Open access peer-reviewed chapter

Remote Sensing of Phytoplankton Pigments

Written By

Guoqing Wang and John Moisan

Submitted: 25 November 2020 Reviewed: 04 December 2020 Published: 05 May 2021

DOI: 10.5772/intechopen.95381

From the Edited Volume

Plankton Communities

Edited by Leonel Pereira and Ana Marta Gonçalves

Chapter metrics overview

530 Chapter Downloads

View Full Metrics

Abstract

Pigments, as a vital part of phytoplankton, act as the light harvesters and protectors in the process of photosynthesis. Historically, most of the previous studies have been focused on chlorophyll a, the primary light harvesting pigment. With the advances in technologies, especially High-Performance Liquid Chromatography (HPLC) and satellite ocean color remote sensing, recent studies promote the importance of the phytoplankton accessory pigments. In this chapter, we will overview the technology advances in phytoplankton pigment identification, the history of ocean color remote sensing and its application in retrieving phytoplankton pigments, and the existing challenges and opportunities for future studies in this field.

Keywords

  • phytoplankton
  • pigments
  • remote sensing
  • ocean color
  • satellite

1. Introduction

Phytoplankton live near the water surface to capture sufficient light for photosynthesis and act as the primary producer of the plankton community. They form the bottom levels of the marine and aquatic food webs, and their existence not only makes life in the water possible but also makes the ocean an important food source for mankind. Phytoplankton play a crucial role in the biogeochemical cycles of many important chemical elements, not only carbon but also of other elements, such as silica and nitrogen [1, 2, 3, 4]. The release and uptake of CO2 and CH4, and the excretion of dimethylsulphide by phytoplankton influence the atmosphere and climate [5]. As a result of the changes in their living condition, their composition and concentration vary over space and time, which in turn can influence the whole ecosystem, such as through the changes in the size structure, formation of harmful algal blooms and development of hypoxic regions. Blooms and hypoxia can disrupt food-webs and threaten human health.

Phytoplankton pigments capture sunlight. The resulting photosynthesis and its products, especially the oxygen and organic compounds, all rely on the light energy captured by the different phytoplankton pigments [6, 7, 8]. Chlorophyll a is the major pigment for light harvesting. Accessory pigments (e.g. chlorophylls b and c, carotenoids, and phycobiliproteins) also play a significant role in photosynthesis and photoprotection, by extending the light collection window and protecting the cell from damage of high irradiance levels or high ultraviolet light exposure. With the commercial availability of fluorometers, routine measurements of chlorophyll a became possible. That single technology to measure chlorophyll a fluorescence made the measurement a universal parameter for estimating phytoplankton biomass and productivity. As a result of improvements in culturing, microscopy, HPLC and molecular methods, rapidly separating and quantifying pigments from different phytoplankton has become possible [9, 10, 11]. These new measurements make it possible to use phytoplankton pigments as indicators to elucidate the composition and fate of phytoplankton in the world’s oceans [12].

Light absorbed by phytoplankton pigments provides the initial energy for carbon cycles, and is also one of the major factors influencing the appearance of water color [13, 14, 15, 16]. To study this important water column phenomenon, ocean color remote sensing was first proposed in late 1970s. Satellite-based ocean color remote sensing provides unique observational capability to scientists for phytoplankton studies by providing synoptic views of the ocean with high spatial and temporal resolution. Since the Coastal Zone Color Scanner (CZCS) mission, chlorophyll a retrieval has been the principle focus of ocean color remote sensing research (e.g., [17]). Whereas this focus continues to the present [18, 19, 20], an evolving interest in retrieving other pigments, has emerged in recent years.

What follows, based on the most recent research findings from the ocean color community, is a brief review of how phytoplankton pigments are estimated from water samples, how pigment maps are derived from satellite measurements and what are the existing challenges and opportunities for the estimates and application of remote sensed pigments. This chapter is not meant to present a comprehensive list of all possible topics related to satellite-based pigment observations, but rather its focus is on the history of pigment retrievals with several examples showing major findings. For interested readers, a full breadth and depth knowledge in this field can be obtained by reading the refereed literature and technical reports compiled on the National Aeronautics and Space Administration ocean color website (https://oceancolor.gsfc.nasa.gov) and by International Ocean Color Coordinating Group (http://www.ioccg.org).

Advertisement

2. Phytoplankton and pigment properties

2.1 Optical properties

2.1.1 Absorption properties

Optical properties of phytoplankton, especially the absorption coefficients of the pigments inside them (Figure 1), play a key role in determining not only the use of this radiant energy for photosynthesis, but also the penetration of the radiant energy within water. These pigment absorption coefficients are important for identifying and quantifying phytoplankton groups [12] and size class distributions (IOCCG report 15 and references therein), understanding of photosynthetic rate [11, 21], and in particular for ocean color interpretation.

Figure 1.

Weight-specific (or pigment-specific) in vitro absorption spectra of various pigments derived from measuring the absorption spectra of individual pigments in solvent and shifting the maxima of the spectra according to Bidigare et al. [14]. Data obtained courtesy of Annick Bricaud (See Bricaud et al. [15]). Credit to Moisan et al. [30].

Light absorption properties of phytoplankton cells from laboratory cultures as experimental materials have received a great deal of attention in fundamental photosynthesis research [22, 23]. However, the phytoplankton pigment absorption properties from natural water is the information needed in ocean color remote sensing. The collection of phytoplankton pigment information has been obtained from measurement of the spectral absorption of phytoplankton, usually through filtration onto a filter pad because of the low in situ concentrations of phytoplankton in the water [24].

Using data on pigment concentrations and their absorption properties, Kirkpatrick et al. [25] used the specific pigment absorption peaks for identification of phytoplankton types. This method has been integrated into spectral shape-based remote sensing algorithms [26, 27]. However, the absorption of phytoplankton is more complicated than a simple sum of the absorption properties of individual pigments. Differences in pigment composition and the pigment package effect influence not only the magnitude but also the shape of the spectra of phytoplankton absorption [14, 15, 28, 29, 30]. All these introduce variabilities in the specific absorption coefficients and increase the uncertainties in the application of such information.

Hoepffner and Sathyendranath [29] proposed Gaussian decomposition of phytoplankton absorption spectra. For the first time, this method decomposed the absorption spectra into Gaussian curve components and linked them to the light absorption coefficients of multiple pigments inside phytoplankton cells. Several studies followed this proxy to estimate multiple phytoplankton pigments for different water bodies [31, 32, 33] but were limited to using only in situ measured absorption coefficients. Wang et al. [34, 35] proposed a semi-analytical algorithm to obtain these Gaussian curves and pigment absorption coefficients from ocean color remote sensing data.

2.1.2 Fluorescence

A portion of the light absorbed by phytoplankton pigments can be emitted at a longer wavelength in a physical process called fluorescence [36]. The energy dissipated in fluorescence is secondary to the amount absorbed and used for photosynthesis, but it is still significant enough to be observed in ocean color remote sensing data. Chlorophyll a fluorescence has been the most significantly used fluorescence feature (Figure 2), and the detection and products from satellite ocean color sensors have been widely used [37, 38]. Several other phytoplankton pigments (pheopigments and phycobilins) can also fluoresce.

Figure 2.

Chlorophyll a fluorescence emission. Data from Du et al. [42] and Dixon et al. [43].

Several factors influence phytoplankton fluorescence: nutrient conditions, stage of growth, physiological state of phytoplankton, pigment content and ratios, taxonomic position of algae, and photoadaptation [39, 40, 41]. In situ chlorophyll fluorescence has been the most frequent method for describing the chlorophyll and phytoplankton variation and distribution in the ocean [41], but all the uncertainties from the pigment properties make the interpretation of the chlorophyll fluorescence data a challenge.

2.2 Pigment measurements

Historically, chlorophyll a has been routinely derived from filtered fluorometric measurements following standard methods using commercially availability of fluorometers. However, even standard methods yield varying results depending on the composition of pigments within the phytoplankton, and errors can be on the order of 50% [44, 45, 46]. The presence of significant amount of chlorophyll b and/or chlorophyll c, causes fluorometric techniques to under- or over-estimate Chlorophyll a with respect to fluorometric measurements [44, 45, 46, 47]. The pigment package effect is also a major source of concern.

The introduction of pigment analyses by high-pressure liquid chromatography (HPLC) [48, 49] facilitated easy and accurate separation, identification, and quantification of phytoplankton pigments. Pigment detection based on HPLC methods enables quantification of over 50 phytoplankton pigments [11, 50]. Some of the pigments can be used as diagnostic pigments for phytoplankton groups (e.g., fucoxanthin for diatoms, peridinin for dinoflagellates, alloxanthin for cryptophytes, chlorophyll b for chlorophytes, 19′-hex-fucoxanthin for haptophytes, and 19′-but-fucoxanthin for pelagophytes) [51, 52]. Moreover, diadinoxanthin and diatoxanthin are generally found in dinoflagellates (Phylum Miozoa, Class Dinophyceae) and diatoms (Phylum Bacillariophyta, Class Bacillariophyceae), whereas lutein, prasinoxanthin, neoxanthin, and violaxanthin are found in class Chlorophyceae (Phylum Chlorophyta) and class Prasinophyceae (Phylum Chlorophyta). Chlorophyll a, c, and β-carotene are used as general indicators of total algal biomass. Phytoplankton are also often categorized into three different groups: micro-phytoplankton (20–200 μm), nano-phytoplankton (2–20 μm), and pico-phytoplankton (0.2–2 μm) [53]. The contribution of each group can also be calculated using its pigment signatures [54].

Advertisement

3. Ocean color remote sensing

Ocean color or aquatic remote sensing refers to the use of optical measurements made from aircraft or satellites to obtain information about the constituents of the waters.

Remote sensing can be classified as active or passive based on the energy source. Active remote sensing shots signal from the sensor platform (satellite or aircraft) to the water body and detects the return signal from it. Passive remote sensing observes the light that is reflected or emitted by the water body. The most commonly used light source for passive remote sensing is sunlight. Sensors detect the reflected or backscattered light coming from the water body. The launch of the first ocean color sensor Coastal Zone Color Scanner (CZCS) in 1978, started the era for passive satellite ocean color remote sensing.

Passive ocean-color remote sensing is conceptually simple (Figure 3). The signals captured by remote sensors provide information on the types and concentrations of the various constituents of the water body. The concentrations of optically-active substances present in the water can be estimated by inverting bio-optical algorithms with remote sensing data. Although this process can be fraught with difficulties, our understanding of the oceans has been completely revolutionized by ocean color remote sensing from daily to decadal temporal scales and local to global spatial scales

Figure 3.

Conceptual figure of passive satellite ocean color remote sensing with Western Lake Erie as an example: Rrs(λ) as remote sensing reflectance, PC: pigment concentration.

For a better understanding of phytoplankton in the global ocean from large spatial and temporal scales, ocean color remote sensing is the most efficient tool, with the advantages of cost-free satellite imagery access from NASA and others, thus providing a data source for hypothesis testing and more efficient utilization of limited in situ data.

Phytoplankton pigments have a major effect on ocean color and are one of the primary reasons for studying it. Following the launch of CZCS, unprecedented data for studying the biology of the oceans have been obtained [55]. For the first time, chlorophyll a concentration in the surface ocean could be estimated at synoptic scales [56, 57], leading to unprecedented understanding of the biogeochemistry of the ocean, e.g., primary productivity [58]. These ocean-color observations were continued by the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission in 1997, which was then followed by the Moderate Resolution Imaging Spectroradiometer (MODIS on Terra in 2000, and Aqua in 2002), the Medium Resolution Imaging Spectrometer (MERIS, 2002–2012), the Visible Infrared Imaging Radiometer Suite (VIIRS, 2011 – present), and the upcoming hyperspectral Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission (planned to launch in 2023).

3.1 Remote sensing of pigments

In the past decades, the identification of phytoplankton pigments from satellite remote sensing has been mainly focused on chlorophyll a, and the products have been widely used to represent the phytoplankton biomass in the primary productivity estimation and biogeochemical models. With the increasing recognition of the important role accessory pigments play, remote sensing of pigments from space form this rapidly advancing field. High temporal and spatial monitoring are particularly important for the study of harmful algal blooms (HABs, e.g. cyanobacteria, [59, 60]). These blooms are often toxic and a growing problem in many coastal and inland waters of the world. A review of chlorophyll a algorithm for global oceans has been provided in recent papers including Dierssen [61] and Hu and Campbell [62]. In general, the method to obtain phytoplankton pigments from satellite remote sensing can be classified into two different categories: empirical, and semi-analytical.

3.1.1 Empirical methods

In the process of obtaining phytoplankton pigment, especially chlorophyll a (Chl-a) concentrations, most effort has focused on empirical algorithms, not only because of the simplicity, but also the effectiveness. The empirical methods estimate pigments from satellite derived remote sensing reflectance (Rrs(λ)) through regression of pigment concentrations against Rrs(λ) band ratios or band differences (e.g., [20, 63, 64]).

These methods account for regional variabilities in water properties and Rrs(λ) input errors through tuning of the empirical coefficients, although the empirical design makes it prone to influences from various in-water constituents. The spectrally dependent Rrs(λ) errors [65] to a large extent could be compensated through the band ratio or band difference used in empirical approaches. Thus, from the CZCS era, a set of empirical algorithms have been adopted by U.S. National Aeronautics and Space Administration (NASA) to produce the default Chl-a products from the existing ocean color satellite sensors, even though these empirical Chl-a products contain large uncertainties [61, 66].

For remote sensing of accessory pigments, Pan et al. [67] proposed to retrieve 17 different phytoplankton pigments from satellite remote sensing data using empirical methods and applied the information to phytoplankton group identification [68]. This method simply used empirical relationships between pigment concentrations with the ratio of two remote sensing reflectance bands (488 or 490 to 547 or 555 nm). However, same as Chl-a, in optically complicated coastal and inland waters, higher uncertainties could be introduced by the large influences from colored detrital matters (CDM) in coastal waters.

Eq. (1) shows the polynomial algorithm for pigments, in which the blue-green band ratio was empirically related to pigment concentrations (Cpigs):

log10Cpigs=a0+i=1Nailog10Rrsλ1Rrsλ2iE1

Where λ1 and λ2 represent the spectral band around blue (440–520) and green (555) region respectively, and a0aN are sensor specific regression coefficients. Details of the spectral bands and parameters used for each sensor can be found in [67] and on NASA ocean color website for Chl-a: https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/.

3.1.2 Semi-analytical algorithms

The semi-analytical algorithms obtain pigments from Rrs(λ) by solving a series of equations established from simplified radiative transfer theory based on several bio-optical assumptions (e.g., [69, 70, 71, 72, 73]). In principle, these methods have the potential to obtain more accurate results than the empirical methods because the different water constituents affecting water color are explicitly separated. However, semi-analytical approach has its own strengths and weaknesses. Semi-analytical methods rely on tuning of the empirical parameters in the bio-optical relationships using global or local datasets. As a result of the optical properties of the constituents, the separation of them from Rrs(λ) is not as explicit as expected.

Semi-analytical algorithms are relatively more complex. Based on the radiative transfer equation, remote sensing reflectance was defined as the ratio of upwelling radiance to downwelling irradiance, and its relationship with inherent optical properties of water constituents can be expressed as:

Rrsλ=Gbbwλ+bbpλawλ+aphλ+aCDOMλ+aNAPλ+bbwλ+bbpλE2

Where G is a parameter related to the environment and solar and sensor viewing geometry. The absorption coefficients of water (aw(λ)), phytoplankton (aph(λ)), colored dissolved organic matter (aCDOM(λ)), non-algal particles (aNAP(λ)), and backscattering coefficients of water (bbw(λ)) and particles (bbp(λ)).

Pigment concentrations can be estimated from phytoplankton absorption coefficients from Gaussian decomposition (Eqs. 3 and 4) or by using pigment specific absorption coefficients (Eq. 5). Figure 4 shows an example of Chl-a global distribution map obtained from MERIS ocean color data using a semi-analytical algorithm.

Figure 4.

Chlorophyll a map of the global ocean from MERIS for the year of 2007 with data from Wang et al. [74].

aphλ=i=1naGauλiexp0.5λλiσi2E3
log10Cpigs=a0+i=1nailog10aGauλiE4

where σi and aGau(λi) are the width and peak magnitude of the ith Gaussian curve at peak center (λi). As shown in Figure 1, in the Gaussian curve assumption in Hoepffner and Sathyendranath [29], each Gaussian curve represents the absorption curve of a specific pigment. Cpigs are pigment concentrations, with a0 and ai as empirical parameters [74].

aphλ=i=1NCpigiapigiE5

With a*pig as the pigment specific absorption coefficients [14, 15, 75, 76].

3.2 Application of remote sensed pigments

The measuring of ocean color from space and the increasing accuracy of in situ pigment measurements for determining phytoplankton groups and types in the water column have greatly facilitated progress in phytoplankton research.

Empirical algorithms used to calculate chlorophyll a concentration from ocean color data were established for different waters (e.g., [17, 19, 60, 63, 77, 78, 79]). The development and application of spectral inversion algorithms to ocean color data have further provided assessments of absorption by phytoplankton pigment [34, 71, 72, 80, 81, 82, 83]. Additional algorithm development using these properties has led to new retrievals regarding plankton community composition, including phytoplankton size fractions, the slope of the particle size distribution, and even specific phytoplankton groups, such as coccolithophores (Phylum Haptophyta, Class Coccolithophyceae), Trichodesmium (Phylum Cyanobacteria), and harmful algal species (e.g., [84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99] and references therein).

In recent years, the use of pigment data to map phytoplankton population and composition in the water column has become an established and convenient way of studying field phytoplankton [100]. Phytoplankton biomass and the structure of phytoplankton community have been widely quantified and assessed using photosynthetic pigment biomarkers [52, 100]. Photosynthetic pigments also function as indicators of the physiological condition of a phytoplankton community, which may be affected by environmental and trophic conditions [101]. Photosynthetic carotenoids (PSC) are dominant in high productivity waters, whereas photoprotective carotenoids (PPC) are more dominant in low productivity waters [102, 103]. In addition, intensive light increases the PPC:PSC ratio [104, 105]. Thus, the PPC:PSC ratio can be used as a good indicator of changes in environmental factors. Figure 5 shows the global maps of PPC and PSC from Wang et al. [74].

Figure 5.

Global maps of photoprotective (PPC) and photosynthetic carotenoids (PSC) from Wang et al. [74].

The sustained time series of these phytoplankton properties from ocean color remote sensing has provided major advances in our understanding of carbon dynamics, plankton annual cycles and their responses to climate variations. Simply, the satellite ocean color remote sensing of pigment will further improve the research revolution in oceanography.

Advertisement

4. Challenges and opportunities

4.1 Uncertainties in satellite remote sensing data

Although ocean color remote sensing observations enabled advances in our understanding of phytoplankton in the ocean, there are several fundamental limitations in the passive radiometric technique. The major uncertainties of remote sensing pigment estimates are from atmospheric correction errors, as a result of the high signal contribution of components other than the targeted water to radiances measured by ocean color instruments, such as reflection from the ocean surface, surface foam, subsurface bubbles, and atmospheric constituents, including clouds, aerosols, and air molecules. A small error from the correction of these atmospheric contribution results in large errors in the obtained remote sensing reflectance and the associated pigment information ([106] and references therein).

Another challenge with ocean color remote sensing comes from the interferences of the optical properties of retrieved water components, including absorption by phytoplankton pigments, colored dissolved matter, and nonalgal particles, and backscattering by suspended particles. This makes the uncertainties from these properties and the derived geophysical parameters from them hard to reduce. The upcoming PACE mission is designed with expanded spectral range and resolution to address this problem [107].

Finally, clouds and strongly scattering aerosol layers have been significant limitation factors of the availability of satellite ocean color data. On average, about 70% of the Earth’s ocean area were covered by clouds on the daily scene obtained from a sensor. For broken cloud or aerosol interfered scenes, the accuracy of ocean color retrievals can be compromised compared to clear sky pixels. In high altitude regions, specifically the polar regions, cloud conditions and low sun angles limited ocean color sampling from late fall through early spring of next year. The lack of sampling for this long period of time makes it impossible for a complete understanding of the biogeochemistry and plankton annual cycles of some of the most productive waters [108].

Other issues are from the limitation of spectral, spatial, and temporal resolutions of the existing satellite sensors: some harmful algal blooms occurring in small lakes and ponds are not able to be detected by satellite sensors with low spatial resolution (~1 km); while the high spatial resolution sensors (e.g., Landsat 8) cannot provide timely coverage of bloom events due to their low temporal resolution.

4.2 More accurate in situ measurements

The satellite ocean color remote sensing has been tasked to acquire remote sensing imagery, validate and monitor its accuracy, process the radiometric data into geophysical information using different algorithms, and apply the final products into scientific research. One of the principles of in situ datasets for the calibration and validation procedure is estimates of near-surface pigment concentrations [109]. Thus, accurate and complete pigment measurements are important to algorithm development as used with remote sensing of phytoplankton pigments. The application of pigment chemotaxonomy in oceanography will be more firmly established by advances in taxonomy and improved pigment analysis (e.g. greater resolution with advanced HPLC and ultra-high performance liquid chromatography – UPLC), more rapid and secure chemical identification, and further measurement and estimation of in vivo pigment absorption coefficients. With improvement in these techniques, more discoveries in pigment and taxonomic diversity and further understanding of their influences on the biogeochemical cycles of the ocean will be achieved. The current challenging environment from climate change makes this an urgent need [14, 15, 75, 76, 91, 110, 111].

4.3 Active remote sensing: LIDAR

Compared to passive ocean color remote sensing, lidar shows many advantages, such as operating at night and high latitudes, and can generally penetrate to the subsurface chlorophyll maximum [112, 113]. Airborne lidar is particularly useful for mapping the depth distribution of phytoplankton. The characteristic depth profiles of phytoplankton provide useful information for differentiation of phytoplankton species as described in Moore et al. [114] two different species of harmful Cyanobacteria in Lake Erie, USA can be identified by the differences in their characteristic depth profiles.

Combining the observations from lidar and ocean color sensors, especially the advanced upcoming PACE mission, would enable the achievement of greater synergies. The pairing of an ocean-optimized satellite profiling lidar with a passive ocean color sensor would provide maximized global data coverage, and enable three-dimensional reconstruction of ocean ecosystems, which would further favor the algorithm development, and expand the retrieval of geophysical properties.

Advertisement

Acknowledgments

We thank the National Aeronautics and Space Administration (NASA) for providing the MERIS imagery, and the support from the NASA Advanced Information Systems Technology (AIST) program.

References

  1. 1. Nelson, D. M., & Smith Jr, W. O. (1986). Phytoplankton bloom dynamics of the western Ross Sea ice edge—II. Mesoscale cycling of nitrogen and silicon. Deep Sea Research Part A. Oceanographic Research Papers, 33(10), 1389-1412
  2. 2. Tréguer, P., & Jacques, G. (1992). Review Dynamics of nutrients and phytoplankton, and fluxes of carbon, nitrogen and silicon in the Antarctic Ocean. In Weddell Sea Ecology (pp. 149-162). Springer, Berlin, Heidelberg
  3. 3. Van Bennekom, A. J., Berger, G. W., Van der Gaast, S. J., & De Vries, R. T. P. (1988). Primary productivity and the silica cycle in the Southern Ocean (Atlantic sector). Palaeogeography, Palaeoclimatology, Palaeoecology, 67(1-2), 19-30
  4. 4. Dortch, Q., & Whitledge, T. E. (1992). Does nitrogen or silicon limit phytoplankton production in the Mississippi River plume and nearby regions. Continental Shelf Research, 12(11), 1293-1309
  5. 5. Charlson, R. J., Lovelock, J. E., Andreae, M. O., & Warren, S. G. (1987). Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate. Nature, 326(6114), 655-661
  6. 6. Kirk, J. T. (1994). Light and photosynthesis in aquatic ecosystems. Cambridge university press
  7. 7. Behrenfeld, M. J., & Falkowski, P. G. (1997). Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnology and oceanography, 42(1), 1-20
  8. 8. Falkowski, P. G. (2002). The ocean’s invisible forest. Scientific American, 287(2), 54-61
  9. 9. Wright, S. W., Jeffrey, S. W., Mantoura, R. F. C., Llewellyn, C. A., Bjørnland, T., Repeta, D., & Welschmeyer, N. (1991). Improved HPLC method for the analysis of chlorophylls and carotenoids from marine phytoplankton. Marine ecology progress series, 183-196
  10. 10. Roy, S., Llewellyn, C. A., Egeland, E. S., & Johnsen, G. (Eds.). (2011). Phytoplankton pigments: characterization, chemotaxonomy and applications in oceanography. Cambridge University Press
  11. 11. Jeffrey, S.W.; Mantoura, R.F.C.; Wright, S.W. (Ed.) (1997). Phytoplankton pigments in oceanography: guidelines to modern methods. Monographs on Oceanographic Methodology, 10. UNESCO Publishing: Paris. ISBN 92-3-103275-5. 661 pp
  12. 12. Schlüter, L., Møhlenberg, F., Havskum, H., & Larsen, S. (2000). The use of phytoplankton pigments for identifying and quantifying phytoplankton groups in coastal areas: testing the influence of light and nutrients on pigment/chlorophyll a ratios. Marine Ecology progress series, 192, 49-63
  13. 13. Morel, A., & Prieur, L. (1977). Analysis of variations in ocean color 1. Limnology and oceanography, 22(4), 709-722
  14. 14. Bidigare, R. R., Ondrusek, M. E., Morrow, J. H., & Kiefer, D. A. (1990, September). In-vivo absorption properties of algal pigments. In Ocean Optics X (Vol. 1302, pp. 290-302). International Society for Optics and Photonics
  15. 15. Bricaud, A., Claustre, H., Ras, J., & Oubelkheir, K. (2004). Natural variability of phytoplanktonic absorption in oceanic waters: Influence of the size structure of algal populations. Journal of Geophysical Research: Oceans, 109(C11)
  16. 16. Gordon, H. R., Brown, O. B., Evans, R. H., Brown, J. W., Smith, R. C., Baker, K. S., & Clark, D. K. (1988). A semianalytic radiance model of ocean color. Journal of Geophysical Research: Atmospheres, 93(D9), 10909-10924
  17. 17. Gordon, H. R., Clark, D. K., Brown, J. W., Brown, O. B., Evans, R. H., & Broenkow, W. W. (1983). Phytoplankton pigment concentrations in the Middle Atlantic Bight: comparison of ship determinations and CZCS estimates. Applied optics, 22(1), 20-36
  18. 18. Morel, A., & Antoine, D. (2000). Pigment index retrieval in Case 1 waters. MERIS ATBD, 2
  19. 19. O'Reilly, J. E., Maritorena, S., Mitchell, B. G., Siegel, D. A., Carder, K. L., Garver, S. A., ... & McClain, C. (1998). Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research: Oceans, 103(C11), 24937-24953
  20. 20. O’Reilly, J. E., Maritorena, S., Siegel, D. A., O’Brien, M. C., Toole, D., Mitchell, B. G., ... & Hooker, S. B. (2000). Ocean color chlorophyll a algorithms for SeaWiFS, OC2, and OC4: Version 4. SeaWiFS postlaunch calibration and validation analyses, Part, 3, 9-23
  21. 21. Sosik, H. M., & Mitchell, B. G. (1995). Light absorption by phytoplankton, photosynthetic pigments and detritus in the California Current System. Deep Sea Research Part I: Oceanographic Research Papers, 42(10), 1717-1748
  22. 22. Laws, E. A., & Bannister, T. T. (1980). Nutrient-and light-limited growth of Thalassiosira fluviatilis in continuous culture, with implications for phytoplankton growth in the ocean 1. Limnology and Oceanography, 25(3), 457-473
  23. 23. Lubart, R., Lavi, R., Friedmann, H., & Rochkind, S. (2006). Photochemistry and photobiology of light absorption by living cells. Photomedicine and Laser Therapy, 24(2), 179-185
  24. 24. Mueller, J. L. (2002). Ocean optics protocols for satellite ocean color sensor validation
  25. 25. Kirkpatrick, G. J., Millie, D. F., Moline, M. A., & Schofield, O. (2000). Optical discrimination of a phytoplankton species in natural mixed populations. Limnology and Oceanography, 45(2), 467-471
  26. 26. Subramaniam, A., Brown, C. W., Hood, R. R., Carpenter, E. J., & Capone, D. G. (2001). Detecting Trichodesmium blooms in SeaWiFS imagery. Deep Sea Research Part II: Topical Studies in Oceanography, 49(1-3), 107-121
  27. 27. Tomlinson, M. C., Wynne, T. T., & Stumpf, R. P. (2009). An evaluation of remote sensing techniques for enhanced detection of the toxic dinoflagellate, Karenia brevis. Remote Sensing of Environment, 113(3), 598-609
  28. 28. Bricaud, A., & Stramski, D. (1990). Spectral absorption coefficients of living phytoplankton and nonalgal biogenous matter: A comparison between the Peru upwelling areaand the Sargasso Sea. Limnology and oceanography, 35(3), 562-582
  29. 29. Hoepffner, N., & Sathyendranath, S. (1991). Effect of pigment composition on absorption properties of phytoplankton. Mar. Ecol. Prog. Ser, 73(1), l-23
  30. 30. Moisan, J. R., Moisan, T. A., & Linkswiler, M. A. (2011). An inverse modeling approach to estimating phytoplankton pigment concentrations from phytoplankton absorption spectra. Journal of Geophysical Research: Oceans, 116(C9)
  31. 31. Hoepffner, N., & Sathyendranath, S. (1993). Determination of the major groups of phytoplankton pigments from the absorption spectra of total particulate matter. Journal of Geophysical Research: Oceans, 98(C12), 22789-22803
  32. 32. Lohrenz, S. E., Weidemann, A. D., & Tuel, M. (2003). Phytoplankton spectral absorption as influenced by community size structure and pigment composition. Journal of Plankton Research, 25(1), 35-61
  33. 33. Chase, A., Boss, E., Zaneveld, R., Bricaud, A., Claustre, H., Ras, J., ... & Westberry, T. K. (2013). Decomposition of in situ particulate absorption spectra. Methods in Oceanography, 7, 110-124
  34. 34. Wang, G., Lee, Z., Mishra, D. R., & Ma, R. (2016). Retrieving absorption coefficients of multiple phytoplankton pigments from hyperspectral remote sensing reflectance measured over cyanobacteria bloom waters. Limnology and Oceanography: Methods, 14(7), 432-447
  35. 35. Wang, G., Lee, Z., & Mouw, C. (2017). Multi-spectral remote sensing of phytoplankton pigment absorption properties in cyanobacteria bloom waters: a regional example in the western basin of Lake Erie. Remote Sensing. 9, no. 12: 1309. doi:10.3390/rs9121309
  36. 36. Babin, M. (2008). Phytoplankton fluorescence: theory, current literature and in situ measurement. Real-time coastal observing systems for marine ecosystem dynamics and harmful algal blooms, edited by: Babin, M., Roesler, C., and Cullen, JJ, 237-280
  37. 37. Neville, R. A., & Gower, J. F. R. (1977). Passive remote sensing of phytoplankton via chlorophyll α fluorescence. Journal of Geophysical Research, 82(24), 3487-3493
  38. 38. Huot, Y., Brown, C. A., & Cullen, J. J. (2005). New algorithms for MODIS sun-induced chlorophyll fluorescence and a comparison with present data products. Limnology and Oceanography: Methods, 3(2), 108-130
  39. 39. Prézelin, B. B., & Alberte, R. S. (1978). Photosynthetic characteristics and organization of chlorophyll in marine dinoflagellates. Proceedings of the National Academy of Sciences, 75(4), 1801-1804
  40. 40. Falkowski, P.G. and Owens T.G. (1980). Light-shade adaptation - 2 strategies in marine-phytoplankton. Plant Physiol. 66(4): 592-595
  41. 41. Cullen, J. J. (2008). Observation and prediction of harmful algal blooms. Real-time coastal observing system for marine ecosystem dynamics and harmful algal blooms: theory, Instrument and Modeling. UNESCO, Paris
  42. 42. Du, H., R.-C. A. Fuh, J. Li, L. A. Corkan and J. S. Lindsey (1998) PhotochemCAD: A computer-aided design and research tool in photochemistry. Photochem. Photobiol. 68, 141-142
  43. 43. Dixon, J. M., M. Taniguchi and J. S. Lindsey (2005), "PhotochemCAD 2. A Refined Program with Accompanying Spectral Databases for Photochemical Calculations, Photochem. Photobiol., 81, 212-213
  44. 44. Trees, C. C., Kennicutt II, M. C., & Brooks, J. M. (1985). Errors associated with the standard fluorimetric determination of chlorophylls and phaeopigments. Marine Chemistry, 17(1), 1-12
  45. 45. Trees, C. C., Clark, D. K., Bidigare, R. R., Ondrusek, M. E., & Mueller, J. L. (2000). Accessory pigments versus chlorophyll a concentrations within the euphotic zone: A ubiquitous relationship. Limnology and Oceanography, 45(5), 1130-1143
  46. 46. Kumari, B. (2005). Comparison of high performance liquid chromatography and fluorometric ocean colour pigments. Journal of the Indian Society of Remote Sensing, 33(4), 541-546
  47. 47. Lorenzen, C. J., & Jeffrey, S. W. (1980). Determination of chlorophyll in seawater. Unesco tech. pap. mar. sci, 35(1), 1-20
  48. 48. Abaychi, J. K., & Riley, J. P. (1979). The determination of phytoplankton pigments by high-performance liquid chromatography. Analytica Chimica Acta, 107, 1-11
  49. 49. Mantoura, R. F. C., & Llewellyn, C. A. (1983). The rapid determination of algal chlorophyll and carotenoid pigments and their breakdown products in natural waters by reverse-phase high-performance liquid chromatography. Analytica Chimica Acta, 151, 297-314
  50. 50. Aneeshkumar, N., & Sujatha, C. H. (2012). Biomarker pigment signatures in Cochin back water system–A tropical estuary south west coast of India. Estuarine, Coastal and Shelf Science, 99, 182-190
  51. 51. Barlow, R., Kyewalyanga, M., Sessions, H., Van den Berg, M., & Morris, T. (2008). Phytoplankton pigments, functional types, and absorption properties in the Delagoa and Natal Bights of the Agulhas ecosystem. Estuarine, Coastal and Shelf Science, 80(2), 201-211
  52. 52. Paerl, H. W., Valdes, L. M., Pinckney, J. L., Piehler, M. F., Dyble, J., & Moisander, P. H. (2003). Phytoplankton photopigments as indicators of estuarine and coastal eutrophication. BioScience, 53(10), 953-964
  53. 53. Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263
  54. 54. Vidussi, F., Claustre, H., Manca, B. B., Luchetta, A., & Marty, J. C. (2001). Phytoplankton pigment distribution in relation to upper thermocline circulation in the eastern Mediterranean Sea during winter. Journal of Geophysical Research: Oceans, 106(C9), 19939-19956
  55. 55. Hovis, W. A., Clark, D. K., Anderson, F., Austin, R. W., Wilson, W. H., Baker, E. T., ... & Sturm, B. (1980). Nimbus-7 Coastal Zone Color Scanner: system description and initial imagery. Science, 210(4465), 60-63
  56. 56. Gordon, H. R., Clark, D. K., Mueller, J. L., & Hovis, W. A. (1980). Phytoplankton pigments from the Nimbus-7 Coastal Zone Color Scanner: comparisons with surface measurements. Science, 210(4465), 63-66
  57. 57. Smith, R. C., & Baker, K. S. (1982). Oceanic chlorophyll concentrations as determined by satellite (Nimbus-7 Coastal Zone Color Scanner). Marine Biology, 66(3), 269-279
  58. 58. Mitchell, B. G. (1994). Coastal zone color scanner retrospective. Journal of Geophysical Research: Oceans, 99(C4), 7291-7292
  59. 59. Simis, S. G., Peters, S. W., & Gons, H. J. (2005). Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water. Limnology and Oceanography, 50(1), 237-245
  60. 60. Mishra, S., Mishra, D. R., Lee, Z., & Tucker, C. S. (2013). Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: A quasi-analytical approach. Remote Sensing of Environment, 133, 141-151
  61. 61. Dierssen, H. M. (2010). Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate. Proceedings of the National Academy of Sciences, 107(40), 17073-17078
  62. 62. Hu, C., & Campbell, J. (2014). Oceanic chlorophyll-a content. In Biophysical applications of satellite remote sensing (pp. 171-203). Springer, Berlin, Heidelberg
  63. 63. Hu, C., Lee, Z., & Franz, B. (2012). Chlorophyll aalgorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1)
  64. 64. Kahru, M., & Mitchell, B. G. (1999). Empirical chlorophyll algorithm and preliminary SeaWiFS validation for the California Current. International Journal of Remote Sensing, 20(17), 3423-3429
  65. 65. Hu, C., Feng, L., & Lee, Z. (2013). Uncertainties of SeaWiFS and MODIS remote sensing reflectance: Implications from clear water measurements. Remote Sensing of Environment, 133, 168-182
  66. 66. Szeto, M., Werdell, P. J., Moore, T. S., & Campbell, J. W. (2011). Are the world's oceans optically different?. Journal of Geophysical Research: Oceans, 116(C7)
  67. 67. Pan, X., Mannino, A., Russ, M. E., Hooker, S. B., & Harding Jr, L. W. (2010). Remote sensing of phytoplankton pigment distribution in the United States northeast coast. Remote Sensing of Environment, 114(11), 2403-2416
  68. 68. Pan, X., Mannino, A., Marshall, H. G., Filippino, K. C., & Mulholland, M. R. (2011). Remote sensing of phytoplankton community composition along the northeast coast of the United States. Remote Sensing of Environment, 115(12), 3731-3747
  69. 69. Carder, K. L., Chen, F. R., Lee, Z. P., Hawes, S. K., & Kamykowski, D. (1999). Semianalytic Moderate-Resolution Imaging Spectrometer algorithms for chlorophyll a and absorption with bio-optical domains based on nitrate-depletion temperatures. Journal of Geophysical Research: Oceans, 104(C3), 5403-5421
  70. 70. IOCCG (2006). Remote Sensing of Inherent Optical Properties: Fundamentals, Tests of Algorithms, and Applications. Lee, Z.-P. (ed.), Reports of the International Ocean-Colour Coordinating Group, No. 5, IOCCG, Dartmouth, Canada
  71. 71. Lee, Z., Carder, K. L., & Arnone, R. A. (2002). Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. Applied optics, 41(27), 5755-5772
  72. 72. Maritorena, S., Siegel, D. A., & Peterson, A. R. (2002). Optimization of a semianalytical ocean color model for global-scale applications. Applied optics, 41(15), 2705-2714
  73. 73. Sathyendranath, S., Prieur, L., & Morel, A. (1989). A three-component model of ocean colour and its application to remote sensing of phytoplankton pigments in coastal waters. International Journal of Remote Sensing, 10(8), 1373-1394
  74. 74. Wang, G., Lee, Z., & Mouw, C. B. (2018). Concentrations of multiple phytoplankton pigments in the global oceans obtained from satellite ocean color measurements with MERIS. Applied Sciences, 8(12), 2678
  75. 75. Wozniak, B., Dera, J., Ficek, D., Majchrowski, R., Kaczmarek, S., Ostrowska, M., & Koblentz-Mishke, O. I. (2000). Model of the in vivo spectral absorption of algal pigments. Part 1. Mathematical apparatus. Oceanologia, 42(2)
  76. 76. Moisan, T. A., Rufty, K. M., Moisan, J. R., & Linkswiler, M. A. (2017). Satellite observations of phytoplankton functional type spatial distributions, phenology, diversity, and ecotones. Frontiers in Marine Science, 4, 189
  77. 77. Sathyendranath, S., Cota, G., Stuart, V., Maass, H., & Platt, T. (2001). Remote sensing of phytoplankton pigments: a comparison of empirical and theoretical approaches. International Journal of Remote Sensing, 22(2-3), 249-273
  78. 78. Gitelson, A. A., Schalles, J. F., & Hladik, C. M. (2007). Remote chlorophyll-a retrieval in turbid, productive estuaries: Chesapeake Bay case study. Remote Sensing of Environment, 109(4), 464-472
  79. 79. Abbott, M. R., & Letelier, R. M. (1999). Algorithm theoretical basis document chlorophyll fluorescence (MODIS product number 20). NASA (http://www. modis. gsfc. nasa. gov/data/atbd)
  80. 80. Garver, S. A., & Siegel, D. A. (1997). Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation: 1. Time series from the Sargasso Sea. Journal of Geophysical Research: Oceans, 102(C8), 18607-18625
  81. 81. Siegel, D. A., Maritorena, S., Nelson, N. B., Hansell, D. A., & Lorenzi-Kayser, M. (2002). Global distribution and dynamics of colored dissolved and detrital organic materials. Journal of Geophysical Research: Oceans, 107(C12), 21-1
  82. 82. Siegel, D. A., Maritorena, S., Nelson, N. B., & Behrenfeld, M. J. (2005). Independence and interdependencies among global ocean color properties: Reassessing the bio-optical assumption. Journal of Geophysical Research: Oceans, 110(C7)
  83. 83. Werdell, P. J., Franz, B. A., Bailey, S. W., Feldman, G. C., Boss, E., Brando, V. E., ... & Loisel, H. (2013). Generalized ocean color inversion model for retrieving marine inherent optical properties. Applied optics, 52(10), 2019-2037
  84. 84. Uitz, J., Claustre, H., Morel, A., & Hooker, S. B. (2006). Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll. Journal of Geophysical Research: Oceans, 111(C8)
  85. 85. Hirata, T., Aiken, J., Hardman-Mountford, N., Smyth, T. J., & Barlow, R. G. (2008). An absorption model to determine phytoplankton size classes from satellite ocean colour. Remote Sensing of Environment, 112(6), 3153-3159
  86. 86. Brewin, R. J., Sathyendranath, S., Hirata, T., Lavender, S. J., Barciela, R. M., & Hardman-Mountford, N. J. (2010). A three-component model of phytoplankton size class for the Atlantic Ocean. Ecological Modelling, 221(11), 1472-1483
  87. 87. Kostadinov, T. S., Siegel, D. A., & Maritorena, S. (2009). Retrieval of the particle size distribution from satellite ocean color observations. Journal of Geophysical Research: Oceans, 114(C9)
  88. 88. Ciotti, A. M., & Bricaud, A. (2006). Retrievals of a size parameter for phytoplankton and spectral light absorption by colored detrital matter from water-leaving radiances at SeaWiFS channels in a continental shelf region off Brazil. Limnology and Oceanography: Methods, 4(7), 237-253
  89. 89. Mouw, C. B., & Yoder, J. A. (2010). Optical determination of phytoplankton size composition from global SeaWiFS imagery. Journal of Geophysical Research: Oceans, 115(C12)
  90. 90. Devred, E., Sathyendranath, S., Stuart, V., & Platt, T. (2011). A three component classification of phytoplankton absorption spectra: Application to ocean-color data. Remote Sensing of Environment, 115(9), 2255-2266
  91. 91. Bricaud, A., Ciotti, A. M., & Gentili, B. (2012). Spatial-temporal variations in phytoplankton size and colored detrital matter absorption at global and regional scales, as derived from twelve years of SeaWiFS data (1998-2009). Global Biogeochemical Cycles, 26(1)
  92. 92. Alvain, S., Moulin, C., Dandonneau, Y., & Bréon, F. M. (2005). Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery. Deep Sea Research Part I: Oceanographic Research Papers, 52(11), 1989-2004
  93. 93. Mustapha, Z. B., Alvain, S., Jamet, C., Loisel, H., & Dessailly, D. (2014). Automatic classification of water-leaving radiance anomalies from global SeaWiFS imagery: application to the detection of phytoplankton groups in open ocean waters. Remote sensing of environment, 146, 97-112
  94. 94. Bracher, A., Vountas, M., Dinter, T., Burrows, J. P., Röttgers, R., & Peeken, I. (2009). Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data. Biogeosciences, 6, 751-764
  95. 95. Sadeghi, A., Dinter, T., Vountas, M., Taylor, B., Peeken, I., Altenburg Soppa, M., & Bracher, A. (2012). Improvements to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data. Ocean Science, 8(6), 1055-1070
  96. 96. Raitsos, D. E., Lavender, S. J., Maravelias, C. D., Haralabous, J., Richardson, A. J., & Reid, P. C. (2008). Identifying four phytoplankton functional types from space: An ecological approach. Limnology and oceanography, 53(2), 605-613
  97. 97. Palacz, A., John, M. S., Brevin, R. J. W., Hirata, T., & Gregg, W. W. (2013). Distribution of phytoplankton functional types in high-nitrate low-chlorophyll waters in a new diagnostic ecological indicator model. Biogeosciences, 10(11), 7553-7574
  98. 98. Kostadinov, T. S., Cabré, A., Vedantham, H., Marinov, I., Bracher, A., Brewin, R. J., ... & Mouw, C. (2017). Inter-comparison of phytoplankton functional type phenology metrics derived from ocean color algorithms and earth system models. Remote sensing of environment, 190, 162-177
  99. 99. IOCCG (2014). Phytoplankton Functional Types from Space. Sathyendranath, S. (ed.), Reports of the International Ocean-Colour Coordinating Group, No. 15, IOCCG, Dartmouth, Canada
  100. 100. Wright, S. W., & Jeffrey, S. W. (2006). Pigment markers for phytoplankton production. In Marine organic matter: biomarkers, isotopes and DNA (pp. 71-104). Springer, Berlin, Heidelberg
  101. 101. Roy, S., Alam, S., & Chattopadhyay, J. (2006). Competing effects of toxin-producing phytoplankton on overall plankton populations in the Bay of Bengal. Bulletin of Mathematical Biology, 68(8), 2303-2320
  102. 102. Barlow, R. G., Aiken, J., Holligan, P. M., Cummings, D. G., Maritorena, S., & Hooker, S. (2002). Phytoplankton pigment and absorption characteristics along meridional transects in the Atlantic Ocean. Deep Sea Research Part I: Oceanographic Research Papers, 49(4), 637-660
  103. 103. Gibb, S. W., Barlow, R. G., Cummings, D. G., Rees, N. W., Trees, C. C., Holligan, P., & Suggett, D. (2000). Surface phytoplankton pigment distributions in the Atlantic Ocean: an assessment of basin scale variability between 50 N and 50 S. Progress in Oceanography, 45(3-4), 339-368
  104. 104. Moreno, D. V., Marrero, J. P., Morales, J., García, C. L., Úbeda, M. V., Rueda, M. J., & Llinás, O. (2012). Phytoplankton functional community structure in Argentinian continental shelf determined by HPLC pigment signatures. Estuarine, Coastal and Shelf Science, 100, 72-81
  105. 105. Vijayan, A. K., Yoshikawa, T., Watanabe, S., Sasaki, H., Matsumoto, K., Saito, S. I., ... & Furuya, K. (2009). Influence of non-photosynthetic pigments on light absorption and quantum yield of photosynthesis in the western equatorial Pacific and the subarctic North Pacific. Journal of oceanography, 65(2), 245-258
  106. 106. IOCCG (2019). Uncertainties in Ocean Colour Remote Sensing. Mélin F. (ed.), IOCCG Report Series, No. 18, International Ocean Colour Coordinating Group, Dartmouth, Canada. http://dx.doi.org/10.25607/OBP696
  107. 107. Werdell, P. J., Behrenfeld, M. J., Bontempi, P. S., Boss, E., Cairns, B., Davis, G. T., & Knobelspiesse, K. D. (2019). The Plankton, Aerosol, Cloud, ocean Ecosystem mission: status, science, advances. Bulletin of the American Meteorological Society, 100(9), 1775-1794
  108. 108. Behrenfeld, M. J., Hu, Y., O’Malley, R. T., Boss, E. S., Hostetler, C. A., Siegel, D. A., ... & Rodier, S. (2017). Annual boom–bust cycles of polar phytoplankton biomass revealed by space-based lidar. Nature Geoscience, 10(2), 118-122
  109. 109. Hooker, S. B., & McClain, C. R. (2000). The calibration and validation of SeaWiFS data. Progress in Oceanography, 45(3-4), 427-465
  110. 110. Nair, A., Sathyendranath, S., Platt, T., Morales, J., Stuart, V., Forget, M. H., ... & Bouman, H. (2008). Remote sensing of phytoplankton functional types. Remote Sensing of Environment, 112(8), 3366-3375
  111. 111. Hallegraeff, G. M. (2010). Ocean climate change, phytoplankton community responses, and harmful algal blooms: a formidable predictive challenge 1. Journal of phycology, 46(2), 220-235
  112. 112. Hostetler, C. A., Behrenfeld, M. J., Hu, Y., Hair, J. W., & Schulien, J. A. (2018). Spaceborne lidar in the study of marine systems. Annual review of marine science, 10, 121-147
  113. 113. Churnside, J. H., & Shaw, J. A. (2020). Lidar remote sensing of the aquatic environment. Applied Optics, 59(10), C92-C99
  114. 114. Moore, T. S., Churnside, J. H., Sullivan, J. M., Twardowski, M. S., Nayak, A. R., McFarland, M. N., ... & Ruberg, S. A. (2019). Vertical distributions of blooming cyanobacteria populations in a freshwater lake from LIDAR observations. Remote Sensing of Environment, 225, 347-367

Written By

Guoqing Wang and John Moisan

Submitted: 25 November 2020 Reviewed: 04 December 2020 Published: 05 May 2021