband sensors, such as Advanced Very High Resolution Radiometer

(AVHRR) (Kahru et al. 1993, Gower 1994, Hakanson and Moberg 1994),

Passive optical remote sensing of cyanobacteria 4403

multi-spectral sensors, such as Coastal Zone Color Scanner (CZCS) (Siegel et al.

1999) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) (Joint and Groom 2000,

Siegel and Gerth, 2000), have been used in the Baltic Sea. AVHRR has also been used

for mapping ‘red tide’ events in Canada (Gower 1994). Even synthetic aperture radars

have been utilized to map the extent of cyanobacterial blooms (Svejkovsky andShandley 2001), despite the fact that the radar beam cannot penetrate the water

surface. Elevated sea surface temperature indicates the presence of phytoplankton

blooms (Kahru et al. 1993). True colour imagery from satellites (Babin et al. 2005,

Ramos et al. 2005, Siegel et al. 2008), Space Shuttle photographs (Kuchler and Jupp

1988) and airborne photography (Wrigley and Horne 1974) have also been used to

detect harmful algal blooms.

The situation is much more complex in coastal and inland waters with heavy

sediment and/or CDOM load. When in offshore waters, even single band (Sarangiet al. 2005, Reinart and Kutser 2006) or two band (Kahru et al. 2004) algorithms

can be used effectively to map extent of phytoplankton blooms. Single band

algorithms do not allow recognition of the reason behind the elevated water-

leaving signal. For example, Moderate Resolution Imaging Spectroradiometer

(MODIS) band 1 can be used in quantitative mapping of cyanobacterial bloom

(Reinart and Kutser 2006) but it has also been used successfully in quantitative

mapping of suspended sediments (Chen et al 2007, Kutser et al. 2007). Separating

algal blooms from sediment-rich water is not possible using single band data andit may not be possible even in the case of higher spectral resolution. Chlorophyll-

retrieval algorithms (see discussion below) should enable separation of sediment-

rich waters from phytoplankton-rich waters. However, most chlorophyll-retrieval

algorithms misinterpret CDOM as a higher concentration of chlorophyll. Thus,

mapping the extent of phytoplankton blooms in complex coastal waters with

highly variable sediment and CDOM loads is problematic. For example, Hu

et al. (2007) have shown that spectral reflectance of Karenia brevis blooms, diatom

blooms and coastal river plumes are statistically similar in many cases.Stumpf (2001), Stumpf et al. (2003) and Miller et al. (2006) recommended discri-

mination of harmful blooms as anomalies against time-series of images. Anomaly

detection allows for development of early warning methods for monitoring HABs

and, in some cases, even discriminating between different HABs (Miller et al. 2006).

However, Hu et al. (2006) have shown for Karenia brevis blooms in the west Florida

shelf that the anomaly detection method gives lots of false positive and false negative

results. The method is more effective in detecting ‘new’ blooms in non-bloom waters,

but the anomaly detection fails in the case of long blooms, like some Karenia brevis

blooms, that may last over a year (Hu et al. 2006).

Another problematic area for mapping the extent of algal blooms is presented by

optically shallow coastal and inland waters. Vahtmae et al. (2006) and Kutser et al.

(2006d) have shown with model simulations that spectral signatures of cyanobacterial

blooms are similar to those of different benthic habitats. Benthic habitat mapping can

usually be organized when no blooms are disturbing this, but monitoring of cyano-

bacterial blooms in shallow-water areas is complicated or may be impossible.

However, in the Baltic Sea, cyanobacterial blooms usually emerge in open-waterareas with no bottom visibility. If occurrence of a bloom is detected in time then its

movement can be monitored and the alarm raised before wind and currents transport

the bloom to shallow-water areas, where it may become optically inseparable from

benthic habitat.

4404 T. Kutser

3. Quantitative mapping of biomass

The next step after detecting the presence of a bloom is mapping of the biomass in the

bloom. Phytoplankton biomass is usually described in terms of concentration of

chlorophyll-a. However, pigments other than chlorophyll can be and have been

used as a proxy for phytoplankton biomass. This section summarizes the different

approaches used, from statistical methods and single band algorithms to analytical

methods. Different biomass proxies (chlorophyll-a and other pigments) are discussed

separately.

3.1 Band-ratio-type algorithms for retrieval of chlorophyll-a

Chlorophyll-retrieval algorithms for multi-spectral sensors, which have one to a few

bands in the visible part of the spectrum, are based on single band algorithms or band-

ratio-type algorithms. For example, single band algorithms have been developed for

Landsat (Galat and Verdin 1989) and AVHRR (Kahru et al. 1993, 2000).

These studies were carried out in Pyramid Lake, Nevada and in the Baltic Sea.

Cyanobacteria were the bloom-forming species in both cases. Both these water bodies

are optically complex Case II waters, where single band algorithms should not work.However, concentration of cyanobacteria reaches very high values in blooms in both

water bodies (chlorophyll-a up to 9790 mg m-3 in Pyramid Lake). In such situations,

the optical properties of the water body are practically determined solely by phyto-

plankton biomass at all wavelengths in the visible and near-infrared parts of the

spectrum and single band algorithms can be used for quantitative mapping of phyto-

plankton biomass.

Kahru and Mitchell (1998) have shown with shipboard measurements in

Californian waters that using a single UV band (340 or 380 nm) value or ratio ofUV to blue band can be used to differentiate between red tide (Lingulodium polyedra)

and ‘normal’ blooms. However, using the UV bands is questionable in many coastal

and inland waters where CDOM is the dominant optically active substance and the

water-leaving signal at UV and blue wavelengths may be negligible. Calibration of

blue and UV bands of remote sensing sensors is technically complicated. Atmospheric

correction of coastal and inland water imagery is still an unsolved problem and the

errors are largest in the blue end of the spectrum. Thus, the potential use of UV bands

in monitoring algal blooms using hand-held or shipboard sensors is limited to veryclear waters.

Single band chlorophyll-retrieval algorithms utilizing red and near-infrared bands

are sometimes suitable for mapping bloom events. For example, Kutser et al (2006b)

have shown that MODIS band 1 (620–670 nm) can be used not only for detailed

mapping of the extent of cyanobacterial blooms but also for mapping cyanobacterial

biomass. Reinart and Kutser (2006) have shown that MODIS band 2 (841–876 nm) is

suitable for separating dense subsurface cyanobacterial blooms from surface scum.

The most commonly used chlorophyll-retrieval algorithms are based on the blue togreen ratio. This band ratio has also been used in mapping phytoplankton blooms

(Dupouy et al. 1988, Sathyendranath et al. 2001). Many authors use chlorophyll

standard products, which are based mostly on the blue to green ratio, in quantitative

mapping of blooms (Siegel and Gerth 2000, Lavender and Groom 2001, Santoleri

et al. 2003, Stumpf et al. 2003, Tang et al. 2004, 2006a, b, Tomlinson et al. 2004,

Sasamal et al. 2005, Wynne et al. 2005, Ahn and Shanmugam 2006, Sackman and

Perry 2006, Miller et al. 2006, Aiken et al. 2007, Folkestad et al. 2007, Siegel et al.


2007). However, the standard products must be treated with caution in coastal and

inland waters. For example, Darecki and Stramski (2004) have shown for the Baltic

Sea that SeaWiFS and MODIS chlorophyll-retrieval algorithms are not suitable even

in non-bloom conditions where they significantly overestimate chlorophyll concen-

trations. The main cause of the error is using blue bands in chlorophyll-retrievalalgorithms. CDOM is the dominant optically active substance in the Baltic Sea, many

lakes and estuaries of CDOM-rich rivers. Changes in concentration of phytoplankton

have only minor or negligible effect on the water reflectance signal at shorter wave-

lengths in these water bodies. Stumpf et al. (2000) have found for the Gulf of Mexico

that SeaWiFS standard product overestimates chlorophyll-a concentration by two to

four times. Thus, the chlorophyll standard products may easily confuse CDOM with

high chlorophyll in coastal waters and even qualitative mapping of phytoplankton

blooms may be questionable in certain circumstances. Reliability of the chlorophyllconcentrations estimated by standard algorithms has proved to be below acceptable

levels in many coastal and inland waters.

Medium Resolution Imaging Spectrometer (MERIS) chlorophyll standard pro-

duct is defined for waters with chlorophyll concentration up to 50 mg m-3, MODIS

and SeaWiFS products up to 64 mg m-3. On the other hand, remote sensing (Kutser

2004) and in situ data (Quibell 1992) suggest that chlorophyll concentration in surface

scum of cyanobacterial blooms may be up to 1000 mg m-3. Galat and Verdin (1989)

have even measured chlorophyll-a values up to 9790 mg m-3 in a dense Nodularia

spumigena bloom in Pyramid Lake, Nevada. The result of having the upper concen-

tration limits is masking out of the densest bloom areas. For example, the densest

areas of cyanobacterial blooms in the Baltic Sea are usually masked out in chlorophyll

product maps as atmospheric correction or processing errors. The reason for this is

very high reflectance in near-infrared (NIR) part of the spectrum not expected by

ocean colour algorithms. Cyanobacteria floating at the water surface look spectrally

like terrestrial plants (NIR reflectance 15–50%, Quibell 1992, Jupp et al. 1994, Kutser

2004). An example of surface scum reflectance spectra measured by the Hyperionsatellite in the Baltic Sea is shown in figure 1. Standard processing chains do not

accept the pixels with high NIR reflectance and mask them out as processing errors.

Use of terrestrial standard products, such as Normalized Difference Vegetation Index

0

10

20

30

40

50

60

400 500 600 700 800Wavelength, nm

Ref

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ance

, %

0

0.5

1

1.5

2

2.5

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Figure 1. Reflectance spectrum of surface scum in cyanobacterial bloom measured byHyperion satellite (solid line) and absorption coefficient of pure water (dashed line), fromSmith and Baker (1981).

4406 T. Kutser

(NDVI), could be a solution in such situations where water reflectance resembles land

more than water. The NDVI has been used in mapping of harmful algal blooms (Lin

et al. 2003) but it was not mentioned whether they observed surface scum that was

floating on the water surface or used NDVI for mapping subsurface bloom.

Band-ratio-type algorithms other than the blue to green have been proposed bymany authors for mapping of phytoplankton blooms (Holligan et al. 1983, Stumpf

and Tyler 1988, Ekstrand 1992, Arst and Kutser 1994, Gower 1994, Kutser and Arst

1994, Subramaniam and Carpenter 1994, Kutser et al. 1995, 1997a, b, 1998a, b, 1999,

Tassan 1995, Yacobi et al. 1995, Arst et al. 1996, Sathyendranath et al. 1997, Siegel

et al. 1999, Harma et al. 2001, Lavender and Groom 2001, Svejkovsky and Shandley

2001, Subramaniam et al. 2002, Vepsalainen et al. 2005, Ahn and Shanmugam 2006,

Zimba and Gitelson 2006). Many chlorophyll-a retrieval algorithms developed for

turbid coastal and inland waters utilize the effect that there is a peak in reflectancespectra near 700 nm in the case of waters with high phytoplankton content and the

height of the peak is correlated with chlorophyll-a concentration (Gitelson 1992,

Millie et al. 1992, Gitelson et al. 1993, 2007, Dekker 1993, Jupp et al. 1994, Kutser

1997, Hoogenboom et al. 1998a, b, Gons 1999, Gower et al. 1999, Cunningham et al.

2001, Harma et al. 2001, Kallio et al. 2001, 2003, Strombeck and Pierson 2001, Gin

et al. 2002, Oki and Yasuoka 2002, Dall’Olmo and Gitelson, 2005, 2006, Gons et al.

2005, Astoreca et al. 2006, Yang and Pan 2006, Zimba and Gitelson 2006, Gower and

King 2007, Koponen et al. 2007, Simis et al. 2007). Using the area of the red peakinstead of band ratios based on the height of the peak has been proposed (Schalles

et al. 1998) for mapping chlorophyll-a concentration in highly productive waters.

Many authors (Neville and Gower 1977, Spitzer and Dirks 1986, Fischer and

Kronfeld 1990, Letelier and Abbott 1996, Cullen et al. 1997, Schalles et al. 1998,

Gower et al. 1999, 2005, Cunningham et al. 2001, Hu et al. 2005, Gower and King

2007) attribute this peak to chlorophyll-a fluorescence and often call the height of the

peak versus a baseline ‘fluorescence line height’ (FLH). In contrast, Dall’Olmo and

Gitelson (2006) have shown that fluorescence has a negligible effect on the formationof the red peak in reflectance spectra of productive waters. Vasilkov and Kopelevich

(1982) and Dekker et al. (2001) also attribute the peak to pure sea water and

phytoplankton absorption and backscattering effects. Zimba and Gitelson (2006)

have shown with their reciprocal reflectance approach that the combined effect of

absorption by water and its constituents (phytoplankton, CDOM) has minima in the

wavelength range 700–720 nm that causes the peak in reflectance spectra of

phytoplankton-rich waters. Bio-optical (Kutser 2004) and radiative transfer model-

ling (Kutser et al. 2008) have shown that the red peak in reflectance spectra can besimulated with models not containing any fluorescence terms. Thus, it is unlikely that

the peak is caused by chlorophyll-a fluorescence. Moreover, Seppala et al. (2007) have

shown that most of the chlorophyll-a in cyanobacteria is located in non-fluorescing

photosystem I. Thus, according to the fluorescence theory, the peak must be the

strongest in the case of algae and non-existent in the case of cyanobacteria. The real

situation is exactly the opposite. Waters dominated by cyanobacteria have the stron-

gest peak near 700 nm. There is usually a chlorophyll-a absorption feature near 685

nm in the reflectance spectra of productive waters instead of the chlorophyll-afluorescence peak that should be there. Quibell (1992) and Richardson (1996) have

shown that the NIR reflectance of freshwater algae is high in the wavelength range

690–950 nm if the algae are in water in very high concentrations. Lower biomass

means a narrower peak in the red and NIR and in most natural waters the peak occurs


in the wavelength range between 690 nm and 730 nm. Thus, the peak near 700 nm is

most probably caused by high (15–50%, Jupp et al 1994, Kutser 2004) reflectance of

phytoplankton in the NIR part of the spectrum (similar to terrestrial plants, see

figure 1), which can overcome strong absorption of light by water molecules that is

increasing exponentially in the wavelength range 700–750 nm.The general tendency in successfulness of the band-ratio-type algorithms in coastal

and inland waters seems to be that green to red (and NIR) bands are more suitable in

the case of higher phytoplankton biomasses and/or more turbid waters than the

algorithms using blue to green bands. Higher biomasses here usually means chlor-

ophyll-a concentrations in ten(s) of mg m-3 and higher. Utilizing the reflectance peak

near 700 nm is very effective in productive coastal and inland waters, but the set of

sensors capable of detecting this peak is mainly limited to hand-held, shipboard or

airborne sensors. The only hyperspectral satellite sensor capable of measuring con-tiguous reflectance spectra in the visible and NIR part of the spectrum, Hyperion, is

an experimental sensor. The usefulness of this type of data has been shown (Kutser

2004), but the sensor is not suitable for operative monitoring due to its narrow swath

and insufficiently frequent revisit times. MERIS is currently the only satellite sensor

that can be used for mapping chlorophyll concentration based on the peak near

700 nm. Gower and King (2007) demonstrated that the FLH algorithm can be used

to estimate chlorophyll concentration in oceanic waters when chlorophyll concentra-

tion is below 20 mgm-3. However, McKee et al (2007) found that the FLH in coastalwaters is strongly influenced by non-algal material, meaning that caution is required

for the interpretation of the FLH signal from coastal waters. Modelling results by

Kutser et al (2006a), Reinart and Kutser (2006) and Gower and King (2007) show that

using MERIS band 9 (705–710 nm) in brand-ratio type algorithms will be more useful

as the peak in reflectance spectra of phytoplankton-rich waters is actually in this

band, not in band 8 (centred at 681 nm) as predicted by the fluorescence theory and

used in FLH algorithms. MODIS sensors do not have appropriate bands to detect the

peak near 700 nm. However, the FLH algorithms (utilizing band at 678 nm) have beenused for quantitative mapping of chlorophyll in a red tide species, Karenia brevis,

blooms (Cannizzaro et al. 2008).

3.2 Validation issues

Quantitative mapping of algal blooms is complicated also due to high spatial hetero-

geneity of the blooms. High horizontal patchiness causes problems in satellite remote

sensing as the variation in concentration of phytoplankton may vary by orders ofmagnitude within one pixel. One of the reasons why it is difficult to develop

chlorophyll-retrieval algorithms for algal blooms is the vertical distribution of some

phytoplankton species in the water column. There may be a need to re-design in situ

sampling strategies to obtain results that are more suitable from a remote sensing

point of view.

Subramaniam et al. (2002) have raised doubts about using satellites like SeaWiFS

with 1 km resolution in detecting blooms where the scale of spatial patchiness is in tens

of metres. The resulting remote sensing signal in one pixel is a mixture of signal fromdense bloom patches and areas of clear water that may be classified in the result as a

medium bloom. Kutser (2004) has shown that even the 30 m spatial resolution of

Hyperion is not adequate in quantitative mapping of cyanobacterial blooms. Many

pixels that were visually part of surface scum (high reflectance in NIR) were blown by

4408 T. Kutser

wind to areas with relatively clear water (no NIR signal) and had reflectance spectra

of the subsurface bloom (peak near 700 nm) as a result of being a mixture of signals

from scum and clear water. Kutser (2004) has also shown that the concentration of

chlorophyll in a cyanobacterial bloom may vary by almost two orders of magnitude

within one square kilometre pixel. Yacobi et al. (1995) found that chlorophyll con-centration varied by 300% on two sides of the boat while studying a Protoperidinium

bloom in Lake Kinnert. This raises questions about using a single point measurement

in cal/val of satellite data.

Continuous measurements, using flow-through systems, for example, should be

more appropriate. However, there are also problems related to using flow-through

systems. Chlorophyll-a fluorometers cannot be used for detecting cyanobacterial

biomass as most of the chlorophyll-a in cyanobacteria is located in non-fluorescing

photosystem I (Raateoja et al 2004, Seppala et al. 2007) and there is no correlationbetween the chlorophyll-a fluorescence and the actual chlorophyll-a concentration.

Fluorometers designed for pigments other than chlorophyll-a should be used in that

case. For example Seppala et al. (2007) have shown that phycocyanin fluorometers

can be used to estimate cyanobacterial biomass in the Baltic Sea.

Cyanobacteria and some red tide species form dense subsurface accumulations

and surface scum, leaving most of the water column relatively clean. Flow-

through systems on ships-of-opportunity take water from the ship’s cooling

system intakes, which are usually at depths of about 4–5 m (Leppanen et al.1995). This is far below the depth where most of the phytoplankton is during

surface blooms. It is also seen in high resolution satellite data (Kutser 2004) that

in dense cyanobacterial blooms ships push the bloom apart, leaving a trail of clear

water where the concentration of chlorophyll is lower by orders of magnitude

than a few tens of metres from the ship track. In such a situation the flow-

through system collects water from the clear water area where there is no correla-

tion between the bloom that remote sensing sensors are detecting in undisturbed

conditions and the biomass measured from water samples.The same problems occur when working from research vessels and especially when

working with phytoplankton species that form aggregations (e.g. cyanobacteria) and

surface scum. It is possible to drift into bloom areas with very low speed and (almost)

not to disturb the natural distribution of the bloom. However, taking a representative

sample of surface scum with conventional water samplers from large research vessel is

practically impossible. Serious problems in water sampling also occur when the bloom

is not unicellular and not well mixed (on a small scale). For example, cyanobacteria

form visually observable aggregations that are relatively difficult to catch with con-ventional sampling devices due to their buoyancy. Moreover, if the water is collected

with Niskin bottles, then the small portion of captured cyanobacterial aggregations

remains in the bottle when water is taken for analysis. The same happens when a

bucket is used for collecting surface water samples. Only a small number of aggrega-

tions can be captured with a bucket and these tend to float away when pouring water

from the bucket into filtrating systems. Thus, the sample used in chlorophyll analysis

is significantly clearer than the real situation in the sea or lake. Chlorophyll measure-

ment errors are also relatively large in these situations as an increase or decrease ofone large aggregation in the final sample changes the measured chlorophyll concen-

tration dramatically. Lack of adequate water samples is one of the reasons of pure

performance of quantitative remote sensing in the case of blooms of phytoplankton

that form aggregations and surface scum.


Many cyanobacteria, but also Dinoflagellates like Karenia brevis, can regulate their

buoyancy and move vertically in the water column. As a result the vertical distribution

of cyanobacteria in the water column is not uniform (in calm weather conditions).

Kutser et al. (2008) have shown that vertical distribution of cyanobacteria has a

significant impact on the remote sensing signal. Remote sensing estimates of chlor-ophyll-a (made using a band-ratio algorithm) may vary five to six times if the biomass

is close to the surface instead of being uniformly mixed in the water column. Schofield

et al (2006) have also shown that water reflectance spectra change due to diel migra-

tion of Karenia brevis. It means that water samples have to be taken from several

depths during such blooms unless it is proved with other instruments that the water

column is mixed well or a majority of the biomass is in a narrow (tens of centimetres)

subsurface layer. In the latter case, one carefully collected surface sample is adequate

as the depth of penetration in such situations is in centimetres (Kutser 2004) andremote sensing sensors cannot get any information from deeper layers anyway. Use of

integrated water samples for cal/val of remote sensing data during stratified blooms is

inappropriate.

If the phytoplankton bloom is situated in a very thin layer just below the water

surface or just above the water surface, then the biomass of phytoplankton per

volume captured in the water sample is very dependent on what instrument was

used and how the sample was collected. New sampling strategies should be developed

for surface blooms. Perhaps it is more appropriate to measure biomass per areainstead of biomass per volume?

3.3 Analytical methods of chlorophyll-a retrieval

Several other methods, besides band ratios and their combinations, have been used

for mapping of phytoplankton blooms. For example using derivative analysis has

been proposed (Richardson et al. 1994, Malthus and Dekker 1995, Rundquist et al.

1996, Fraser 1998, Schofield et al. 1999, Kirkpatrick et al. 2000, Hunter et al. 2008).Supervised classification schemes with classes selected from image data have been

used for detection of phytoplankton blooms (Richardson and Kruse 2000, Koponen

et al. 2002, Subramaniam et al. 2002, Ahn and Shanmugam 2006, Miller et al. 2006).

Huang and Lou (2003), as well as Pozdnyakov et al. (2005), used neural networks for

mapping chlorophyll-a content in phytoplankton blooms. MERIS coastal water

chlorophyll-retrieval algorithm is based on a neural network approach (Schiller and

Doerffer 1999).

Craig et al. (2006) used a mixed method for detection and assessment of Karenia

brevis biomass (cell counts). First they derived absorption spectra from reflectance

data and then used fourth derivatives of the absorption spectra to detect Karenia

brevis and estimate its biomass. A similar method was used by Millie et al. (2002) for

discriminating different microalgae. However, their results are from in situ measured

data. It has to be tested whether the absorption spectra can be retrieved with necessary

accuracy from reflectance spectra to achieve similar results with remote sensing data.

Reflectance spectra simulated by bio-optical or radiative transfer models have been

used in interpretation of remote sensing data. For example, Hoogenboom et al.(1998a) used a matrix inversion technique to retrieve chlorophyll concentration

(and tripton dry weight) in phytoplankton blooms. Arst and Kutser (1994), Kutser

et al. (1995, 2001) and Kutser (1997) used a ‘similarity method’, where the similarity

between measured and modelled reflectance spectra was estimated comparing integral

4410 T. Kutser

over measured spectrum with integral over modelled spectra. It was assumed that

concentrations of chlorophyll, CDOM and total suspended matter in the study area

were equal to those used in simulating the most similar model reflectance spectrum.

Kutser (2004) proposed a physics-based approach: using modelled spectral libraries

for quantitative mapping of cyanobacterial blooms from space. Spectral AngleMapper (SAM) was used in comparing the image spectra with the spectral library.

SAM normalizes both the spectral library and image spectra before comparing them

in n-dimensional space. Therefore, the classification results are relatively insensitive to

variations in illumination conditions during the image acquisition. Advantages of this

method compared to band-ratio-type algorithms are: simultaneous retrieval of chlor-

ophyll-a, CDOM and suspended matter; no requirement to collect cal/val in situ data

simultaneously with image acquisition and no need to develop algorithms for differ-

ent sensors. The advantage of the spectral library method over the ‘similarity method’is shorter computational times as the modelling is carried out only once, not for every

pixel in the image.

Modelled spectral libraries can be created for different altitudes, for example for

just above the water surface (using in-water optical model) or top of atmosphere

(using in-water and atmospheric models). Classifying the ‘raw’ image with the top-of-

atmosphere spectral library gave better results than using the atmospherically cor-

rected image and spectral library of just above the water surface reflectances (Kutser

et al. 2006c). These results were obtained for mapping coral reef benthic habitat, butthis may be applicable also to quantitative mapping of phytoplankton blooms.

Creating spectral libraries that contain reflectance spectra of different phytoplankton

species may be one of the ways to detect dominant phytoplankton classes in the water.

3.4 Quantitative mapping of biomass through accessory pigments

Phytoplankton biomass is usually described by concentration of chlorophyll-a.

Therefore, chlorophyll-a is also the main characteristic used in remote sensing ofblooms. Some phytoplankton groups contain accessory pigments specific to a smaller

group of phytoplankton. For example, Dinoflagellates contain peridinin,

Cryptophytes contain alloxanthin, diatoms contain fucoxanthin and diadinoxanthin,

cyanobacteria contain zeaxanthin and phycobiliproteins (Jeffrey and Vesk 1997).

Detecting the presence of some of the pigments by remote sensing may be possible.

Most cyanobacteria contain a phycobilin pigment called phycocyanin.

Phycocyanin is the most often used diagnostic pigment that allows one to detect the

presence of cyanobacteria. It has been shown (Dekker et al. 1992, Dekker 1993,Schalles and Yacobi, 2000, Simis et al. 2005, 2007) that quantitative mapping of

phycocyanin by remote sensing is possible. Phycocyanin is detectable due to two

characteristic spectral features in reflectance spectra: a phycocyanin absorption fea-

ture near 620–630 nm and a peak near 650 nm. However, this double feature is not

detectable at small concentrations of cyanobacteria (Kutser et al. 2006a). This effect

may limit quantitative mapping of phycocyanin by means of remote sensing and using

remote sensing in early warning systems, the aim of which is recognizing emerging

potentially harmful blooms. For example, chlorophyll-a concentrations higher than4–5 mg m-3 are considered a bloom in the Baltic Sea, but it is not possible to separate

cyanobacteria from other phytoplankton if the chlorophyll concentration is below

8–10 mg m-3 (Kutser et al. 2006a) Another problem related to describing cyanobac-

terial biomass through phycocyanin concentration is variability in intracellular


concentration of phycocyanin between different species and also its dependence on

environmental conditions (Tandeau de Marsac and Houmard 1988).

Hoge et al. (1999) have proposed a method for recovering phycoerythrin absorp-

tion coefficient from satellite data. The method is based on a linear matrix inversion of

an ocean radiance model. Thus, quantitative mapping of cyanobacterial biomass ispossible at least through concentrations of two accessory pigments besides the usual

chlorophyll-a.

Detecting specific spectral features caused by accessory pigments requires high

spectral resolution of the sensors used. Laboratory (Quibell 1992, Richardson

1996), airborne (Jupp et al 1994) and space-borne data (Kutser 2004) indicate that

hyperspectral sensors with spectral resolution of at least 10 nm should be adequate to

detect accessory pigments like phycocyanin. The majority of such sensors are airborne

or hand-held spectrometers (except Hyperion), but MERIS spectral resolution andband configuration is also appropriate for quantitative mapping of phycocyanin

(Simis et al. 2005, 2007, Ruiz-Verdu et al. 2005). Ruiz-Verdu et al. (2005) have also

shown that CHRIS/Proba is suitable for mapping phycocyanin and chlorophyll

concentrations. Vincent et al. (2004) used Landsat Thematic Mapper (TM) data for

mapping phycocyanin in a cyanobacterial bloom in Lake Erie. The only Landsat

band where phycocyanin can have an impact on the measured signal is band 3

(630–650 nm). However, an increase in biomass may cause deepening of the phyco-

cyanin absorption feature at 630 nm and an increase in the reflectance at 650 nm.Thus, the increasing biomass may cause both a decrease and increase in the Landsat

band 3 signal. The good correlation between Landsat data and phycocyanin concen-

tration showed by Vincent et al. (2004) can be explained by a good correlation

between water turbidity and biomass (phycocyanin concentration) in the study

area. In a similar way, total phosphorus and CO2 saturation in lake waters can be

mapped by remote sensing, despite the fact that they do not have any direct effect on

the measured remote sensing signal. Just the concentration of the former is often in

good correlation with lake water turbidity (Kutser et al. 1995) and the latter withCDOM concentration in lakes (Kutser et al. 2005).

4. Detecting species composition of blooms

Recognition of bloom-forming phytoplankton at a species level based on their reflec-

tance spectra requires uniqueness of spectral signatures of different species and

sensors that are capable of detecting these differences. Concentrations of CDOM

and suspended matter are also important as they can mask the spectral features thatcharacterize certain phytoplankton species or their groups.

Recognizing bloom-forming phytoplankton at a class level is possible in certain

circumstances. Subramaniam et al. (2002) have proposed a multi-step classification

scheme that allows separation of moderate blooms of cyanobacteria Trichodesmium

spp. from waters dominated by ‘average’ algae, rich in CDOM or suspended particles,

and from optically shallow waters. Metsamaa et al. (2006) and Kutser et al. (2006a)

have shown that reflectance of waters dominated by cyanobacteria differs from

reflectance of waters dominated by other phytoplankton groups. However, separat-ing different cyanobacteria from each other by their optical signatures is questionable.

Moreover, modelling studies with optical properties of the open parts of the Baltic Sea

and hyperspectral satellite data (Hyperion) showed that the concentration of cyano-

bacteria has to be relatively high (chlorophyll-a . 8–10 mg m-3) before the

4412 T. Kutser

phycocyanin absorption feature, separating cyanobacteria from other phytoplank-

ton, becomes detectable in reflectance spectra (Kutser et al. 2006a). Recognizing

cyanobacterial blooms in optically shallow water is also practically impossible

based on spectral signatures as many shallow-water benthic habitats (brown and

red macroalgae, corals) are spectrally similar to cyanobacteria (Kutser et al. 2003,Vahtmae et al. 2006).

Recent results by Cannizzaro et al. (2008) indicate that reflectance spectra of a red

tide species, Karenia brevis (formerly Gymnodinium breve), are very similar to reflec-

tance of cyanobacteria (Quibell 1992, Kutser et al. 2006a, Metsamaa et al. 2006), i.e.

there is also the double feature (peak at 650 nm trough at 630 nm) in reflectance

spectra of Karenia brevis when it is present in high quantity (chlorophyll .50 mg m-3).

Absorption by chlorophylls-c1-2-3 in Karenia brevis can result in similar spectral

features in reflectance spectrum like phycocyanin absorption in the case of cyano-bacteria. Consequently, recognizing cyanobacterial blooms by their optical signature

may be complicated in waters where blooms of dinoflagellates may occur (and vice

versa) as the shape of reflectance spectra of these groups are similar to each other. The

main difference between blooms of Karenia brevis and cyanobacteria is in the bright-

ness of the water. Cyanobacterial blooms are brighter than surrounding water masses

due to high backscattering by the cells of cyanobacteria, while blooms of Karenia

brevis are dark (Cannizzaro et al. 2007) due to very low backscattering.

Laboratory measurements with hand-held spectrometers (Johnsen et al. 1994,Hunter et al. 2008) have shown that some phytoplankton groups are optically separ-

able from each other. However, Hunter et al. (2008) also showed that varying con-

centrations of suspended particulate matter cause significant attenuation in spectral

signatures and accuracy of biomarker pigment estimation.

Many authors (Johnsen and Sakshaug 1993, Subramaniam and Carpenter 1994,

2002, Millie et al. 1995, 1997, Tassan 1995, Subramaniam et al. 1999a, b, Stumpf et al.

2003, Tomlinson et al. 2004, Ramos et al. 2005, Sarangi et al. 2005, Sasamal et al.

2005, Westberry et al. 2005, Wynne et al. 2005, Leong and Taguchi 2006, Sackmannand Perry 2006) name specific phytoplankton species in titles of their papers. This

may give the impression that recognition of these bloom-forming species is feasible by

remote sensing. However, recognition of the species was not the intention of the

authors in many cases. They just state the dominant species in the bloom studied.

For example, Tomlinson et al. (2004) used standard chlorophyll product anomalies to

detect harmful blooms of Karenia brevis and to separate those from areas rich in

CDOM or particulate matter. Even this proved to be difficult with SeaWiFS. Luckily,

the Karenia brevis blooms do not occur in areas with high CDOM or particulatematter concentrations. However, recognizing bloom-forming phytoplankton at a

species level with satellites seems to be an unrealistic task (Garver et al. 1994,

Cracknell et al. 2001). It may be feasible only in regions where a single species is

responsible for all blooms occurring in this area. In that case the bloom-forming

species are determined intuitively, not based on the optical signatures of the bloom.

Lee and Carder (2004) have proposed a method for deriving phytoplankton

absorption spectra from hyperspectral reflectance. This is one of the approaches

that could be used for detecting accessory pigments in phytoplankton, provided thepigments are present in high enough quantities to have an effect on reflectance and

absorption coefficient spectra. If the spectral features in absorption spectra caused by

accessory pigments are detectable, methods like those proposed by Hoepffner and

Sathyendranath (1991), Millie et al. (1995, 1997, 2002) and Schwarz et al. (2002) can


be used to identify these pigments and consequently provide information about

species composition of the main bloom-forming phytoplankton. Craig et al. (2006)

have used absorption spectra retrieved from reflectance to detect and assess biomass

of a red tide species, Karenia brevis.

Malthus et al. (1997) showed with neural network and optical data of fourbloom-forming phytoplankton species that development of algorithms for identi-

fication of algal species from remote sensing data could be possible if the domi-

nant species in the study area is optically different and the number is small. Pena-

Matinez et al (2003) have also shown that reflectance spectra of Cryptophyta,

Cyanobacteria, Chlorophyta and Diatoms are different if more than 80% of the

biomass corresponds to a single taxonomic group. Stumpf and Tomlinson (2005)

provide a table of bloom-forming algae species for which remote sensing is

currently being used.

5. Remote sensing sensors

The choice of the most appropriate senor depends on the task to be solved and water

body under investigation. In most cases the area that needs to be monitored is large

and only satellite sensors can provide the spatial coverage needed. However, the

spatial resolution of most satellite sensors is inadequate in the case of sophisticated

shorelines of coastal waters or small to medium-sized lakes. Use of airborne remotesensing is justified on these areas despite its higher costs compared to satellite data.

Several countries have equipped their boarder guard or coast guard aircraft with

imaging spectrometers, enabling retrieval of high spatial and spectral resolution data

at a small extra cost to monitoring programmes. In many cases, water monitoring

authorities are interested in cheap and robust hand-held remote sensing instruments

that can indicate the presence of harmful phytoplankton in the pond or lake under

investigation.

The most critical parameter of a satellite sensor is revisit time if the task is operativemonitoring. MODIS, MERIS, SeaWiFS and AVHRR provide data with the neces-

sary frequency and have been used in monitoring of phytoplankton blooms, as

described above. Spatial resolution is an issue in the case of coastal waters with a

sophisticated shoreline and most lakes. MODIS band 1 and 2 data and MERIS full

resolution imagery are the best options from a spatial resolution viewpoint. However,

MODIS single band data are inadequate to separate algal blooms from plumes of

turbid water and these data can only be used in areas where only algae can cause the

elevated water-leaving signal (e.g. offshore waters).Medium (Landsat, Advanced Land Imager (ALI), Satellite Pour l’Observation de

la Terre (SPOT)) and high (IKONOS, Quickbird) spatial resolution multi-spectral

satellites can be used for mapping spatial extent and even type of bloom in smaller

water bodies. However, revisit time of the sensors (e.g. 16 days for Landsat) is

inadequate for operative monitoring. Spectral resolution of the sensors is too coarse

for detecting pigment composition of blooms and the cost of the imagery is too high

for regular monitoring purposes.

Hyperion, with around 200 usable spectral bands, 10 nm spectral resolution and 30 mspatial resolution, provides data that are adequate even for detecting the presence of

accessory pigments. The Hyperion swath is relatively small (7.7 · 185 km). Its pointable

sensor allows more frequent revisits than non-pointable sensors, but it is still an

experimental sensor that cannot be used in routine monitoring.

4414 T. Kutser

The band configuration of MERIS provides the widest capabilities in bloom

monitoring from space, as MODIS lacks several spectral bands (near 630 nm and

700 nm) crucial for detecting accessory pigments (phycocyanin) or estimating chlor-

ophyll-a concentration in productive waters. However, easier access to data makes

MODIS the most often used sensor in monitoring of algal blooms.Spectral (a few nanometres) and spatial (below 1 m) resolution of airborne sensors

(Airborne Imaging Spectroradiometer for Applications (AISA), Airborne Visible and

Infrared Imaging Spectrometer (AVIRIS), Compact Airborne Spectrographic

Imager (CASI), HyMap etc.) makes them very effective tools in mapping of phyto-

plankton blooms. The only limiting factor of their use is the cost of data per unit of

study area, which does often not allow acquiring of data as frequently as desired or

covering as large an area as needed.

6. Conclusions

The main strategy in mapping algal blooms is to use as simple and robust methods and

algorithms as possible to achieve the desired results. The spatial and spectral resolu-

tion required depends also on the type and size of the water body under investigation

and the specific task that has to be solved (e.g. mapping of bloom extent, mapping of

biomass, recognizing potentially harmful blooms).

Monitoring of the extent and dynamics of phytoplankton blooms is relativelystraightforward in many cases as discoloration of water by elevated biomass is distinct

in many circumstances. For example, cyanobacterial blooms are usually easily detect-

able by almost any optical sensor available. Exceptions here may be re-suspended

sediment and/or CDOM-rich waters, where mapping of the bloom extent may be

problematic even with high spectral and spatial resolution sensors.

Quantitative mapping of bloom biomass has been demonstrated in oceanic, coastal

and inland waters. The general tendency is that chlorophyll-retrieval algorithms

based on blue to green bands work well in clear (oligotrophic) waters and algorithmsutilizing green to NIR bands work better in more productive, turbid and/or CDOM-

rich coastal and inland waters. The peak near 700 nm in water reflectance spectra is

most often used as a proxy for phytoplankton biomass in productive waters.

The vertical distribution of phytoplankton affects the water-leaving signal.

Developing methods for quantitative mapping of phytoplankton biomass in such

blooms is problematic unless appropriate in situ methods are used. The question

about appropriate in situ methods rises also in case of coarse spatial resolution of

some satellite sensors as it has been shown that phytoplankton biomass can vary by atleast two orders of magnitude in one square kilometre pixel.

Analytical methods using full measured reflectance spectra instead of band ratios

and statistics have been developed for mapping of phytoplankton biomass and/or

detecting species composition of the blooms. Some of the methods (use of derivative

spectra) require hyperspectral data and, therefore, are mainly limited to airborne and

hand-held sensors. Use of spectral libraries (or look-up tables) of waters with known

properties (measured in situ or modelled using bio-optical or radiative transfer

models) works well with hyperspectral data but is as easily applicable to currentocean colour sensors (MERIS, MODIS, SeaWiFS). The same applies to use of neural

networks.

Mapping phytoplankton biomass in terms of accessory pigments has been demon-

strated for phycocyanin. Phycocyanin is a pigment present mainly in cyanobacteria.


Thus, it is possible to separate blooms of cyanobacteria from blooms of other

phytoplankton. However, the differences in reflectance spectra become detectable

only if the cyanobacterial biomass is sufficiently high (chlorophyll-a higher than

8–10 mg m-3 in the Baltic Sea).

Several authors have demonstrated mapping of algal blooms at a species level.However, laboratory, in situ and modelling results of the authors cited in this review

suggest that separating blooms of different phytoplankton species based solely on

their optical signatures is highly problematic. The number of bloom-forming species

has to be limited and/or the water masses have to be optically simple and/or there has

to be some other local background knowledge (hydrodynamics, temperature or

nutrient requirements of some species, etc.) to be able to separate blooms of phyto-

plankton of different species from each other or from river plumes and areas with high

re-suspended sediment concentration.

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Documents

Passive optical remote sensing of cyanobacteria and other intense phytoplankton blooms in coastal and inland waters