**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 biooptical 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

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,

#### **Figure 3.**

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

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 (*R*rs(λ)) through regression of pigment concentrations against *R*rs(λ) band ratios or band differences (e.g., [20, 63, 64]).

These methods account for regional variabilities in water properties and *R*rs(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 *R*rs(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

*Remote Sensing of Phytoplankton Pigments DOI: http://dx.doi.org/10.5772/intechopen.95381*

[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):

$$\log\_{10}\left(\mathbf{C}\_{\text{p\%}}\right) = a\_0 + \sum\_{i=1}^{N} a\_i \left(\log\_{10}\left(\frac{R\_{\text{ref}}\left(\mathcal{A}\_1\right)}{R\_{\text{ref}}\left(\mathcal{A}\_2\right)}\right)\right)^i \tag{1}$$

Where λ1 and λ2 represent the spectral band around blue (440–520) and green (555) region respectively, and *a*0 – *a*N 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 *R*rs(λ) by solving a series of equations established from simplified radiative transfer theory based on several bio-optical assumptions (e.g., [69–73]). 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 *R*rs(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:

$$R\_n\left(\boldsymbol{\lambda}\right) = G \frac{b\_{bw}\left(\boldsymbol{\lambda}\right) + b\_{bp}\left(\boldsymbol{\lambda}\right)}{a\_w\left(\boldsymbol{\lambda}\right) + a\_{pl}\left(\boldsymbol{\lambda}\right) + a\_{\text{CDM}}\left(\boldsymbol{\lambda}\right) + a\_{\text{MAP}}\left(\boldsymbol{\lambda}\right) + b\_{bw}\left(\boldsymbol{\lambda}\right) + b\_{bp}\left(\boldsymbol{\lambda}\right)}\tag{2}$$

Where G is a parameter related to the environment and solar and sensor viewing geometry. The absorption coefficients of water (*a*w(λ)), phytoplankton (*a*ph(λ)), colored dissolved organic matter (*a*CDOM(λ)), non-algal particles (*a*NAP(λ)), and backscattering coefficients of water (*b*bw(λ)) and particles (*b*bp(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.

$$\left| \boldsymbol{a}\_{ph} \left( \boldsymbol{\lambda} \right) = \sum\_{i=1}^{u} \boldsymbol{a}\_{\text{Gau}} \left( \boldsymbol{\lambda}\_{i} \right) \exp \left[ -\mathbf{0}.5 \left( \frac{\boldsymbol{\lambda} - \boldsymbol{\lambda}\_{i}}{\sigma\_{i}} \right)^{2} \right] \tag{3}$$

$$\log \mathbf{g}\_{10} \left( \mathbf{C}\_{\text{pğs}} \right) = a\_0 + \sum\_{i=1}^{n} a\_i \log\_{10} \left( a\_{\text{Gum}} \left( \boldsymbol{\lambda}\_i \right) \right) \tag{4}$$

where σi and *a*Gau(*λ*i) are the width and peak magnitude of the *i*th Gaussian curve at peak center (*λ*i). 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 *a*0 and *a*i as empirical parameters [74].

$$\mathcal{a}\_{ph}\left(\mathcal{A}\right) = \sum\_{i=1}^{N} \mathbf{C}\_{\text{p4gi}} \mathbf{a}\_{pigi}^{\*}\tag{5}$$

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, 77–79]). The development and application of spectral inversion algorithms to ocean color data have further provided assessments of absorption by phytoplankton pigment [34, 71, 72, 80–83]. 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., [84–99] 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,

**Figure 4.**

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

*Remote Sensing of Phytoplankton Pigments DOI: http://dx.doi.org/10.5772/intechopen.95381*

**Figure 5.**

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

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].

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.
