VERNON L. STARK

QUANTIFYING COLOR IN THE NORTH ATLANTIC AND NORTH PACIFIC

Phytoplankton, the microscopic plants that form the lowest level of the oceanic food chain, reveal their presence because of their chlorophyll and other pigments, which change the color of ocean waters. Quantitative observations of ocean color can be used to estimate the concentration of these pigments. The fIrst such observations of ocean color on a global scale were provided by the Coastal Zone Color Scanner, which was fIelded on the Nimbus-7 in 1978. At the Applied Physics Laboratory, data from the Coastal Zone Color Scanner have been used to quantify the spatial and temporal variability of ocean color (specifIcally water reflectance) in the North Atlantic and North PacifIc oceans at wavelengths ranging from 400 to 600 nm. In addition to that work, several other applications of ocean color data also demonstrate the wide variety of investigations that can benefIt from ocean color data.

INTRODUCTION The color of ocean water varies as a result of variations far outlived its design life and collected data from Octo­ in material suspended or dissolved in the water. In the ber 1978 through June 1986. open ocean, the primary constituents affecting ocean The czcs measured at 443, 520, 550, color are the , which are passively floating and 670 nm with a spatial resolution at nadir of 825 m. or weakly swimming minute plant life. Ocean waters Since light is scattered by the atmosphere, the upwelling with very low concentrations of phytoplankton appear light measured by the czcs contains atmospheric as well blue in . As the phytoplankton concentration in­ as oceanic contributions. To quantify ocean color, the creases, the water color shifts toward green. One can use atmospheric contribution- arising from Rayleigh and remote sensors to detect and quantify this color shift and aerosol - must be subtracted from the radiance to estimate the concentration of phytoplankton. measured by the czcs. Since most of the light at the Many factors affect the concentration of phytoplank­ sensor results from atmospheric scattering, I this so-called ton; they include light, nutrients, currents, temperature, atmospheric correction must be done accurately to obtain grazing by zooplankton (passively floating or weakly accurate estimates of the water-leaving radiance. After swimming animals), and vertical mixing of the water atmospheric correction of czcs data, the so-called bio­ column as a result of cooling and storms. Since those optical algorithms are applied to estimate the concentra­ influences vary spatially and temporally, the concentra­ tion of phytoplankton pigments from the water-leaving tion of phytoplankton also exhibits a great deal of spatial radiances at 443, 520, and 550 nm. 2 The algorithms are and temporal variability. Consequently, remote sensors empirical and provide a quantitative relationship between that can repeatedly survey large areas are needed to char­ ocean color and phytoplankton pigment concentration. acterize the variability of phytoplankton in the oceans. Water-leaving radiances from the czcs provide not only This paper discusses one of the primary sensors used an estimate of phytoplankton concentration but also an to collect ocean color data, the Coastal Zone Color Scan­ estimate of the ocean optical clarity. Optical clarity can ner (CZCS), and provides several applications of ocean be estimated since, in open ocean waters, spatial and color data. temporal variations in ocean optical properties are gov­ erned largely by spatial and temporal variations of the THE COASTAL ZONE COLOR SCANNER phytoplankton concentration. The specific optical clarity The vast majority of the ocean color data available parameter generally derived is the diffuse today were collected by the czcs, which was one of the coefficient at a wavelength of 490 nm, K(490). The inverse sensor systems on the Nimbus-7 spacecraft and the first of this coefficient is known as the optical depth, the dis­ satellite-based scanner specifically designed to observe tance that 490-nm light travels before being attenuated by the ocean at visible wavelengths. It had the sensitivity a factor of e (e "" 2.718). The clearest ocean waters have and wavelengths needed to observe ocean color changes an optical depth (at 490 nm) of about 40 m, and turbid associated with variations in phytoplankton concentra­ waters have optical depths of 5 m or less. Because of such tion. The czcs was a proof-of-concept instrument and attenuation, the czcs provides only near-surface estimates was designed to have a one-year lifetime. Fortunately, it of phytoplankton concentration and optical attenuation. In

224 f ohns Hopkins APL Technical Digest, Volume 14, Number 3 (1993) fact, 90% of the water-leaving radiance originates in the low-latitude areas, where the primary factor limiting upper attenuation length of the water column.3 growth is the availability of nutrients. Such an area is the The foregoing data-processing steps-that is, the atmo­ one near Bermuda, where in the summer the upper ocean spheric correction and application of the algorithms to is strongly stratified, with a median mixed-layer depth of derive estimates of phytoplankton concentration and about 20 m. During the summer, phytoplankton in these water clarity-and other processing steps not detailed here waters have an abundance of light, but the strongly strat­ were applied to the entire czcs data set by investigators ified waters inhibit the renewal of nutrients through mix­ from the Goddard Space Flight Center and the University ing. Consequently, nutrients in the near-surface waters of Miami.4 The resulting data set has proved valuable in become depleted and phytoplankton levels are quite low. a wide variety of civilian and military research investi­ During the fall, when mixing brings additional nutrients gations, as will be discussed in the next section. to the surface, phytoplankton levels in the near-surface waters increase. This type of seasonal phytoplankton APPLICATIONS OF OCEAN COLOR DATA growth cycle is illustrated by Figure 3, which shows The czcs data set provides the observations necessary results compiled from a seven-year time series of in situ to study a variety of phenomena. In this section, I will data taken about fifteen miles southeast of Bermuda. The discuss in some detail the application of czcs data to the time series was used to infer the minimum, median, and study of spatial and temporal variability in phytoplankton maximum pigment concentrations that would be observed 3 concentration and to the quantification of ocean color in by a remote sensor during each month of the year. The terms of water reflectance. The section will conclude with figure shows that phytoplankton levels in the near-surface a sampling of other uses of ocean color data. waters are consistently low from May through September when nutrients limit phytoplankton growth. During the Characterization of Phytoplankton Variability period of increased mixing (October through April), phy­ toplankton levels tend to be elevated and are quite variable The data set has provided the first global view of czcs compared with levels during the summer. oceanic phytoplankton and the complex spatial and tem­ Between the two extremes of nutrient- and light-lim­ poral variations in phytoplankton that result from vari­ ited phytoplankton popUlations are those where limita­ ability in factors that affect phytoplankton growth. A tions to growth vary seasonally. Moreover, spatial and particularly striking example of the temporal variability temporal variability can also result from a variety of other observed by the czcs is the spring bloom in the North 5 causes, such as grazing by zooplankton, currents, chang­ Atlantic Ocean. The bloom can be understood by con­ es in species composition, temperature variations, and sidering the situation in many high-latitude areas in the upwelling (in which subsurface waters rise, bringing up winter, when deep mixing of the near-surface waters additional nutrients). These temporal and spatial varia­ occurs (often extending to hundreds of meters) and tions in the influences on phytoplankton growth result in ambient light levels are relatively low. The seasonally low considerable temporal and spatial variability in phyto­ light levels, combined with deep mixing (which carries plankton standing stocks. Studies of such variability have the phytoplankton to depths where little light penetrates), been greatly aided by the data collected by the czcs. For results in available light levels that are insufficient to example, czcs data have been used for testing models that support significant phytoplankton production. In the characterize the spring bloom of phytoplankton,6 for spring, heating of the upper water column and a de­ characterizing phytoplankton blooms in the marginal ice creased intensity of storms allows the water column to zone,7 for characterizing eddies,8 and for studying up­ stabilize so that near-surface mixing penetrates to a depth welling events.9, IO of only a few tens of meters. The decrease in the mixed­ layer depth (which allows phytoplankton to remain in the Quantification of Ocean Color Variability near-surface waters where more light is available), along The czcs water-leaving radiance data at 443, 520, and with increased light levels and the abundance of nutrients 550 nm have been used in a study at APL to characterize brought to the surface by deep winter mixing, allows for the spatial and temporal variability of ocean color at rapid phytoplankton growth, the so-called spring bloom wavelengths ranging from 400 to 600 nm. 11 In this study, of phytoplankton. The spring bloom can be seen by ocean color is characterized in terms of the water reflec­ comparing Figures 1 and 2, which show median phy­ tance, R, defined as 7r times the in-water ratio of the toplankton concentrations for March and Mayas derived 4 upwelling radiance to downwelling irradiance. Water from a subset of the global czcs database. Those figures reflectance is estimated using a model developed by indicate that phytoplankton concentrations in the north­ Howard Gordon of the University of Miarni.12 Using the ern North Atlantic Ocean increase significantly during results of Gordon's Monte Carlo simulations of light the spring as the amount of light the phytoplankton have propagation and making various approximations, one can available increases. For example, in the area south of derive the following approximate relationship: 11 ,12 Iceland, pigment concentrations during March are on the 3 order of 0.15 mg/m . By May, the pigment concentration 3 R(A) = 0.346bb (A) in that region has increased to 0.40 mg/m or greater, and K(A) , 3 many areas show concentrations in excess of 0.65 mg/m . This type of cycle, where phytoplankton growth is where R(A) is the reflectance at wavelength A, bb(A) is governed largely by seasonally changing light levels and the backscattering coefficient, and K(A) is the diffuse mixed-layer depths, contrasts with the situation in many attenuation coefficient. The value of K(A) is estimated

Johns Hopkins APL Technical Digest, Volume 14, Number 3 (1993) 225 V. L. Stark

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Pigment K (490)

60

0.656 0.100

50 0.405 0.070 OJ Q) ~ Q) "0 0.257 0.050 .a 40 § -£ 0 z 0.148 0.035

30 0.065 0.025

0.000 0.021 20

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West longitude (deg) Figure 1. Med ian pigment concentration (mg/m3) and diffuse atten uati on coefficient K(490) (i n m- ') fo r March. Black denotes no data and white indicates land or ice .

using the ratio of water-leaving radiances at 443 and 550 between those characterized by curves 1 and 2. Curve 4 nm and the Smith-Baker algorithm, which relates K('A ) is typical of very turbid waters where near-surface phy­ and pigment concentration. 13 Backscattering is estimated toplankton are abundant, and curve 5 is representative of from the magnitudes of the water-leaving radiances at very clear waters with particularly high reflectance. Fig­ 520 and 550 nm. Since the water-leaving radiances at 520 ures 5 and 6 show where waters with reflectances that and 550 nm depend only weakly on pigment concentra­ match the curves in Figure 4 can be found. (In this article, tion, variations in the magnitude of the water-leaving two reflectance curves are considered matching if at all radiances at tho e wavelengths result primarily from vari­ wavelengths from 400 to 600 nm the difference in reflec­ ations in back cattering rather than variations in pigment tance between the curves is less than or equal to one concentration. Backscattering is an important parameter percent. February data used are from 1979, 1980, and to estimate, since the magnitude of backscatter can vary 1981; August data are from 1979 and 1980.) As Figures ignificantly even for a fixed pigment concentration. 14 5 and 6 indicate, curve 1 of Figure 4 matches large areas Sample reflectance results derived by applying Gor­ in the southern portion of the region where clear waters don's model to czcs data are shown in Figures 4 through occur. Curve 2 matches fairly large regions in the north­ 6. Figure 4 hows reflectance curves typical for waters ern portion of the area where moderately turbid waters of the North Atlantic and North Pacific oceans. Curve 1 are found. Curve 3 falls between curves 1 and 2 and is a reflectance curve typical of clear-water areas where matches areas where water properties change from those near-surface phytoplankton concentrations are low. characteristic of curve 1 to those characteristic of curve Curve 2 is characteristic of moderately turbid waters, and 2. Curve 4 matches northern and near-coastal areas where curve 3 is characteristic of waters that are transitional very turbid waters are encountered. Curve 5 matches

226 f ohns Hopkins APL Technical Digest, Volume 14, Number 3 (1993) Quantifying Ocean Color

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Pigment K(490) 60

0.656 0.100 50

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10 80 60 40 20 o

West longitude (deg)

Figure 2. Median pigment concentration (mg/m3) and diffuse attenuation coefficient K(490) (in m- 1) for May. Black denotes no data and white indicates land or ice.

8

1.0 6 ;;g ~ M OJ () E 0.8 c 0, co 4 t3 E. OJ c 'a5 ,g 0.6 a: ~ C 2 OJ () § 0.4 () C OJ §,0.2 450 500 550 600 a:: Wavelength (nm)

O L-~ __-L __ -L __ ~ __L-~ __~ __-L __ ~ __~~ Figure 4. Reflectance curves characteristic of North Atlantic and Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec North Pacific waters. Curve 1: Clear water with low phytoplankton concentrations. Curve 2: Moderately turbid water. Curve 3: Water Figure 3. Minimum, median, and maximum pigment concentra­ transitional between waters characterized by curves 1 and 2. tions observed at station S near Bermuda (located at 32°10'N, Curve 4: Very turbid water with abundant phytoplankton. Curve 5: 64° 30'W) for each month of the year. Very clear water with high reflectance.

Johns Hophns APL Technical Digest, Volume 14, Number 3 (1993) 227 V. L. Stark

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60

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20 5

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~ig. ure 5. Map for February showing areas with reflectance that matches (to within ±1 %) the reflectance curves shown in Figure 4. Black Indicates no czcs data and white denotes land or ice. Colors indicate the reflectance curve in Figure 4 that matches the water reflectance for that location in February. Gray areas are not matched by any of the curves in Figure 4.

areas where clear waters with relatively high reflectance Other Applications of CZCS Data occur. Area not matched by any of the curves in Figure 4 are gray in Figures 5 and 6; reflectance curves matched The applications I have noted comprise but a small by waters in the gray areas occur infrequently and for sample of the many uses of czcs data. We will turn now simplicity are not shown (10.4% of waters in the area briefly to other civilian and military uses of czcs data. shown are not matched by the five curves in Fig. 4). Since oceanic phytoplankton utilize significant Figures 5 and 6 illustrate the significant changes in amounts of carbon dioxide (C02), one major use of ocean phytoplankton concentrations and the consequent chang­ color data is in assessing the role phytoplankton play in es in ocean color that can occur from one season to the global . IS Ocean color data provide a great another. For example, clear waters (characterized by re­ deal of information about phytoplankton concentration, flectance curves 1 and 5) extend much farther north in which in turn can be used to estimate CO2 consumption. August than in February, largely as a result of near­ But since 90% of the water-leaving radiance originates in surface nutrient depletion that occurs during the summer the upper attenuation length (at most about 40 m) , ocean when the trong stratification inhibits vertical mixing and color data provide only near-surface estimates of phy­ 3 nutrient replenishment. With the near-surface nutrient toplankton concentration. ,J6 Algorithms relating this supply depleted, phytoplankton levels fall to low values near-surface biomass measurement to phytoplankton and water are quite clear and have a relatively high growth throughout the water column are used to assess the reflectance. quantity of CO2 consumed by phytoplankton.

228 Johns Hopkins APL Technical Digest, Volume 14, Number 3 (1993) Quantifying Ocean Color

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60

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Q) :s> 40 () OJ 4 Q) ~ Q) "0 2 ~ 5 :; 0 Z

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Figure 6 Map for August showing areas with reflectance that matches (to within ±1 %) the reflectance curves shown in Figure 4. Black indicates no czcs data and white denotes land or ice. Colors indicate the reflectance curve in Figure 4 that matches the water reflectance for that location in August. Gray areas are not matched by any of the curves in Figure 4.

Another use of ocean color data is in locating fronts the dynamics of ocean currents and in biological ocean­ and tracking water masses such as river plumes and ography. One of the most practical uses of the ability to eddies. In this regard, ocean color sensors can augment locate ocean color fronts may be in the fishing industry; the capability provided by satellite infrared sensors, albacore tuna have been found to aggregate at ocean color which provide the means to distinguish water masses on fronts off the west coast of the U.S. IS On the military side, the basis of temperature differences. Ocean color sensors fronts and eddies are significant for both acoustic and can be useful in situations where surface heating has nonacoustic antisubmarine warfare. The large changes in eliminated the near-surface temperature contrast between sound velocity typically associated with water mass water masses but a color contrast still exists. Ocean color boundaries makes them important in underwater sound sensors also provide important ocean front observations propagation. In addition, ocean optical clarity can change in situations where atmospheric moisture is high. 17 When markedly in the vicinity of a front or eddy, resulting in atmospheric moisture is high, the upwelling visible radi­ substantial changes in the performance of optical detec­ ation can penetrate more readily than the infrared radi­ tion systems. ation, since absorption by water vapor is lower in the Finally, other military applications of ocean color data visible than in the infrared. can be found in mine warfare, submarine laser commu­ The ability to track oceanic fronts and eddies is useful nications, and optical mapping of bathymetry in shallow for both civilian and military purposes. On the civilian areas. 17 In those applications, ocean color data can be side, tracking of fronts and eddies can aid in studies of used to estimate ocean optical properties and system

Johns Hopkins APL Technical Digest, Volume 14, Number 3 (1993) 229 V. L. Stark performance. For example, estimates of optical clarity 6Wroblewski, 1. S. , Sarmiento, 1. L. , and Flierl, G. R. , "An Ocean Basin Scale can be used to judge the approximate depth to which Model of Plankton Dynamics in the North Atlantic: I. Solutions for the Climatological Oceanographic Conditions in May," Global Biogeochem. communications to a submarine are reliable. Also, in the Cycles 2, 199-218 ( 1988). use of airborne lasers to map bathymetry, ocean optical 7 Sullivan, C. W., McClain, C. R. , Comiso, 1. c., and Smith, W.O., Jr., data can provide information about the spatial and tem­ "Phytoplankton Standing Crop Within an Antarctic Ice Edge Assessed by Satellite Remote Sensing," J. Geophys. Res. 93, 12,487-12,498 (1988). poral variability of ocean optical properties. Such infor­ 8Gordon, H. R., Clark D. K., Brown, 1. W., Brown, O. B., and Evans, R. H., mation can be used for survey planning to improve the "Sateltite Measurement of the PhytoplanJ...'ton Pigment Concentration in the Surface Waters of a Warm Core Ring," J. Marine Res. 40, 491 - efficiency and areal coverage of laser bathymetric sur­ 502 ( 1982). veys. For example, in many areas water clarity varies 9 Abbott, M. R. , and Zion, P. M., "Satellite Observations of Phytoplankton significantly with time. Ocean color data can be used to Variability During an Upwelling Event," Cont. Shelf Res. 4, 661-680 (1985). IOM cClain, C. R. , Pietrafesa, L. 1. , and Yoder, 1. A. , "Observations of Gulf determine when waters are clearest and therefore provide Stream-Induced and Wind-Driven Upwelling in the Georgia Bight Using the best conditions for an optical bathymetric survey. Ocean Color and Infrared Imagery," J. Geophys. Res. 89, 3705-3723 (1984). II Stark, V. L. , with contributions from Gordon, H. R., "Variability of Reflectance as Inferred from CZCS Data," JHU/APL Report STD-R-2029 SUMMARY (1991). The CZCS demonstrated that ocean color data are valu­ 12 Gordon, H. R., Brown, O. B. , Evans, R. H., Brown, 1. W., Smith, R. C. , et aI. , "A Semianalytic Radiance Model of Ocean Color," J. Geophys. Res. 93, able for an exceptionally wide variety of applications, 10,909-J 0,924 (1988). including location of areas likely to contain commercially 13 Baker, K. S. , and Smith, R. C., "Bio-optical Clas ification and Model of valuable fish, assessment of the role of oceanic phy­ atural Waters. 2," Limnol. and Oceanogr. 27, 500-509 (1982). 14Gordon, H. R., and Morel, A. Y., Remote Assessment of Ocean Color for toplankton in the global carbon cycle, studies of phy­ Il1I erpretation of Satellite Visible Imagery: A Review, Springer-Verlag, ew toplankton spatial and temporal variability, studies of the York ( 1983). 15Gower, J. F. R., "Potential Future Applications of Ocean Colour Sensing for dynamics of fronts and eddies, and assessment of the Large Scale and Global Studies," Adv. Space Res. 9, (7)39-(7)43 (1989). performance of antisubmarine warfare sensor systems. 16Balch, W., Evans, R. , Brown, J., Feldman, G. , McClain, C., et al., "The Because many of these applications require real-time Remote Sensing of Ocean Primary Productivity: Use of New Data Compila­ tion to Test Satellite Algorithm ," 1. Geophys. Res. 97, 2279-2293 (1992). data, the failure of the czcs in 1986 represented a serious 17 Hickman, G. D. (ed.), Applications of Ocean Color to Naval Waifare, Naval limitation to the application of ocean color data. Fortu­ Oceanographic and Atmospheric Re earch Laboratory Report OARL-BC- nately, the collection of remotely sensed ocean color data 009-91-321 ( 1992). 18Laurs, R. M., Fiedler, P. c., and Montgomery, D. R. , "Albacore Tuna Catch will resume in early 1994 with the fielding of the Sea­ Distributions Relative to Environmental Features Observed from ," viewing Wide-Field-of-view Sensor (SeaWiFS). The Deep-Sea Res. 31, 1085-1099 (1984). 19 McClain, C. R. , Esaias, W. E. , Barnes, W., Guenther, B., Endres, D., et. al., SeaWiFS will measure the ocean color at more visible SeaWiFS Calibration and Validation Plan, Vol. 3, NASA Technical Memo­ bands and with a higher signal-to-noise ratio than did the randum 104566 (1992). CZCS. 19 Moreover, improved atmospheric correction algo­ rithms, sensor calibration methods, bio-optical algo­ rithms (for relating ocean color to water contents includ­ ing phytoplankton pigments), and a 100% daylight duty cycle (the czcs only took selected imagery and had a duty cycle of less than 10%) will provide a higher-quality data set with better spatial and temporal coverage. SeaWiFS data will provide the information needed to accelerate the progres of research in the exceptionally wide variety of THE AUTHOR areas in which ocean color data can be applied.

REFERENCES VERNO L. STARK received a I Gordon, H. R., and Castano, D. J., "Coastal Zone Color Scanner Atmospheric B. S. in physic from Colorado Correction Algorithm: MUltiple Scattering Effects," Appl. Opt. 26, 211 1-2 122 State University in 1977 and an 2(1987). M. S. in applied phy ics from The Gordon, H. R., Clark, D. K., Brown, J. W., Brown, O. B., Evans, R. H., et Johns Hopkin University in al ., "Phytoplankton Pi gment Concentrations in the Middle Atlantic Bight: 1980. Mr. Stark joined APL in Compari on of Ship Determinations and CZCS Estimates," Appl. Opt. 22, 20- 1977 and ha worked in a variety 36 ( 1983). 3Gordon, H. R., and Clark, D. K. , "Remote Sensing Optical Properties of a of areas including optical ocean­ Stratified Ocean: An Improved Interpretation," Appl. Opt. 19, 3428-3430 ography, physical oceanography, (1980). data analysis, and tactical simula­ 4Feldman. G. c., Kuring, ., Ng, c., Esaias, W. E., McClai n, c., et al., "Ocean tion. Hi s interests include optical Color-Availability of the Global Data Set," £OS, Trans. Am. Geophys. Union oceanography biological ocean­ 70, 634, 635, 640-64 1 (1989). ography and remote ensing. 5E aias, W. E. , Feldman, G. c., McClain. C. R. , and Elrod, 1. A. , "Monthly Satellite-Derived Phytoplankton Pigment Di tribution for the orth Atlantic Ocean Basi n," £OS, Trans. Am. Geophys. Union 67, front cover and 835-837 (1986).

230 Johns Hopkins APL Technical Digest, Volume 14, Number 3 (1993)