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ISSN : 1225-6692(Print)
ISSN : 2287-4518(Online)
Journal of the Korean earth science society Vol.40 No.4 pp.315-328
DOI : https://doi.org/10.5467/JKESS.2019.40.4.315

Overview of Chlorophyll-a Concentration Retrieval Algorithms from Multi-Satellite Data

Ji-Eun Park1, Kyung-Ae Park2*, Young-Je Park3, Hee-Jeong Han3
1Department of Science Education, Seoul National University, Seoul 08826, Korea
2Department of Earth Science Education/Research Institute of Oceanography, Seoul National University, Seoul 08826, Korea
3Korea Ocean Satellite Center, Korea Institute of Ocean Science and Technology, Busan 49111, Korea
Corresponding author: kapark@snu.ac.kr Tel: +82-2-880-7780
August 10, 2019 August 26, 2019 August 26, 2019

Abstract


Since the Coastal Zone Color Scanner (CZCS)/Nimbus-7 was launched in 1978, a variety of studies have been conducted to retrieve ocean color variables from multi-satellites. Several algorithms and formulations have been suggested for estimating ocean color variables based on multi band data at different wavelengths. Chlorophyll-a (chl-a) concentration is one of the most important variables to understand low-level ecosystem in the ocean. To retrieve chl-a concentrations from the satellite observations, an appropriate algorithm depending on water properties is required for each satellite sensor. Most operational empirical algorithms in the global ocean have been developed based on the band-ratio approach, which has the disadvantage of being more adapted to the open ocean than to coastal areas. Alternative algorithms, including the semi-analytical approach, may complement the limits of band-ratio algorithms. As more sensors are planned by various space agencies to monitor the ocean surface, it is expected that continuous monitoring of oceanic ecosystems and environments should be conducted to contribute to the understanding of the oceanic biosphere and the impact of climate change. This study presents an overview of the past and present algorithms for the estimation of chl-a concentration based on multi-satellite data and also presents the prospects for ongoing and upcoming ocean color satellites.



초록


    Ministry of Land, Transport and Maritime Affairs

    Introduction

    The pigment chlorophyll-a (chl-a) is found in all phytoplankton in the ocean, and is responsible for photosynthesis because it absorbs light to provide energy. The distribution of the chl-a concentration is a good indicator of the areas that are capable of maintaining the oceanic biosphere because phytoplankton form low-trophic level feeding grounds. Therefore, measuring the chl-a concentration in the ocean enables the estimation of primary productivity from marine biology and conveys essential information about the carbon cycle and other biogeochemical processes (e.g., Bricaud et al., 1995;O’Reilly et al., 1998). Given that chl-a influences the ocean color by backscattering spectrally localized water-leaving radiance, ocean color sensors are designed to measure optical parameters from the ocean surface remotely (Robinson, 2004).

    The emergence of ocean color satellite profoundly enriched the oceanographers’ understanding of the low-trophic level marine biomass, by providing unprecedented observations of the global ocean (Fig. 1) (Yoder et al., 1988;Feldman et al., 1989;Aiken et al., 1992;McClain, 1993;Mitchell, 1994;O’Reilly et al., 1998). Numerous geophysical marine phenomena that are induced by physical and biological interactions, including mesoscale features and air-sea interactions such as storm-induced effects and surface currents, can be inferred from the satellite observed chl-a concentration images (e.g., Babin et al., 2007;McClain, 2009;Park et al., 2016;Park et al., 2018). There are also numerous benefits to society derived from the ocean color imagery, including coastal ecosystem management, fisheries, and detection of harmful algal blooms (HABs) (e.g., IOCCG, 2008;Klemas, 2011;Wilson, 2011;Shen et al., 2012;Blondeau-Patissier et al., 2014;Kratzer et al., 2014).

    Global scale observations are typically performed by near-polar satellites at an altitude of approximately 700-800 km, which cover the Earth’s surface within 2- 3 days (Fig. 2). After the successful Coastal Zone Color Scanner (CZCS)/Nimbus-7 (launched in 1978) demonstrated that quantitative estimations of geophysical variables could be derived from top of the atmosphere radiance, many near-polar orbit satellites from the Seaviewing Wide Field-of-view Sensor (SeaWiFS)/ OrbView-2 instrument launched in 1997 to the Visible Infrared Imaging Radiometer Suite (VIIRS)/Joint Polar Satellite System (JPSS)-1 launched in 2017 have dedicated to ocean color missions. Unlike the nearpolar satellites, geostationary satellites operate at the hourly time scale for a fixed area. The Geostationary Ocean Color Imager (GOCI)/Communication, Ocean & Meteorological Satellite (COMS), the world’s first geostationary ocean color sensor, was launched in 2010 and is also observing the seas off the Korean Peninsula. These successive ocean color missions have monitored global ocean surface on a near-real-time basis (Figs. 3-4).

    The algorithms for converting satellite observations into oceanic variables usually work based on the remote sensing reflectance (Rrs) (O’Reilly et al., 1998). Two types of algorithm have been historically employed to derive chl-a concentration from satellite-derived radiance (Dierssen, 2010;Hu and Campbell, 2014). The first is the empirical algorithm, which works via regression, sequencing the blue-to-green wavelength ratio or the difference of Rrs against chl-a concentration (e.g., Kahru and Mitchell, 1999;O’Reilly et al., 2000;Hu et al., 2012). The second is the semi-analytical algorithm, which operates by solving equations of chl-a concentration and water-leaving radiance established from models of the radiative transfer theory using certain bio-optical assumptions (e.g., Sathyendranath et al., 1989;Carder et al., 1999;Lee et al., 2002;Maritorena et al., 2002;IOCCG, 2006). In addition to these two algorithm types, hybrid or neural network (NN)-based algorithms have also been developed (e.g., Doerffer and Schiller, 2007;Hu et al., 2012, 2019).

    Case 1 waters refer to those whose optical properties are dominated by phytoplankton and their associated degradation products (Morel and Prieur, 1977). Case 2 waters, meanwhile, is influenced by the optical interference of both suspended particulate matter (SPM) and colored dissolved organic matter (CDOM) (Carder et al., 1991;Hu et al., 2000;Pradhan et al., 2005;Tilstone et al., 2013;Bukata et al., 2018). Unlike the oceans, coastal waters are affected by a large quantity of particulate material, land-derived yellow substance, river inputs, and land drainage. In general, global algorithms are only suitable for Case 1 water, which is the open ocean. By contrast, Case 2 water under the influence of continental discharge may not fit a general global algorithm, and a local algorithm is needed (e.g., Ruddick et al., 2001;Gitelson et al., 2009;Moses et al., 2009). Therefore, it is important to develop and tune the appropriate chl-a retrieval algorithm depending on the spectral characteristics of each sea area studied and ocean color sensor used (Gitelson et al., 1996;Garcia et al., 2005;Hattab et al., 2013). Deriving accurate chl-a data products from measurements several hundreds of kilometers above the Earth requires a thorough understanding of the ocean color measurements including the specifications of the ocean color sensors, and development of biooptical algorithms. This article presents a review of the main ocean color sensors, retrieval algorithms for chl-a concentration, and its assessment that have been operated so far.

    Ocean Color Satellites

    Near-Polar Orbiting Ocean Color Satellites

    The first global map of the near-surface marine biomass was obtained by CZCS, which operated from 1978 to 1986 (e.g., Clarke et al., 1970;Hovis et al., 1981;Hooker et al., 1992). Although the first marine biosphere composite map could not cover the entire ocean surface, it clearly demonstrated the effectiveness of remote sensing for monitoring phytoplankton at a global scale, which was previously not feasible using ships. CZCS observed ocean surface with 6 bands ranging from 433 to 12,500 nm (Table 1). In the early years of ocean color remote sensing, the final product of CZCS was considered a pigment concentration of chl-a plus phaeopigments because determining chl-a based on the tri-chrometric spectrometric method (Strickland and Parsons, 1972) or fluorescence method (Yentsch and Menzel, 1963) was imprecise due to the presence of other interfering pigments (Aiken et al., 1995). Afterwards, with the development of SeaWiFS, retrieval of chl-a concentration from CZCS data is also available by using a conversion equation from pigment to chl-a concentration or the chl-a concentration retrieval algorithm for CZCS (O’Reilly et al., 1998).

    SeaWiFS followed CZCS, from 1997 to 2010, with 8 spectral bands ranging from 412 to 865 nm; it collected global data at a resolution of 4 km and local data at 1 km (Hooker et al., 1992). As ocean biology is strongly influenced by physical properties (temperature, salinity, light) and dynamics (mixing, upwelling, advection), the fact that accurate time series of geophysical variables such as sea surface temperature, wind speed, and sea level were readily available for correlative analyses with SeaWiFS data meant a considerable improvement for the ocean color community (McClain, 2009). Many studies provided detailed statistical analyses of the global biological variability in the context of physical dynamics using SeaWiFS data (e.g., Wilson and Adamec, 2002;Doney et al., 2003;Yoder and Kennelly, 2003;Uz and Yoder, 2004;Wilson and Coles, 2005).

    The Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua has been active since 2002 for the longest period among the ocean color missions and continue to play an important role in establishing long-term time-series ocean color data. MODIS acquires ocean surface data in 36 spectral bands. The total spectral range of MODIS is 0.4 to 14.4 μm, covering visible to long-wave infra-red spectral regions with the resolution of from 250 m to 1 km depending on the wavelength. MODIS is also a key instrument on board of Terra satellite (2000-present). Terra and Aqua cross the equator three hours apart: Terra at 10:30 a.m., descending, and Aqua at 1:30 p.m., ascending, so that MODIS achieves near global coverage for one day. The Ocean Biology Processing Group (OBPG) at NASA Goddard Space Flight Center produces operational ocean color products from the MODIS/Aqua. However documented uncertainties and instabilities have inhibited the production from MODIS/Terra due to degradation of the radiometric response and damage of the mirror in the sensor (Franz et al., 2008). That is why MODIS/Aqua is more widely used for research studies about ocean color compared to MODIS/Terra.

    The MEdium Resolution Imaging Spectrometer (MERIS) was one of the main instruments on board the European Space Agency (ESA)'s Environmental Satellite (ENVISAT)-1 platform, which operated from 2002 to 2012, that was capable of sensing in the 390 nm to 1040 nm spectral range using 15 bands. The primary objective of MERIS was to observe ocean color, both in the open ocean and in coastal zones (Rast et al., 1999). The full resolution (FR) and reduced resolution (RR) data were of approximately 300 and 1,200 m on ground, respectively. The Ocean Land Color Instrument (OLCI) onboard Sentinel-3 is an optical instrument that provides data continuity to the MERIS capability. OLCI and MERIS have similar spectral range and spatial resolution; however, six bands were added to OLCI to improve the atmospheric and aerosol correction capabilities (400 nm and 1.02 μm), cloud top pressure (764.4 and 767.5 nm), water vapor retrieval (940 nm), and chlorophyll fluorescence measurement (673 nm) (Donlon et al., 2012). Sentinel- 3A, launched in 2016, and Sentinel-3B, wich followed in 2018, work in a pair to cover the ground. Sentinel- 3C and Sentinel-3D are also planned to be launched from 2021 onwards.

    The Joint Polar Satellite System (JPSS) was created as the next generation of polar orbiting satellites from NOAA/NASA. JPSS consists of Suomi-NPP, launched in 2011, and JPSS-1 (also known as NOAA-20), launched in 2017, and have the VIIRS instrument in common. The VIIRS sensor was designed to extend and improve upon the series of measurements initiated by its predecessors, the Advanced Very High Resolution Radiometer (AVHRR), MODIS, and SeaWIFS. VIIRS is performed simultaneously in 22 spectral bands providing global coverage with a resolution of 750 m. VIIRS will also be flown on the JPSS-2, -3, and -4 satellite missions.

    Near-polar orbiting ocean color missions have provided surface marine-optical parameters for almost three decades, from qualitative to quantitative estimates by observing the global ocean surface in near-realtime (Blondeau-Patissier et al., 2014). Low-Earth orbit satellites, such as Landsat series, have higher spatial resolution, of a few to 30 m, and narrower swath width under 200 km compared to the near-polar orbiting ocean color missions. Based on these characteristics of low-Earth orbit satellites, they are actively used for monitoring the coastal and in-land waters rather than the global researches (e.g., Urbanski et al., 2016;Boucher et al., 2018).

    Geostationary Ocean Color Satellites

    Geostationary imagers are highlighted as the new generation of ocean color capabilities (IOCCG, 2012;Blondeau-Patissier et al., 2014). Global warming has profound implications, from the coastal to open-ocean ecosystems (e.g., Harley et al., 2006;Hoegh-Guldberg and Bruno, 2010). Geostationary satellites can focus on more limited areas with time-consecutive measurements. GOCI/COMS has observed seas around the Korean Peninsula using 16 slots from 2011 at hourly intervals up to 8 times a day (Fig. 4a). GOCI was designed to have a spectral composition of 8 bands from 412 to 865 nm, similar to that of SeaWiFS. Based on hourly-based observations with 500 m spatial resolution, GOCI provides scientific opportunities including short-time air-sea interaction response and hourly ocean surface movements, which was not possible with polar-orbiting satellites (Fig. 4b) (e.g., Park et al., 2016;Park et al., 2018). The Advanced Himawari Imager (AHI) carried by Himawari-8, the Japanese Meteorological Agency (JMA) geostationary meteorological satellite (GMS) series, also has visible bands (with total spectral range from 0.47 to 13.3 μm) and performs the full disk by every 10 min from 2014 (Lim et al., 2018). However, some problems such as noise in the mid and high latitudes (>35°) of the winter hemisphere due to the long path of the solar light have not yet been solved (Murakami, 2016), so it is not yet actively used in the marine ecosystem research field.

    Chlorophyll-a Concentration Retrieval Algorithms

    Empirical Algorithms

    Empirical algorithms for retrieval of chl-a concentration from remote sensing are based on statistical relationships between either ratios or difference of normalized water-leaving radiance or Rrs and the in-situ chl-a concentration. The empirical type algorithm use information in single or multiple bands and employs different functional forms such as the power function, multiple regression hyperbolic, second- or third-order polynomials, or most commonly the log-transformation (O’Reilly et al., 1998).

    Band-ratio Algorithms: The band-ratio algorithms apply the radiance ratio of blue and green wavelengths where the chl-a absorption and reflection, respectively, were recognized to correlate well with the surface chl-a concentration (Gordon and Morel, 1983). This algorithm derives chl-a concentration (CHL) values from the following equation:

    log 10 C H L = a 0 + i = 1 4 a i ( log 10 R rs ( λ blue ) R rs ( λ green ) )
    (1)

    where λblue and λgreen are the instrument-specific wavelengths closest to 443 and 555 nm, respectively, and the numerator, Rrs(λblue), is the greatest of several input Rrs values. The coefficients, a0-a4, are sensorspecific adjusted to represent the best fit between the band-ratio and in-situ chl-a (O’Reilly et al., 2000). Various band combinations and regression coefficients are developed for different water types with similar band-ratio forms (O’Reilly and Werdell, 2019).

    This type of algorithm is also known as the ocean color (OC)X algorithm, where “X” stands for the number of bands used in (1). The OBPG provides coefficients for each band combination of OCX algorithms derived from version 2 of the NASA biooptical marine algorithm data set (NOMAD) for the CZCS, SeaWiFS, MODIS, MERIS, and VIIRS sensor (Table 2) (Werdell and Bailey, 2005). To obtain advances in algorithms or sensor calibration knowledge, reprocessing of data products from each supported mission is required to improve product quality (https://oceancolor.gsfc.nasa.gov/reprocessing/). However, the band-ratio algorithms for the global ocean normally overestimate the values of marine-optical products in coastal regions (e.g., Darecki and Stramski 2004;Magnuson et al., 2004). In Case 2 waters, the ratio between the blue and green wavelength becomes less sensitive to changes in chl-a concentrations because of increasing concentrations of SPM and CDOM (e.g., Bowers et al., 1996).

    Band-difference Algorithms: The distinction of variation rate with wavelength of absorption and scattering by chl-a allows quantification of chl-a concentration through spectral differences. Spectral band difference algorithms usually use band ranges from the red-near infra-red or the blue-green spectral regions. For eutrophic waters, the traditional band ratio algorithms become less efficient, as the signal in the blue spectrum drops below the limits of detection because of high absorption by algae. Since Neville and Gower (1977) confirmed that the height of the peak observed at 685 nm by solar-induced chlorophyll fluorescence was strongly correlated with chl-a concentration, the fluorescence line height (FLH) was introduced as a superior estimate of high chl-a concentration (e.g., Falkowski and Kiefer, 1985;Gower et al., 2004;Hu et al., 2005).

    The FLH is a relative measure of water-leaving radiance (L) in the chlorophyll fluorescence emission band from a baseline of linear fit between the two bands bracketing the fluorescent band as follows:

    F L H = L F [ L R + λ R λ F λ R λ L ( L L L R ) ]
    (2)

    where the subscripts F, L, and R refer to the fluorescence band and its left and right bands, respectively, as a baseline. The FLH algorithms of MODIS and MERIS use three bands 667, 678, and 746 nm (Hu et al., 2005) and 665, 681, and 708.75 nm (Gower et al., 2004) as the left, fluorescent, and right bands. This product cannot be derived from other ocean color sensors without chlorophyll fluorescence emissions bands in the 670-690 nm range. In addition to the FLH, various band-difference based algorithms such as maximum chlorophyll index and color index are also proposed (Gower et al., 2005;Hu et al., 2012).

    Hybrid Algorithms: The NASA/OBPG adapted hybrid approaches, which merges the color index (CI) for low-chl-a waters (≤0.25 mg m−3, ~78% of the global ocean area) and band-ratio algorithm for higher-chl-a waters (>0.4 mg m−3), as the default global algorithm (Hu et al., 2012, 2019). The CI is defined as the difference between Rrs in the green wavelength and a reference formed linearly between Rrs in the blue and red wavelength, which is similar to the concept of the FLH except the bands shift to bluegreen- red. The CI is converted to chl-a concentration value in the logarithmic relation.

    Semi-Analytical Algorithms

    To address the complexity of Case 2 waters, the semi-analytical models relate Rrs to the relevant inherent optical properties (IOPs), namely absorption and backscattering coefficients, of sea water. The former can be due to phytoplankton, CDOM, and detritus, while the latter is mainly due to particles (Roesler and Perry, 1995;Sathyendranath and Platt, 1997;Carder et al., 1999;Maritorena et al., 2002). The Garvel, Siegel, and Maritorena (GSM) algorithm is based on the quadratic relationship between Rrs and the absorption and backscattering coefficients (Garver and Siegel, 1997;Maritorena et al., 2002). The Rrs values of SeaWiFS, combined with IOP parameters that vary depending on water optical characteristics, are used as the model input. The CARDER algorithm for MODIS derives chl-a concentrations and IOPs from Rrs. This algorithm employs a more complex approach, where pigment absorption components are separated from those due to degradation products and the chlorophyll-specific phytoplankton absorption coefficient (Carder et al., 1999, 2004).

    Algorithms Based on Neural Network

    NN based retrieval algorithms are usually designed to relate Rrs to a physical property. An artificial NN inversion procedure was developed as a MERIS Case 2 water algorithm (e.g., Schiller and Doerffer, 2005). This algorithm inverts the log of Rrs of MODIS bands and three angles (solar zenith, viewing zenith, azimuth difference) directly into the log of IOPs with high efficiency. The derived optical coefficients are used to compute the chl-a concentration. For the development of the NN algorithm, thousands to tens of thousands of simulated Rrs spectra are trained to cover a large range of Case 1 and Case 2 waters with different observation and solar angles. Many studies also have showed the efficiency of the NN approach for detecting HABs (e.g., Guallar et al., 2016;El-Habashi et al., 2017;Lee and Lee, 2018).

    Algorithms for Geostationary Satellites

    GOCI applies band-ratio based algorithm as a default chl-a retrieval algorithm such as OC2 with 490 and 555 nm or OC3 with 443, 490, and 555 nm band. However, because observation area of GOCI, seas around the Korean Peninsula, consists of the Case 1 water (East Sea), Case 2 water (Yellow Sea), and the mixed water of Case 1 and Case 2 (East China Sea), the optimized algorithm that performs well in the optically complex water is also needed (Moon et al., 2010;Kim et al., 2016;Yoon et al., 2019). The Yellow Sea Large Marine Ecosystem Ocean Color Project (YOC) algorithm, one of the local algorithms based on the Tassan’s approach, was obtained using in-situ data from the Yellow Sea and East China Sea, which is currently used another operational algorithm for GOCI (Siswanto et al., 2011). The Tassan’s formula was designed to compensate the effects of CDOM and SPM on the blue to green band-ratio by using another band-ratio of shorter wavelengths given as

    C H L = 10 c 0 + c 1 R + c 2 R 2 , R = log 10 R rs ( 443 ) R rs ( 555 ) [ R rs ( 412 ) R rs ( 490 ) ] c 3
    (3)

    where the coefficients c0-c4 are regionally optimized through an iterative fitting routine (Tassan, 1994;Siswanto et al., 2011).

    Accuracy Assessment and Validation

    A large data set containing coincident in-situ chl-a concentrations and Rrs measurements is needed to evaluate the accuracy, precision, and suitability of a wide variety of chl-a algorithms for ocean color measurements (O’Reilly et al., 1998). The NASA SeaWiFS Bio-optical Archive and Storage System (SeaBASS) provide a database of measurements collected by many research groups to develop and validate satellite ocean color algorithms. The NOMAD data set is a subset of SeaBASS, which is specifically compiled for bio-optical algorithm development as it contains coincident measurements of chl-a, Rrs, and other data collected simultaneously in the global oceans (Werdell and Bailey, 2005).

    The accuracy of chl-a products distributed by NASA/OBPG was evaluated by satellite-to-in-situ match-up through mean absolute error (MAE) and mean bias which are based on log10 (Seegers et al., 2018). The MAE and mean bias take the form of 10 i = 1 n | log 10 ( C H L i S a t t l i t e ) log 10 ( C H L i i n s i t u ) | n and 10 i = 1 n log 10 ( C H L i S a t t l i t e ) log 10 ( C H L i i n s i t u ) n , respectivley, where n represents the match-up point size. The performance of the satellite-observed chl-a product varied for each satellite. SeaWiFS reported the lowest biases, with values of 1.03 (3%). CZCS reported the only negative bias of 0.63 (37%). VIIRS had the lowest MAE, with values of 1.48 indicating variability of 48%, while CZCS had the highest MAE, with an indistinguishable value of 2.01 (201%). All data set used for each sensor included chl-a values from low to high concentration (https://seabass.gsfc.nasa.gov/) (Table 3). The performance of chl-a estimation from GOCI was evaluated using 130 match-ups from the seas around the Korean Peninsula during 2010 and 2014 through absolute percent difference (APD) as following:

    A P D = 1 n i n | C H L i s a t e l l i t e C H L i i n s i t u C H L i i n s i t u |
    (4)

    (Kim et al., 2016). The APD of chl-a retrieval algorithms for GOCI with the coefficients derived by Kim et al. (2016) were 35-50% depending on the SPM concentration and algorithm type. The results of algorithm assessment showed that Tassan’s approach performed better than OC3 for all SPM levels. Fig. 5

    Prospect and Concluding Remarks

    In addition to the plans such as the Sentinel-3 series of ESA and JPSS series of NASA, there is an important mission planned by NASA, starting in 2022, named the Plankton, Aerosol, Cloud, and Ocean Ecosystem (PACE) mission using the Ocean Color Instrument (OCI) as the next generation polarimetry measurement. For the geostationary mission, GOCI-II is planned to be launched in 2019 onboard the Geostationary Earth Orbit Korea Multi-Purpose Satellite, GEO-KOMPSAT-2B, with 250 m spatial resolution of 13 bands as the half-hourly basis observation in daylight (Coste et al., 2017) (Figs. 3-4).

    Despite the continued development of the advanced ocean color sensors, there are inevitable limitations to the ocean color mission such as lack of the spatial coverage of daily products due to cloud or inter-orbit gaps and temporal discontinuity between missions. To overcome the limitations, the International Ocean- Colour Coordinating Group (IOCCG) recommended the merging of ocean color data from multiple missions (IOCCG, 2007). Several attempts for merging products have been enhancing ocean color science as providing consistent and seamless high spatial resolution data of in space and time (e.g., Gregg and Conkright, 2001;Kwiatkowska and Fargion, 2003;Maritorena and Siegel, 2005;Pottier et al., 2006;Pitarch et al., 2016).

    Furthermore, coastal applications for monitoring HABs, coral reefs, and oil spill and fisheries, which have been increasingly important, require higher spatial and spectral resolution than most currently available ocean color sensors to resolve the complex optical signals of costal area. Hyperspectral measurements, which typically have 100 or more bands with bandwidths of a few nm, historically have usually been made from airborne sensors. However, recent developments in hyperspectral enable the observations of satellite-based remote sensing on the open and coastal ocean (e.g., Blondeau-Patissier et al., 2014). The GEOstationary Coastal and Air Pollution Events (GEO-CAPE) mission with hyperspectral bands of 10 nm interval, which NASA plans to launch after 2020, would also meet the needs on the coastal managements.

    In this review, we have summarized the ocean color missions and operational chl-a concentration retrieval algorithms that the scientific community has proposed to date. Over the past decades, with the help of the series of ocean color sensors from CZCS to GOCI, there has been progress in understanding the changes in the marine ecosystem and the impact of rapid climate change on the oceanic biosphere environment based on the imageries of chl-a concentrations in the oceans and coasts (e.g., Smetacek and Cloern, 2008;Martinez et al., 2009;Racault et al., 2012;Siegel et al., 2013). However, there are still many challenges to be solved in the ocean color remote sensing, such as the classification of phytoplankton type, more accurate atmospheric correction schemes, and improvements in data quality as well as technical issues.

    Acknowledgments

    This research was a part of the project entitled “Research for applications of Geostationary Ocean Color Imager (GOCI)” funded by the Ministry, Land, Transport and Maritime Affairs (MLTM), Korea.

    Figure

    JKESS-40-4-315_F1.gif

    Global distribution of the monthly mean of ocean surface chlorophyll-a concentration (mg m−3) from MODIS-Aqua in September 2017 (from NASA, https://svs.gsfc.nasa.gov/4596).

    JKESS-40-4-315_F2.gif

    Example of the orbital tracks of MODIS/Aqua in one day (June 12, 2019). All times are in UTC (from Space Science and Engineering Center, University of Wisconsin-Madison, https://www.ssec.wisc.edu/datacenter/aqua/).

    JKESS-40-4-315_F3.gif

    Time series of main ocean color satellite missions from the 1970s to the 2020s, where the light gray arrow bars represent upcoming missions as of 2019.

    JKESS-40-4-315_F4.gif

    Temporal evolution of ocean color satellites and sensors (photographs from NASA, ESA, and KIOST).

    JKESS-40-4-315_F5.gif

    (a) Coverage area with the arrangement of 16 slots and (b) an example of chlorophyll-a concentration image of GOCI (from KIOST).

    Table

    Specifications of the main ocean color satellite missions to date (abbreviations are explained in the text)

    Bands and coefficients of operational algorithms for retrieval of remote sensing chlorophyll-a concentration

    Performance of several chl-a products currently implemented by NASA using SEABASS data set

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