Full report

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11I__ NASA Technical Memorandum SeaWiFS Technical Report 104566, Vol. 29 Series Stanford B. Hooker and Elaine R. Firestone, Editors Volume 29, The SeaWiFS Pigment Algorithm CZCS-Type James Aiken, Gerald E Moore, Charles C. Trees, Stanford B. Hooker, and Dennis K. Clark June 1995

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NASA Technical Memorandum 104566, Vol. 29 SeaWiFS Technical Report Series Stanford B. Hooker, Editor Goddard Space Flight Center Greenbelt, Maryland Elaine R. Firestone, Technical Editor General Sciences Corporation Laurel, Maryland Volume 29, The SeaWiFS Pigment Algorithm James Aiken and Gerald F. Moore Plymouth Marine Laboratory Plymouth, United Kingdom Charles C. Trees San Diego State University San Diego, California National Aeronautics and Space Administration Goddard Space Flight Center Greenbelt, Maryland 20771 1995 CZCS-Type Stanford B. Hooker NASA Goddard Space Flight Center Greenbelt, Maryland Dennis K. Clark NOAA/National Environmental Satellite Data Information Service Camp Springs, Maryland

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This publication is available from the NASA Center for AeroSpace Information, 800 Elkridge Landing Road, Linthicum Heights, MD 21090-2934, (301) 621-0390.

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J. Aiken, G.F. Moore, C.C. Trees, S.B. Hooker, and D.K. Clark ABSTRACT The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission will provide operational ocean color that will be superior to the previous Coastal Zone Color Sensor (CZCS) proof-of-concept mission. An algorithm is needed that exploits the full functionality of SeaWiFS whilst remaining compatible in concept with algorithms used for the CZCS. This document describes the theoretical rationale of radiance band-ratio methods for determining chlorophyll a and other important biogeochemical parameters, and their implementation for the SeaWIFS mission. Pigment interrelationships are examined to explain the success of the CZCS algorithms. In the context where chlorophyll a absorbs only weakly at 520 nm, the success of the 520 nm to 550 nm CZCS band ratio needs to be explained. This is explained by showing that in pigment data from a range of oceanic provinces chlorophyll a (absorbing at less than 490 nm), carotenoids (absorbing at greater than 460 nm), and total pigment are highly correlated. Correlations within pigment groups particularly photoprotectant and photosynthetic carotenoids are less robust. The sources of variability in optical data are examined using the NIMBUS Experiment Team (NET) bio-optical data set and bio-optical model. In both the model and NET data, the majority of the variance in the optical data is attributed to variability in pigment (chlorophyll a), and total particulates, with less than 5% of the variability resulting from pigment assemblage. The relationships between band ratios and chlorophyll is examined analytically, and a new formulation based on a dual hyperbolic model is suggested which gives a better calibration curve than the conventional log-log linear regression fit. The new calibration curve shows the 490:555 ratio is the best single-band ratio and is the recommended CZCS-type pigment algorithm. Using both the model and NET data, a number of multiband algorithms are developed; the best of which is an algorithm based on the 443:555 and 490:555 ratios. From model data, the form of potential algorithms for other products, such as total particulates and dissolved organic matter 1. INTRODUCTION As a second-generation ocean color instrument, the Seaviewing Wide Field-of-view Sensor (SeaWiFS) offers a variety of design improvements over its predecessor the Coastal Zone Color Scanner (CZCS). The design of the SeaWiFS instrument was driven by science requirements as defined by the SeaWiFS Prelaunch Science Working Group (SP- SWG). The SPSWG was an ad hoc committee selected by the National Aeronautics and Space Administration (NASA) Headquarters for the purpose of providing NASA with guidance in the formulation of mission objectives, specifications, and goals. The SPSWG has specifically expressed a requirement for continuity between CZCS and SeaWiFS products. Consequently, the SeaWiFS Project Office (SPO) plans to produce three groups of level-2 derived products: Sea- WiFS baseline, CZCS-type, and potential SeaWiFS products. A differentiation is made between CZCS-type pigment and SeaWiFS baseline chlorophyll-like pigment concentrations. The SeaWiFS semianalytical algorithm for chlorophyll a will be developed using analytical and semianalytical models. Chlorophyll a is the parameter that has been chosen as it is regarded universally as the most appropriate measure of viable phytoplankton biomass (i.e., (DOM), are suggested. uct derived from CZCS imagery, termed the CZCS Pigment Algorithm product. There is no predetermined consensus for the rationale or definition of this product (chlorophyll a, photosynthetic pigments, or total pigments). A choice must be made at the outset, so a methodological approach can be determined and described. The proposed approach should be essentially empirical, and use band ratios in common with the original CZCS algorithms. There is a strong desire in the community, for example, the Bio- Optical Algorithm Working Group (BOAWG), that there should be a SeaWiFS product compatible with the CZCS imagery, so retrospective processing can be applied and comparability between CZCS and SeaWiFS data can be achieved. To derive global bio-mass and productivity trends on decadal time scales, it is possible the standard NASA CZCS two-band algorithm, used for the global processing, could be used unaltered. It may be that an algorithm using all three bands at 443, 520, and 550nm, however, would give a more statistically robust relationship; these bands are close to the SeaWiFS 443, 510, and 555 nm bands and continuity would seem likely in this case. This hypothesis is flawed logically in a number of respects, primarily because of the differences between the CZCS and SeaWiFS instruments (Hooker et al. 1993). Seaphytoplankton which are growing actively and capable of WiFS will have precision radiometry (10 bit resolution), growth). The CZCS-type pigment product is supposed to provide some form of continuity with the totM pigment prodprecision calibration (prelaunch and onboard), stability of calibration monitoring, and established vicarious calibration schemes--CZCS had nothing comparable. SeaWiFS

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TheSeaWiFSCZCS-TypePigmentAlgorithm nm)which contribute to over 95% of the total pigment biomass. Most will havetwoinfrared(IR)bands(765and865 whereasof these have an important influence on water color at the will allowfor precisionatmosphericcorrection, waslimitedandoftenfailed wavelengths of the SeaWiFS bands. An accurate assess- CZCSatmosphericcorrection atmosphericment would involve the sum of the optically weighted con- (manyoftheassumptionsnecessaryforCZCS correctionwereinvalid,e.g.,oftenthe 670nmbandwas tribution of the main pigments present for different natural not truly a zerowater-leavingradianceband);SeaWiFSphytoplankton assemblages in different geographical sites correctedand seasons. will haveat leastfiveprecisionatmospherically poten- A comment on pigment measurement techniques is apbands(whereasCZCShadthree)offeringagreater tial for multibandalgorithmsofwidespreadapplicability.propriate here. Much of the insight into the composition In fact,the onlystrictly comparablefeaturebetweenthe and significance of differing phytoplankton pigments has CZCS and SeaWiFS instruments is the common blue band at 443 nm. The radical difference between the atmospheric correction schemes may mean that comparability will be limited. It is likely the best SeaWiFS band-ratio algorithms will use three, four, or five visible bands not compatible with CZCS: three bands using 412, 443, and 555 nm or 443, 490, and 555nm; four bands using 412, 443, 490 (or 510), and 555 nm; or five bands using 412, 443,490, 510, and 555 nm. The BOAWG team has agreed that besides the CZCS-type pigment algorithm, there should be continued research to identify the best possible SeaWiFS pigment algorithm. The objective of this study is to provide a band-ratio algorithm that has the best possible continuity with CZCS measurements. The focus of attention, however, is on the derivation of the best possible band-ratio algorithm for the retrieval of phytoplankton pigments from SeaWiFS obsercome as a result of the development of analyses using the high performance liquid chromatography (HPLC) technique (Mantoura and Llewellyn 1983 and Trees et al. 1985), which is the recommended methodology in the Ocean Optics Protocols for Sea WiFS Validation (Mueller and Austin 1995). Along with the insight on pigments for some has come confusion for others, with earlier reports that concentrations of pigments determined by HPLC and fluorescence (Trees et al. 1985) differed markedly--much lower pigment concentrations were obtained using the HPLC technique. A thorough investigation by Trees et. al (1995) has shown that when each method is applied rigorously, each yields about one-to-one (:t:10%) relationships for chlorophyll a in most bio-optical provinces. Errors can arise, however, if the protocols for sampling, filtration, extraction, and calibration are not adhered to strictly; exceptions occur vations. The desire is to achieve these goals based on a if either chlorophyll b or chlorophyll c are atypically high sound theoretical basis and rationale. 2. PIGMENTS REVISITED The product from the CZCS pigment algorithm was chlorophyll a plus phaeopigment concentration, Ca + Cp (as determined by the fluorometric method), which were considered the main absorbing agents of biogenic origin by the NIMBUS Experiment Team (NET). The reasons for the choice of the Ca + Cp parameter was partly historical and partly methodological. Prior to 1980, the principle methods for the determination of chlorophyll a were the tri-chrometric spectrometric method (Strickland and Parsons 1972) and the fluorescence method (Yentsch and Menzel 1963). Both methods could be imprecise (e.g., at low concentrations) and frequently under- and over-estimated both Ca and Cp due to the presence of other interfering pigments, notably chlorophyll b and chlorophyll c. It is now accepted by most biological oceanographers that phaeopigments rarely exceed 3-8% of the total pigment concentration in the surface layer of the ocean, with the exception of a few well understood circumstances, for example, when zooplankton grazing is high and localized. Furthermore, there are a number of other photosynthetic and photoprotectant pigments which co-exist, co-vary, and absorb at the same wavelengths as chlorophyll a, and which occur in significant concentrations. Individually, they may account for approximately 5-50% of the total pigment concentration and, in combination with chlorophyll a, may (greater than about 5% of the total). Recently, there have been several studies investigating the contribution of the major phytoplankton pigments to light absorption in the oceans, including the independent analyses by Bidigare et al. (1990) as well as Hoepffner and Sathyendranath (1993). Although the latter study was restricted to samples from the Georges Bank area, both studies showed the major pigments that need to be included to account for 95% of the light absorbed are relatively few in number: 1) Chlorophylls a, b, and c; 2) The photosynthetic carotenoids (PSC); and 3) The photoprotectant carotenoids (PPC). In the yellow-orange part of the spectrum (at around 550nm), the phycobilin, phycoerythrin, and phycocyanin pigments are moderately important light absorbers. These pigments occur mostly in cyanobacteria, which are generally unimportant in the surface layers of the ocean relevant to this study; the only known instances of significance correspond to blooms of cyanobacteria occurring in upwelling regions. Other, taxa-specific, tag-pigments are also insignificant to the total light absorption in the ocean--individually or collectively they account for less than 5% of the absorption. Both Bidigare et al. (1990) and Hoepffner and Sathyendranath (1993) give tables of the specific absorption coefficients of these major pigment groups. Although there is general agreement between these

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J. Aiken,G.F.Moore,C.C.Trees,S.B.Hooker,andD.K.Clark Table 1. Totalpigmentconcentration(CTp) to chlorophyll a (Ca) relationships. CTP R 2 0.021 + 2.17Ca 95.5 -0.040 + 2.30Ca 95.9 0.031 + 2.16Ca 93.2 Table 2. Global total pigment regressions}. N Comments 670 All data. 630 Ca _ 3mgm -3 416 z < 10m Region Ca R 2 C b R 2 Cc R 2 Cabc R 2 Cpp R 2 Cps R 2 Antarctic 0.544 97.3 0.154 79.6 0.295 98.8 0.043 45.4 0.252 96.2 O.0O6 35.7 NEAT 89-9O 0.422 99.6 0.134 99.5 0.427 99.3 0.049 83.5 0.377 99.5 0.016 76.6 NEAT 91_ 0.519 98.6 0.020 86.3 0.436 97.4 0.122 80.5 0.313 92.5 0.024 27.1 GIN Seas 0.367 96.6 0.103 88.8 0.443 95.0 0.096 68.8 0.346 94.9 0.085 74.6 Georges Bank 0.051 98.2 0.358 98.5 0.104 96.9 0.253 97.7 0.525 98.8 0.066 84.4 0.036 89.7 0.437 99.4 0.231 98.7 0.207 98.8 Bermuda (BATS) 0.499 99.6 0.026 52.6 0.446 99.7 0.074 93.6 0.042 91.4 0.436 99.2 0.249 94.5 0.186 97.1 EqPac Global 0.475 0.042 0.077 0.407 0.128 0.276 t Regression assumes no intercept. two different studies, there is some divergence concerning the coefficients in the blue spectral region. Figure la, from Bidigare et al. (1990), shows the weightspecific absorption coefficient for the major pigment groups, and Fig. lb shows the equivalent figure from Hoepffner and Sathyendranath (1993). Figure 2 shows the actual pigment absorption for the major pigments and the summed total for measurements from a cruise to the Northeast Atlantic (NEAT) in June 1991 (Holligan et al. 1993). For the latter, the phytoplankton assemblage was dominated by coccolithophores and small flagellates, yet these data are similar to the earlier studies with the total pigment absorption dominated by chlorophyll a, PSC, and PPC--chlorophyll b and chlorophyll c have less than 5% significance. Bidigare et al. (1990) concluded accessory pigments do not always co-vary with chlorophyll a over depth and time. In this study, the relationships of accessory pigments to chlorophyll a for the surface layers only, sensed by satellite color imagers, are examined using data from a wide variety of sources (published and unpublished). An examination of the relationship between chlorophyll a and total pigments (sum of chlorophylls a, b, c, PSC, and PPC) shows a robust relationship (97% of the variance explained). Nearly 5,600 pigment determinations from many bio-optical provinces were used in the analysis (see Fig. 3). Province by province and cruise by cruise, the ratio of total pigment to chlorophyll a varied from 1.876-2.876 with a mean of 2.164. The conclusion from this analysis could be that it matters little whether the algorithm product is chlorophyll a or total pigments, since the relationship between these two measures of marine phytoplankton biomass, on a global level, are so tightly coupled. Two issues make this hypothesis invalid: 1. The optical influence of the different pigment groups (e.g., the chlorophylls and carotenoids) are quite dif- :_NEAT 1991 is coccolithophore bloom data. ferent in the five SeaWiFS blue and green bands (412, 443, 490, 510, and 555 nm). 2. The relative composition of carotenoids (e.g., PSC) in different biogeographical (bio-optical) provinces is not constant and varies significantly from cruise to cruise and province to province. This is demonstrated here by a thorough analysis of all pigment interrelationships for seven widely differing biogeochemical (bio-optical) provinces: • Greenland, Iceland, and Norwegian (GIN) Seas 1986-87 (Trees), • Georges Bank (Hoepffner and Sathyendranath 1992), • NEAT 1989-90 (United Kingdom) Biogeochemical Ocean Flux Study (BOFS), • NEAT 1991 BOFS (Trees), • Equatorial Pacific (EqPac) 1992 (Spring and Fall), • Antarctica 1992 BOFS (Sterna), and • Bermuda Atlantic Time-Series Station (BATS). Note that the total pigment to chlorophyll a relationships for all combined data are highly correlated as shown in Table 1, with the major pigment relationships shown in Fig. 4. Examining the data province by province, it is evident there are widely varying ratios of the concentrations of chlorophylls a, b, c, PPC, and PSC (Ca, Cb, Cc, Cpp, and Cps, respectively) to total pigment concentration (CTp) as shown in Table 2 and Fig. 5a-g. This analysis shows that for chlorophyll a, chlorophyll c, total carotenoids, and PSC, the intraprovince covariance is extremely tight for all provinces (R 2 is 96-99% for chlorophyll a to CTp), although the coefficients of variance differ: notably, the fraction of chlorophyll a is lowest in the

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TheSeaWiFSCZCS-TypePigmentAlgorithm 0.1 0.1 E E 0.08- C O lu 0.06 - O ! / 0.04- °°-- ,°° O om ,m O o. I " "° 0.02 e_ {D 0 Key --ca cb c_ ....... Cps -- Cpp 400 450 500 550 600 650 700 750 Wavelength [nm] : 0.1 2 E 0.08- 0.06o o |m im 0 .," ".%'" *" [ %% 0.02 Q. Key Ca Cb ....... Cps + Cpp \ A 400 450 500 550 600 650 700 750 Wavelength [nm] Fig. 1. Pigment-specific absorption versus wavelength from a) Bidigare et al. (1990) and b) Hoepffner and Sathyendranath (1993), top and bottom plots, respectively. 4

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J. Aiken, G.F. Moore, C.C. Trees, S.B. Hooker, and D.K. Clark 0.01 m"o :, O.O4- _= : e_eeaoo ." " 0.03 °, _, % .8 0.02 "....'-...... " L::' 400 450 500 Wavelength [nm] Key _ C a Cps cb -- cpp Cc .... CTp "'" \ 550 600 650 700 750 Fig. 2. Simulated pigment absorption. 12- "" _ 6. i,- O. -I- ++ + + + Z + +,1,+ + +* :t-,-++;++"*++ - ++.#- -t +.,, _4 0 1 2 3 4 5 Ca [mg m"3] Fig. 3. Chlorophyll a versus total pigments.

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The SeaWiFS CZCS-Type 10.00 I a) , 1,oo ........................._ ................. 010 0.01 10.00 0,01 0.10 1.00 -3 CHIom_yl • mO.m 0.I0 | I 0.01 1000 001 0.10 lO0 -3 Oarotenolda mg_ e) 10.00 ! i ,. i * • I.oo ............................................" .......................... 0.10 ,, • _ P._ ! ! I 0.01 1000 0.01 0.10 1.00 -3 Photosynthot_G_otonolds mg/n Pigment Algorithm b) lO00I _ 1.oo ...................... 0.10 ....................................................... • i i oo,_ i i ' 0.I0 1.00 1000 0,01 -3 C_I • mg.m d) 10.00 ! i . ": " .......................I-:'-: " E 1.00 "! i 0.10 0 ,% *° • i f-' i i 0.01 10.00 OD1 0.10 1.00 -3 TotaJ Chlorophyl mg,m f) 10.00 , 1.oo .'.,.."'__.,_ i • ..iH_t....i_i• 0.10 ..e .............. • -:"............. 0.01 0.10 1.00 10.00 0.01 -3 Photoprot_ctant Carotenolda mg.m Fig. 4. Pigment regressions: a) CTP = 0.13+1.96C (R2--93.2%), b) C_bc = 0.02+1.20Ca (R2=97.6%), c) CTp = 0.13 + 2.27[Cpp + Cesj (R2=93.7%) , d) Cpp + Ces = 0.12 + 0.76C_b¢ (R--77.5%), e) CpP + Cps --- 0.03 + 1.15Ces (R =97.5%), and f) Cpp + Cps = 0.41 + 3.67Cpp (R2--56.2%) •

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0.75 0.60 0.45 0.30 I m ANT NEAT N91 GIN GB BATS EP ANT NEAT N91 GIN GB BATS EP ANT NEAT N91 GIN GB BATS EP Biogeochemical Province Biogeochemical Province Biogeochemical Province > 0.25 d 0.25- 0.20 - ii _ 0.15 _ 0.15- 0.05 - llil 010 °°oi elm nl 0- 0.50 - e f 0.40 - 0.30- © I-'i | olo i imu= - - o m m m ANT NEAT N91 GIN GB BATS EP N91 GIN GB BATS EP ANT NEAT N91 GIN GB BATS EP ANT NEAT Biogeochemical Province Biogeochemical Province Biogeochemical Province ANT NEAT N91 GIN GB BATS EP ANT NEAT 0.75 @ © 0.60 ' i-/ + 0.45 , ¢3 0.30 m 0 . N91 GIN GB BATS EP ANT NEAT N91 GIN G8 BATS EP Biogeochemical Province Biogeochemical Province Biogeochemical Province Fig. 5. Global total pigment ratios for a variety of biogeochemical provinces: a) CTp/C, b) CTp/Cps, c) CTp/(Cps + Cpp), d) CTp/Cc, e) CTp/C b, f) CTp/Cpp, g) (Cps + Cpp)/Cabc, h) (Cps + Cpp)/Ca, and i) Cpp/(Cps + Cpp). The provinces are encoded as follows: Antarctica (ANT), NEAT (NEAT 89-90), N91 (NEAT 91), GIN Seas (GIN), Grand Banks (GB), BATS, and EqPac (EP).

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TheSeaWiFSCZCS-TypePigmentAlgorithm GIN Seas(0.367),andhighestin Antarctica(0.514).The 3. BAND-RATIO ALGORITHMS low fractionin the GIN Seasis partly compensatedby the highfractionof chlorophyllb (0.085) and chlorophyll c tested methods used for CZCS, is essentially empirical, al- (0.103). In contrast, the fraction of carotenoids, mostly though, as is the case with the mainstream semianalyti- PSC, is lowest in Antarctica (0.295) and relatively constant cal SeaWiFS algorithm for chlorophyll a, the methods are elsewhere; relative to chlorophyll a, there are substantially validated by recourse to standard bio-optical, hydrological more carotenoids in the GIN Seas than anywhere else and models. By this means, semi-empirical algorithms are deonly here are they the major pigment group and exceed veloped, whereby analytical methods are used to propose the fraction for chlorophyll a. the form of the algorithm, while empirical methods are The approach adopted here, in line with the tried and The global mean fraction of chlorophyll a is close to 0.5 used to determine the numerical coefficients. (depressed only by the GIN Seas data), whereas, the global mean fraction of total carotenoids is about 0.4. The global for CZCS, developed by empirical methods (Clark 1981) mean fractions of chlorophylls b and c are 0.042 and 0.077, or semianalytical models (Gordon et al. 1988), were in The two-band (blue to green ratio) algorithms used respectively, and are most significant in the GIN Seas. fact remarkably successful for processing and interpreting Chlorophyll b is practically insignificant in the Antarctica, CZCS imagery, dependant as they were, on high accu- NEAT, and the BATS data. The global mean fraction of racy in-water measurements of normalized water-leaving PPC is 0.128, over 20% of the total pigment composition in radiances, LWN(A), and concurrently determined pigment BATS and EqPac data (both of which are high light) and concentrations. Any errors in the historical CZCS pigrelatively low in the Antarctica (0.043) and NEAT 89-90 ment databases are a result of the sensor measurements data, both of which are low light environments. (radiometric accuracy and stability, as well as atmospheric The extremes of the interpigment variance, from pro- correction procedures) and not a result of the algorithms vince to province, is highlighted in Table 3 and Figs. 5g-i, employed. The NET database of water-leaving radiances which shows the ratio of total carotenoids (Cpp + Cps) and in situ pigment measurements, although restricted in to chlorophyll a (C_), and to total chlorophyll (C_bc). The its geographical and seasonal coverage, is still the major latter being the sum of chlorophylls a, b, and c. The lowest reliable source of data for algorithm development and is ratio (0.522) is found in Antarctica and the highest ratio used in this paper for these reasons. (1.129) is found in the GIN Seas. These extremes are re- Recently, the NET database has been reworked for the duced somewhat if the carotenoid to total chlorophyll ratio SeaWiFS wavelengths and bandwidths using binomial or is considered (0.416 for Antarctica and 0.738 for the GIN polynomial curve fitting procedures to generate a range Seas). of empirical algorithms, which successfully explain high percentages of the variance between the variables. All of Table 3. Global interpigment ratiost. these are completely satisfactory as SeaWiFS algorithms, and with the greater precision of the SeaWiFS sensor, they Cpp"'Cps Cpp--Cps Region R 2 R 2 should provide accurate interpretations of the imagery (af- Ca Cab¢ ter atmospheric correction). These algorithms are listed Antarctic 0.522 94.3 0.416 97.9 in Table 4. Using NASA Airborne Oceanographic Lidar NEAT 89-90 0.835 94.8 0.735 97.2 NEAT 91 0.814 92.9 0.745 91.9 GIN Seas 1.129 86.3 0.738 84.5 SeaWiFS data, Aiken et al. (1992) demonstrated that com- Georges Bank 0.665 95.0 0.549 96.3 binations of two-band ratios could successfully explain a BATS 0.824 98.8 0.715 98.3 greater percentage of the variance between pigment and EqPac 0.911 98.2 0.711 97.2 radiance ratios than any single two-band ratio on its own Global 0.814 0.658 (AOL) data from the North Atlantic Bloom Experiment (NABE) in May 1989 (Hoge and Swift 1993) to simulate (Table 5). Similar results were obtained by Aiken and t Regression assumes no intercept. Moore (1995), using data from a bio-optical model with the values for absorption and scattering coefficients taken Using the radiances LWN(443) and LwN(550), these from the literature. Algorithms for the other constituents differences in the relative pigment concentrations would of the water column could be derived from the same synlead to significant differences in the coefficients of a stan- thetic data (see Table 6). dard two-band algorithm for each of these different biogeo- In the following section, an attempt is made to juschemical (bio-optical) provinces, because of the different tify the validity of band-ratio algorithms, employing simcontributions to the optical absorption from each pigment ple two-band ratios or linear combinations of more than at 443 and 550 nm. It is likely that multiband algorithms two ratios. These can account for a greater percentage of (3, 4, and 5 wavelengths) designed to account for the in- the variance between variables in the NET data, because fluences of different pigments on the absorption at each each two-band ratio can be related to a specific property waveband, may produce algorithms that would have more of the bio-optical assemblage, which can be demonstrated widespread application. by recourse to simple bio-optical models. 8

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J. Aiken,G.F.Moore,C.C.Trees,S.B.Hooker,andD.K.Clark Table 4. Algorithms from D. Clark. Ao AI A2 A3 R 2 Parameter Case Ratio CTp 1 2 + 3 + 4:5 8.73 -11.20 4.43 -0.62 97.0 CTp 1+2 2+3+4:5 4.76 -5.36 1.70 -0.21 93.6 CT 1 2+3+4:5 8.00 -9.99 3.77 -0.50 95.8 CT 1+2 2+3+4:5 4.74 -5.37 1.65 -0.18 92.2 4.81 -7.56 3.32 -0.50 72.2 TSM 1 2 + 3 + 4:5 TSM 1 + 2 2 + 3 + 4:5 5.57 -8.64 3.83 -0.58 84.6 ISM 1 2 + 3 + 4:5 11.20 -18.70 8.77 -1.37 60.1 ISM 1 + 2 2 + 3 + 4:5 4.81 -7.56 3.32 -0.50 72.2 OSM 1 2 + 3 + 4:5 4.79 -8.02 3.75 -0.60 74.1 4.39 -7.29 3.35 -0.53 83.6 OSM 1 + 2 2 + 3 + 4:5 c(535) 1 + 2 2 + 3 + 4:5 3.56 -4.53 1.44 0.15 86.6 c(535) 1 + 2 4:5 -0.12 -1.74 81.6 Note: All are loglo regressions. Table 5. AOL algorithms (Aiken et al. 1992' of the form a(Lu()I):Lu(A2))Z(L(Aa):L_,(A4)) _. _a 2 /3 )4 Ot 410 555 0.78 440 555 2.02 490 555 2.51 440 555 410 440 1.88 440 555 440 490 2.69 fl 7 R 2 N -2.90 64.1 753 -2.84 86.3 753 -2.77 92.8 753 -2.47 2.71 86.8 733 -2.64 4.04 93.7 733 Table 6. Model algorithms (Aiken and Moore 1994) of the form a(R(A,):R(A2))(R(£a):R(A4)) _ Algorithm A1 A2 A3 A4 Chlorophyll 412 555 443 555 490 555 443 555 412 443 490 555 412 555 DOC 412 555 443 555 490 555 443 555 412 443 490 555 412 555 4. BIO-OPTICAL MODELS 4.1 Analytical Basis of Band-Ratio Models Water-leaving radiance is a function of the downwelling light field, the interface effects, and the inherent optical properties (IOPs) of the water column constituents integrated over 1-2 optical depths. Analytically, it can be expressed as LWN ---- F0 (1ny(---r---p)(1 - tS)R]_j (1) where F0 is the extraterrestrial irradiance, n is the refractive index of seawater, R is the irradiance reflectance, p is a fl V R 2 1.87 -1.87 69 1.53 -2.15 89 1.77 -2.71 95 0.52 -2.41 -5.20 95 1.05 -4.66 -5.37 69 1.44 -0.92 28 1.04 -0.56 12 O.97 -0.51 6 1.33 -1.10 -10.2 90 -1.67 3.51 -14.8 77 the Fresnel reflectance at normal incidence, _ is the Fresnel reflectance for sun and sky irradiance, r is the air-water reflectance for diffuse irradiance, and Q is the ratio of upwelling irradiance to radiance, which varies with the angular distribution of the upwelling light field, and is equal to r for an isotropic distribution. The (1 - p)(1 - _)n -2 term gives the effect of the air-water interface, and shows a weak relationship with wavelength, varying as the refractive index of water. The term 1 - rR can be assumed to be unity in Case-1 waters. Assuming the interface term is constant, the ratio of remotely sensed water-leaving radiances at wavelengths )ti and Aj, respectively, is expressed as Li.j = R(A,)Q(Aj)Fo(Ai) (2) " R(A3)Q(A,)Fo(),j)'

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The SeaWiFS CZCS-Type Pigment Algorithm where the expression L,:j is a shorthand form for the ratio LwN (,)/LwN (). Q shows a weak relationship with wavelength, which is analytically difficult to determine, the main factor being the relative change in scattering phase function (Morel and Gentili 1991 and 1993). The main determinant of the radiance ratio is the irradiance reflectance R. This may be expressed as = a(,0, r l, (3) L J where G(#0, ;) represents the effect of the downwelling light field; bs(,,k) is the backscatter coefficient; and a(;) the absorption coefficient. The IOPs, a()Q and bs(,), are the sum of the optical properties of pure seawater and the optically active water column constituents, i.e., chlorophyll (a, b, and c), carotenoids, dissolved organic matter (DOM), and detrital particulates. Substituting for R, the normalized water-leaving radiance ratio, Li:j, is expressed as r a(.,kj)bs(,,ki)Fo(,,ki) 1 (4) (7) L,:j= gL J' where g is assumed to be a constant that consists of the ratios of the air-sea interface effects, the effects of the light field [the Morel and Gentili (1991) f factor], and the relative spectral variation of Q. Using Morel and Gentili's (1993) figures for the spectral variation of .f/Q, g will be 1 =t=3.5% with the remotely sensed viewing geometry. For the purposes of discussion of the effects of the water constituents, the factor g has been omitted (it is assumed to be unity). By partitioning the IOPs of the constituents of the water into the sum of the parts, Li:j can be expressed as a,.(.j) + ag(,j)G + a(,j)c Li: j a(.X,) + %(;)G + %(,)C (5) sorption, and ag(443) is approximately 0.3 x a¢(443). If bs_,(,,)+ bs,,(,,)P Fo(;,) X X -bb(,,kj) + bsp(Xj)P Fo(.,kj)' where a_ and bbw are the absorption and backscatter coefficients of water, respectively; P is the particulate concentration including detrital material, and bbp is its specific backscatter coefficient (normally normalized to chlorophyll a concentration); G is the concentration of DOM and DOM-like absorbers and a 9 its specific absorption; and C is the chlorophyll biomass concentration and av is its specific absorption. In this formulation, the backscatter has been decoupied from the chlorophyll concentration, and it is assumed that the relationship between the backscatter and phytoplankton biomass depends on ecological, rather than optical, correlates. The formulation of biomass absorption does not include the package effect (Duysens 1956). The 10 chlorophyll biomass absorption can be partitioned according to the functional groups of pigment. This includes chlorophyll a, b, and c; the photosynthetic carotenoids; and the photoprotectant carotenoids as per the following formulation: Ca¢ = a_,Ca + abC b + acCc, (6) + apsCps 4- appCpp where Ca, Cb, C_, aa, as, and ac are the concentrations and specific absorptions of chlorophyll a, b, and c, respectively; Cps, Cpp, aps, and app are the concentrations and specific absorptions of the photosynthetic and photoprotectant carotenoids, respectively. It can be seen from this that band-ratio algorithms are almost wholly dependant on the IOPs. The CZCS algorithm, or the SeaWiFS equivalent, can be taken as a case study. The )V555nm algorithms (where)i = 412, 443, 490, or 510nm) can be approximated by the following expression: a,.() + %(),j)G + a,sc(),j)C L,:j = a,.(),,) + %(.)C. + aas(.,)C bb(,,k_) + bbp(A,)P Fo()q) X X -bbp(£j)P Fo(Aj)" The particulate backscatter, bbp, will come from both detrital material and phytoplankton; this backscatter, although correlated with chlorophyll biomass, shows a highly variable relationship from province to province. For all but the most oligotrophic waters, bbp is greater than bbw at 555nm (or 550nm for CZCS). Bricaud et al. (1981) show for typical oceanic particle distributions that the wavelength dependence for bbp is approximately A -- 1; thus, the backscatter term in (7) can be approximated by an empirical constant. Bricaud et al. (1981) showed for most oceanic areas, DOM absorption is correlated with chlorophyll-specific abthis DOM dependence is used in (7), then the radiance ratio can be approximated by [a_(j) + a,(j)C] F0(,) L_:j = Bb[a_(£_ ) +aa,()i)C j Fo(,,kj)' (8) or + CA ' (9) where Bb, B, Aj, and A_ are arbitrary constants. In the case where LwN(555) is the reference band of the twoband ratio and when ag is much less than a_ (i.e., C less than 1.0mgm-3), the radiance ratio can be further approximated to B L,:j - 1+CA" (10)

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J. Aiken,G.F.Moore,C.C.Trees,S.B.Hooker,andD.K.Clark equa- 4.2.1 Model Parameterization In the analysespresentedhere,the hyperbolic tions(9) and(10)arethe basicmodelsforthe two-band ratioalgorithmsthat useSeaWiFSband5ratherthanthe conventionalmodelderivedfroma log-logfit. Thesehyperbolicmodelshavethe usefulpropertythat thecoefficient B can he expressed in terms of the IOPs of pure seawater, i.e., [bbw(A,) aw(Aj) Y0(adl (11) B = gL_a,(:,) J' and that there is a lower limit to the ratio of normalized water-leaving radiances This lower limit is useful, since it determines the range of applicability of the algorithms, i.e., the point where the radiance ratio does not give a meaningful estimate of chlorophyll a or pigment. The factor g in (11) corresponds to the ratio of the Morel and Gentili f/Q ratio at bands i and j. Morel and Gentili (1993) found this ratio to be 0.804 for the 440 and 565 nm wavelengths, over the whole range of water types and it0. Comparisons of the clear water B values in Tables 11 and 12 show the f/Q ratio to be 0.799 for the 443:555 band ratio. This further validates the hyperbolic model, since discrepancies between model values can be explained in terms of light field effects. 4.2 Model Development Two bio-optical models were developed to support the analysis of the various algorithms: the first to determine the differential effects of the bio-optical determinands on the radiance ratios; and the second to determine the effects of the biological variability and intercorrelation of the water column constituents on the radiance ratios. The purpose of the second model was to test algorithm formulation on sets of simulated data that contained variability found in a wider range of bio-optical provinces than the NET data. Both models use (5) and (6) with a full pigment assemblage. The package effect was not included in either model. Raman emission was included in the second model, using an approximation of Marshall and Smith's (1990) expression for surface Raman reflectance: bnE (13) LIWN -- 6.0Qa + a" where E_ is the downwelling irradiance at the Raman excitation wavelength, bR is the Raman scattering coefficient, a' is the absorption at the Raman excitation wavelength, and a is the absorption at the Raman emission wavelength. Without the Raman term, the model does not give a reasonable approximation to the optical properties of pure water. All the data were integrated over the SeaWiFS band responses (Barnes et al. 1994). Data for pure seawater scattering were taken from Morel (1974) and for pure seawater absorbancy from Smith and Baker (1981). Particulate backscatter was scaled to the backscatter from Gordon et al. (1988), at 443nm and 1 mgm -a chlorophyll; backscatter for other wavelengths was calculated using an a -n dependence, with n = 1. The exact value of n, however, will depend on the oceanic particle size distribution (Morel and Prieur 1977). Carder (pers. comm) indicates n depends on the R(443):R(490) ratio, which is dependent on DOM and the carotenoid to chlorophyll ratio. DOM absorption was determined using a curve of the form ag(A ) = ag(375)e-8()'-a75) (14) using a slope S of 0.014 and a base value of 0.06 (Bricaud et al. 1981), the slope being identical with that used in Carder et al. 1994. Detrital material was assumed to have the same basic spectral shape as DOM and was included in the model with a backscatter to absorption ratio of 1:2.18 derived from the transmission and absorption data of Prieur and Sathyendranath (1981) and assuming the San Diego harbor scattering phase function (Petzold 1972). Specific pigment absorption was taken from Bidigare et al. (1990). The model parameter values are summarized in Tables 7 and 8. The estimated global averages for pigment are shown in Table 9, and compared with data for chlorophyll specific absorption from Prieur and Sathyendranath (1981). 4.2.2 Global Determinands This model was used to determine the differential effects of the biogeochemical parameters on the two-band ratios. The values of these parameters were fixed for a chlorophyll value of 1.0 ing m -3. The pigments were fixed at the global ratios of Ca:Cb=5.77; Ca:Co=5.01; Ca:Cps=l.28; Ca:Cpp=5.32; determined from the global pigment data set for chlorophyll a in the range 0.8-1.2. The particulates were assumed to be phytoplankton only. The increase in scattering effectively increased the phytoplankton specific scattering. Detrital material increased scattering and the base DOM absorption in the ratio 1:2.18. 4.2.3 BSM Data The driving variable for the Bio-Optical Synthetic Model (BSM) was chlorophylla; in each run of the model, 2,000 random data points were generated using a log uniform random variate with values of chlorophyll from 0.018- 20.08. These chlorophyll values were used to determine the detrital, DOM, and pigment concentrations, and, hence, bio-optical parameters using (6) and (7). Raman stimulated emission was simulated using a randomly varying F0 from 60-150#Wcm -2 nm -1 using (13). 11

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TheSeaWiFSCZCS-TypePigmentAlgorithm Table7. Inherentopticalpropertiesof bio-opticalconstituents. Band _ bbw bbp 1 412 0.0034 0.0031 2 443 0.0024 0.0030 3 490 0.0016 0.0028 4 510 0.0013 0.0027 5 555 0.0009 0.0026 t Data derived from Prieur and Sathyendranath (1981). aw a_ a¢t bR ( )t) 0.016 0.034 0.050 0.015 0.023 0.058 0.021 0.012 0.045 0.00048 0.036 0.009 0.036 0.00042 0.067 0.005 0.019 0.00029 Table 8. Inherent optical properties of phytoplankton pigments:t. Band _ a t aa 1 412 0.050 0.017 2 443 0.058 0.018 3 490 0.045 0.003 4 510 0.036 0.001 5 555 0.019 0.001 Data derived from Prieur and Sathyendranath (1981). :_ Data derived from Bidigare et al. (1991). a b ae a p s a p p 0.003 0.021 0.008 0.022 0.013 0.046 0.019 0.045 0.011 0.011 0.036 0.049 0.001 0.002 0.033 0.022 0.002 0.003 0.008 0.001 Table 9. Inherent optical properties of phytoplankton pigments weighted according to climatological ratios:[:. Band )_ a_ aE§ aa a b ac aps app 1 412 0.050 0.032 0.017 0.001 0.004 0.006 0.004 2 443 0.058 0.053 0.018 0.003 0.009 0.015 0.008 3 490 0.045 0.046 0.003 0.002 0.002 0.030 0.009 4 510 0.036 0.032 0.001 0.000 0.000 0.027 0.004 5 555 0.019 0.009 0.001 0.000 0.001 0.007 0.000 t Data derived from Prieur and Sathyendranath (1981). :_ The absorbance values are calculated according to climatological ratios (Ca:Cb=5.77, C,:Cc=5.01, Ca:Cps=l.28, and Ca:Cpp:5.32). The data is from the GIN Seas, EqPac, NEAT, and the Antarctic. § az : aa ÷abTac +aps Tapp. The variance for the scattering was derived from the UK-BOFS database using c(670) and chlorophyll, for data at or above the 10% light level. The variance of the c(670) to chlorophyll ratio was found to be log-normally distributed with a variance of 0.823; since log variance is scale invariate, this log variance was used to generate the detrital backscatter contribution. The variance for phytoplankton backscatter was arbitrarily set at 0.100. The ratio of detrital backscatter was twice the value of phytoplankton backscatter; this figure was derived from Prieur and Sathyendranath's (1981) brain(525). The pigment data was similarly found to be log-normally distributed, with varib, following figures, derived from Trees et al. (1985), and valiances of 1.017, 0.683, 0.482, and 0.151 for chlorophyll chlorophyll c, PSC, and PPC, respectively. The variation of DOM absorption with chlorophyll is dependant on oceanic conditions. Bricaud et al. (1981) observe an almost constant background, ag(375) = 0.06, whereas Prieur and Sathyendranath (1981) and Carder et al. (1989) show a weak correlation with chlorophyll a, and Carder et al. (1986) show a good relationship with chlorophyll a. The assumption applied here is that ag co-varies 12 with chlorophyll a, but with a log variance of 0.21, derived from NABE DOM concentrations. The relationships described above resulted in the 95% ranges in the model input data of 0.023-15.02 for chlorophylla; 0.001-1.70 for chlorophyll b; 0.002-1.87 for chlorophyll c; 0.015-9.69 for PSC; 0.003-5.20 for PPC; and 0.024-13.62 for DOM relative absorption. In order to compare the results from the BSM data with the NET data, it was necessary to simulate pigment data. Trees et al. (1985) showed that the fluorometric method (Yentsch and Menzel 1963) of chlorophyll a determination was affected by coexisting chlorophyll b and c. Using the dated by comparison with NEAT HPLC data, fluorometric chlorophyll a was calculated as C s = 0.941Ca - 0.292C b + 0.371Cc (15) and pigment as Ps = 1.166Ca + 0.616C b + 0.544Cc. (16)

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J. Aiken,G.F.Moore,C.C.Trees,S.B.Hooker,andD.K.Clark 5. DATA ANALYSES 5.1 Algorithm Development In the previous section, a theoretical basis for bandratio algorithms using standard hydrological, bio-optical models was established. In this section, these models are used with selected data sets to derive empirical fits of the data to produce the algorithms for SeaWiFS. The inherfour groups of band ratios as the NET data (Table 10). The R 2 values are considerably higher for the BSM data (up to 96%) where the only source of variability is from the bio-optical determinands. The higher R 2 values are explained by the fact that no measurement error was added to the BSM data. The inference from this observation is that since the synthetic pigment and radiance data are so highly correlated, the basic bio-optical model used in the synthesis and the parameter values employed are both reent statistical properties of the NET data are explored to liable. establish a baseline for comparative reference. Using the conventional power law model and the pigment definition for the CZCS algorithms C_ + Cp = c_'(L,:j) z' (17) or ln(C_ + Cp) = ln(c_') + /3'ln (L,:j). (18) Table 10 shows the coefficients of the In-In regression and the percentage of variance explained (R 2) for pigment (Ca + Cp) regressed against all two-band combinations for the NET data (SeaWiFS band set), and Fig. 6 shows Using simple bio-optical models, a hyperbolic function of chlorophyll was found to be the simplest expression for the relationship for the band ratio, see (10), with an asymptotic coefficient (B) as C --_ 0, which relates to the IOPs of pure water given by (11). This simpler model is only robust where chlorophyll is low, and can be used to empirically determine the clear water B. Using the clear water B, the full range of data (Case-1 and Case-2) can be fitted using the full model (9). Tables 11 and 12 show the coefficients for the full model with Ca + Cp and Ca and band ratios fitted for the NET data. Tables 13 and 14 show the coefficients for chlorophyll a and simulated pigthe scatterplots for the major ratios. The regressions fall ment for the BSM data. In all cases, the percentage of into four groups, which depend on the wavelength of the reference radiance. The first group, reference LWN(555), variance explained is high, up to 91% for the NET data and up to 96% for the key band ratios for the BSM data. shows very high R 2 values for all two-band ratios, in- In all cases, this model provides a superior fit compared dicating that Ca + Cp is highly correlated in all cases. with the ln-ln regressions. The increasing value of R 2 from 412 to 443 to 490 to 510 Again, the primary conclusion from these findings is the is surprising since the 490 and 510 bands are at longer basic bio-optical models and parameters used are sound, wavelengths than the chlorophyll a (or phaeopigment) ab- as demonstrated by their utility to generate relationships sorption peaks. This is due to the highly correlated co- with high confidence. A secondary conclusion is that these occurrence and co-variance of chlorophyll and carotenoids methods, using synthetic data, are suitable for the gener- (which absorb at 490 and 510nm), demonstrated in Sec- ation of algorithms for parameters where there are few in tion 2, which means carotenoids effectively and accurately behave as surrogates for chlorophyll at the longer wavelengths. Coupled with this effect, the longer wavelengths are least affected by the absorption of light in the blue spectral region by DOM, which contaminates the 412 and 443nm wavebands to the maximum extent. The second and third groups of band ratios (reference bands 510 and 490 nm, respectively) also show increasing R 2 values with situ calibration measurements, but where there is a good knowledge of the IOPs of the parameters (e.g., DOM, pigments, etc.). It is suggested here that good in situ data sets with a limited range of parameter values (e.g., NET data which has no accessory pigment measurements) can be bootstrapped to synthetic data to infer a greater number of parameters. It can be shown in these and other analyses that there is potential for closure between apincreasing wavelength, 412 to 443 to 490 nm. The same ex- parent optical measurements and synthetic measurements planations of the effects of co-existing DOM and accessory pigments (carotenoids) apply. In each case, the percentage that use IOPs. of variance explained is smaller than for the first group as 5.2 Sensitivity Analysis of Ratio Models the reference wavelengths are influenced somewhat more by carotenoid absorption. Only the LWN(412):LwN(443) ratio is poorly correlated with pigment; (this is understandable since these wavebands are influenced most significantly by the absorption of DOM and detritus which have similar absorption spectra). Table 10 shows the In-In regression coefficients and R 2 values for the BSM data set. The scatterplots are by 5-10%, the effect being greatest in the blue part of the for the LWN(555) base ratios compared to NET data ex- spectrum and decreasing towards the green. It is surprisshown in Fig. 7. Interestingly, these regressions show Figure 8 shows the sensitivity of retrieved pigment concentration for each of the primary ratios (412:555, 443:555, 490:555, and 510:555). Table 15 shows symbolically the effects on the remaining secondary band ratios, together with the main ratios. At the level of chlorophyll a considered, detritus, DOM, and chlorophyll-specific scattering all change the apparent chlorophyll retrieved and the ratios actly the same patterns of coefficients and R 2 values for the ing that scattering should depress the ratio and result in an 13

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TheSeaWiFSCZCS-TypePigmentAlgorithm Table 10. Logregressionsof NETandsyntheticsimulatedpigmentswith bandratios. Band NET C_ + Cp Ratio Intercept Slope R 2 412:555 -0.339 -1.095 72.2 443:555 0.051 -1.284 81.7 490:555 0.696 -2.085 86.4 510:555 0.688 -2.864 86.8 412:510 -0.505 -1.533 58.7 443:510 -0.464 -2.209 74.4 490:510 0.627 -7.035 81.5 412:490 -0.791 -1.733 46.5 443:490 -0.924 -2.983 65.9 412:443 -0.601 -2.913 20.9 Table 11. NET data pigment curve fits. Band Ratio C A1 0.07 16.79 1 0.76 84.4 13.80 412:555 13.14 + 1.86 0.059 + BSM Ps Intercept Slope R 2 -0.199 -1.387 90.9 0.028 - 1.562 95.1 0.322 -2.262 95.4 0.151 -3.541 95.9 -0.418 -2.193 84.4 -0.073 -2.702 87.8 0.604 -6.117 92.3 -0.967 -3.315 77.5 -0.605 -4.745 82.6 -1.629 -9.761 58.2 A2 R 2 C_t 443:555 9.55 + 1.08 0.068 + 0.05 9.68 1 0.40 87.9 11.94 + 0.04 3.82 1 0.16 89.8 5.67 490:555 5.29 + 0.37 0.232 0.03 1.94 1 0.09 91.4 2.80 510:555 3.07+0.15 0.262 1 C_ adjusted for Raman scattering in model. Table 12. NET data chlorophyll curve fits. Band Ratio C AI A2 R 2 Cwt 412:555 13.14 + 1.86 0.081 1 0.09 20.51 1 0.94 83.3 13.80 0.11510.07 11.04 1 0.52 86.5 11.94 443:555 9.55 1 1.08 490:555 5.2910.37 0.328 1 0.05 4.27 1 0.21 88.5 5.67 510:555 3.07+0.15 0.350 1 0.04 2.44 ± 0.12 90.5 2.80 C_ adjusted for Raman scattering in model. Table 13. BSM chlorophyll a curve fits. Band Ratio C A1 A2 R 2 Cwt 412:555 13.82 1 0.84 0.167 1 0.03 23.09 1 0.34 90.3 13.80 443:555 11.7410.52 0.27910.03 18.5810.24 92.7 11.94 490:555 5.35 + 0.10 0.499 10.02 7.42 10.09 95.4 5.67 510:555 2.63 1 0.02 0.662 1 0.01 3.61 1 0.04 95.8 2.80 f C_ adjusted for Raman scattering in model. Table 14. BSM pigment curve fits. Band Ratio C A1 A2 R _ C_ t 412:555 13.82 1 0.84 0.134 1 0.03 17.74 1 0.26 90.0 13.80 443:555 11.74 1 0.52 0.219 1 0.02 14.26 1 0.19 92.6 11.94 490:555 5.35 1 0.10 0.385 + 0.01 5.69 1 0.07 95.2 5.67 510:555 2.63 1 0.02 0.510 t 0.01 2..76 1 0.03 95.6 2.80 C_ adjusted for Raman scattering in model. 14

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1. Aiken, G.F. Moore, C.C. Trees, S+B. Hooker, and D.K. Clark 10.0 i o i ! ID ..,.l 1.0 ...................+...............+i .+ i s- ,..J t_+ L-+-'-'_ OO.00 0.1 "-------'-- 10.00 0.01 0,I0 1.00 p_m_t rr,gm" lOO io ID °° °° ......................... I 1,[ 0 o _ _ lo,oo oo'oo •o,oi o.1o .0o Moment m_ 4 10.o o oomi o _ ' o i o ' i I o r'6""",• o.... . ................. ',++, ID ............... .: : o i o : %01_oo'oo p'oT_ mOm" Oo _ ! 10.0 °°_ ii i ,+! o _ i :_00.00 0'1001 0.10 " pl_t morn" _o.o d) oo _ o o : o o to _o i q' o,i, -. .................. J _. 1.0 O i oo 1o,oo 1oo,oo 0.1 _ .00 0.01 0.10 p_t mO.m4 Ioo pIQr_t m_m" LWN (443):LwN (555), Fig. 6. NET d_ta (weighted pigment) scatterplots: a) LwJv(412) :LWN(555)' b) LwJv(490):LwP¢(510). c) LWN( 490):LwN(555)' d) LWN( 510):LWN(555)' e) LWN(412) :LwN(510)' and f) 15

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The SeaWiFS CZCS-Type 10.0 • 9 . ....., 1.0 8 U** __ 0..* • . :2.5-• • s-,oTn_. # *% 0,1 ......... i ......... i ......... 0.01 0.10 1DO 10.00 Pigment mg_ 4 10.0 ....... , ........., ........; • . ..'a(.. -;--,:.,.,.,_ . 1o ...,,,, .......:.. • ,,... 0.1 ' ' ....... ' ......... ' 0.01 0.10 1.00 10.00 PtgTmntmorn4 Pigment Algorithm 10.0 ........., ........., ......... b) _ 1.0 J 0.1 0.01 0.10 1.00 10.00 Plgnent morn 4 10.0 ......... ! ......... u ......... d) LO LO _, 1.0 0 0.1 , ........ t ..... ,, ..t ........ 0,01 0.10 1.00 10.00 _t _4 Fig. 7. NET data (circles) compared with the synthetic data set (diamonds): a) LwN(412):LwN(555), b) LwN(490):LwN(555), c) LWN(443):LwN(555), and d) Lwg(510):Lww(555). 16

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J. Aiken, G.F. Moore, C.C. Trees, S.B. Hooker, and D.K. Clark ,,-, 1.20 e,.. E 1.15 .m a. " 1.10 ,n 01 ,'- 1.05 m e., 0 1.00 412:555 443:555 490:555 510:555 Band Ratio 412:555 443:555 490:555 510:555 Band Ratio 412:555 443:555 490:555 510:555 Band Ratio •. 1.20g .E 1.15 412:555 443:555 490:555 510:555 Band Ratio , 1.20b _ 1.15-" .__ a. • .=_ 1.10- 1.05- =,- 1.00- 412:555 443:555 490:555 510:555 Band Ratio d 412:555 443:555 490:555 510:555 Band Ratio 412:555 443:555 490:555 510:555 Band Ratio 1.20h ¢w 1.15-" .__ a. • .__ 1.10- ¢D 1.o5m 412:555 443:555 490:555 510:555 Band Ratio Fig. 8. The effects on retrieved pigment of 20% (10% for detritus) change in concentration relative to a climatoligical value: a) scattering, b) Cb, c) DOM, d) Co, e) detritus, f) Cps, g) Ca, and h) eRR. All values are calculated for a chlorophyll concentration of 1.0 mg m -3. 11"

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TheSeaWiFSCZCS-TypePigmentAlgorithm Table 15. Sensitivityofratioto modelsingleparameterchange. Band Ratio Scat. Gelb. Detr. 412:555 +÷ ÷+÷÷ +÷÷÷ 443:555 ÷÷ ÷÷ ÷+÷ 490:555 ÷ ÷ ÷÷ 510:555 ÷ 412:443 ÷ ++ ÷++++ 443:490 ÷ ÷÷÷÷ 490:510 ÷÷÷ 412:490 ÷ ÷÷÷ +÷÷÷+ 443:510 ÷ + ÷÷÷÷÷ 412:510 ÷÷ ++÷ ÷÷+÷÷ Chl. a Chl. b Chl. c PSC PPC +÷ ÷÷ ÷ ÷ ÷ ÷÷÷ ÷ ÷÷÷ ÷÷ ÷ +÷ ÷+ ÷ ÷+ Note: < 3% +; 3 - -5% ++; 5 - -7% +++; 7 - -20% ++++; > 20% +++++ (and similarly for negative values). Table 16. Principal component factor loadings. Band Ratio F1-NET F1-BSM F2-NET F2-BSM F3-NET F3-BSM 412:555 ÷ + 443:555 ÷4 +÷ 490:555 ÷+÷ +++ 510:555 ÷÷÷ ÷÷÷÷ 412:443 ÷÷÷÷÷ 443:490 + 490:510 ÷+ ÷ 412:490 ÷ ÷÷÷ 443:510 412:510 ÷÷ ÷ ÷÷ ÷ ÷÷÷÷÷ ÷÷÷÷÷ ÷÷÷ ÷÷÷÷+ ÷÷÷÷ ÷÷÷ ÷ ÷÷ --- ÷÷÷÷÷ --- ÷÷÷ Note: < 3% +; 3 - -5% ++; 5 - -7% +++; 7 - -20% ++++; > 20% +++++ (and similarly for negative values). increase in apparent chlorophyll retrieved, but it should be 5.3 Analysis of NET and BSM Data remembered that the ratios have been explored in a region where backscatter from water is dominant. limited measures of biogeochemical parameters other than Chlorophyll a has a dominant effect on the 412:555 and chlorophyll a and phaeopigment. In order to determine the 443:555 ratios, contrasted with photosynthetic carotenoids bio-optical variability of the data, principal components which affect the 490:555 and 510:555 ratios. The photopro- The NET data represent high quality optical data, with tectant carotenoids effect most of the band ratios with the analysis was used, and the results were compared with the dominant affect being on the 490:555 ratio. Chlorophylls BSM data set containing variability from a wider range of b and c have only minor affects on the band ratios, and provinces than those present in the NET data. The aim was to determine if the NET data contained sufficient varimajor province difference would be needed for their affect on band ratios to become important. The only two-band ability to generate global algorithms, and to determine how ratios affected by all pigments are the 443:555 and 490:555, far bio-optical variability reflects ecosystem variability. with the later being less affected by DOM or detritus. Analysis of the NET data log radiance ratios, by prin- Of all the secondary ratios, the most useful may be cipal components analysis using varimax rotation, showed 412:443, which is not influenced by pigment but is strongly that three factors could be extracted explaining 99.6% of influenced by DOM and detritus. For determining ac- the total variance. These three factors explained 57.5, 41.2, cessory pigments, the 412:490, 443:510, and 412:510 ra- and 0.9 percent of the variance, respectively. The relative tios seem most promising, since they show a differential factor loadings are shown in Table 16, with the normalinfluence between photosynthetic carotenoids and chloro- ized factor loadings for the LWN(555) based ratios shown phyll a. The response of the different ratios to differing in Fig. 9. The first column of each factor pair shows the biogeochemistry, would imply that most, if not all, of the factor pattern for the NET data. By comparison with the optical and biogeochemical information found in upwelled previous analysis of radiance ratio patterns, the first two spectra is contained in the primary and secondary ratios factors, F1 and F2, can be ascribed to pigment biomass and considered in this study. detritus concentration, respectively, with the first factor 18

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J. Aiken,G.F.Moore,C.C.Trees,S.B.Hooker,andD.K.Clark 100- \ C 75- m m e,, t_ .,., 50- C t,,} "- 25fl,,,, ° 443:555 490:555 510:555 412:555 Band Ratio 100- 8, 75 m ,_ 5o 8 25 O- I i m o 412:555 443:555 490:555 510:555 Band Ratio 100- I m 0'} p. 75 m ¢r_ 5O C CJ ¢_ 25 G) Q. 0 412:555 443:555 490:555 510:555 Band Ratio Fig. 9. The normalized spectral change in the factors F1 (top), F: (middle), and F3 (bottom). The NET data is shown as the white bars and the simulated data as the hatched bars. 19

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TheSeaWiFSCZCS-TypePigmentAlgorithm the NET data, a factor that was not included in the model. showinga high correlation with log pigment (R2-89.5%) or log chlorophyll (R2=89.2%). The meaning of the third Figure 5i shows such a shift in pigment ratios; high light areas, e.g., EqPac and BATS, show a higher PPC relative factor, F3, is more difficult to determine, but the principal to PSC ratio or PPC to carotenoid ratio. loadings are on those ratios which correlate with carotenoid concentration, notably 490:510 and 510:555. This factor It is surprising that the information in the NET and could be related to absolute carotenoid concentration or BSM data sets can be reduced to three factors, whereas there are five factors in the biogeochemical variables that relative pigment abundance (PSC:PPC ratio). The BSM data showed a similar factor pattern to the generate the BSM optical data. This may be due to the NET data with 99.9% of the total variance explained, and limited range of bands chosen, but the reduction of optical the three factors explaining 52.3%, 43.2%, and 4.4% of the data to three factors is supported by a number of other variance, respectively. The change in the total variance ex- studies. Sathyendranath (1981), using principal compoplained is probably attributable to the BSM data assuming nents analysis on log reflectance data from 400-650 nm a perfect radiometer; the relatively small change in total with a resolution of 10nm, found three factors explainvariance explained indicates the quality of the NET opti- ing 57.6%, 42.1%, and 0.2%, respectively, of the variance. cal data. The higher percentage variance explained by the Garver et al. (1995), using empirical orthogonal factor third factor, reflects the greater range of pigment and detri- analysis on absorbance spectra from 400-700nm, found tal variance introduced into the synthetic data. The factor detrital and phytoplankton components explaining 54% profiles are shown in Table 16, and the patterns for FI and and 44%, respectively, of the variance; the residual 2% of F2 are almost identical to the NET data. The comparison variance was assumed to be pigment variability. Mueller with F3, is less clear, but both NET and BSM data show (1976), using principal components analysis on airborne loadings on ratios that relate to carotenoid concentration. spectrometer data from 400-750 nm with a resolution of 5- The difference in the precise loadings of the third factor is 7.5 nm, found four factors explaining 77.6%, 17.21%, 2.4%, likely to be caused by the lower pigment variability in the and 0.9%, respectively, of the variances; the first three fac- NET data compared with the BSM data. This hypothesis tors were related to pigment, showing similar patterns to needs to be tested further using data sets that have concur- the NET factors, but the fourth factor was unrelated to rent radiance measurements and HPLC pigments. These pigment and may have been due to residual atmospheric will become available as part of the SeaWiFS calibration effects. and validation activity. From this and other studies, it can be concluded that In the BSM data, the underlying biogeochemistry is for oceanic waters, the SeaWiFS band set provides suffiknown a priori; thus, the factors can be related to their cient information to adequately specify the upwelling specprincipal driving variable. F1 shows the best relation- trum, and that the full compliment of ratios contain the ship with total pigment, chlorophyll (a, b, and c) , and full variance of the original upwelling radiance. The total carotenoids, with the best correlation being with total pig- variability in the optical signal can he reduced to three facment (R2=89.9%). F2 shows little correlation with pig- tors, although there are more factors in the underlying bioment, correlating with the total particulate concentration geochemistry. This fact has two implications for algorithm and total DOM-like absorbance (i.e., detrital absorbance), development: first, it is unlikely that more than three suitbut with a poor R 2 of 58.1% and 62.3%, respectively. F3 ably chosen ratios are needed to retrieve any parameter to shows low correlations with all variables, the best being maximum accuracy; and second, different biogeochemical with DOM-like absorbance and photosynthetic carotenoids, signals are not uniquely converted to optical signatures, with R 2 of 30.6% and 27.6% respectively. This would still implying that the perfect single biogeochemical parameter seem to indicate F3 represents the chlorophyll to carotenoid algorithm may not exist. ratio, since an increase in DOM has almost the same effect on a normalized SeaWiFS spectrum as an increase in 5.4 Multiband Algorithms carotenoids. Figure 9 shows the spectral differences in the factors, Multiband algorithms were developed using two methderived by selecting data where each factor was lower than odologies, the first used empirical multiple regression of log the factor average. F1 and F2 show similar patterns in ratios to log pigment, i.e., fitting curves of the form both the NET data and the BSM data, although for F2 the typical DOM curve is more pronounced in the NET data than in the BSM data. F3 shows a greater disparity between the NET data and the BSM data; the main affect in the NET data is the 490:555 ratio whereas in the BSM which is equivalent to data, it is the 510:555 ratio. The spectral patterning of the change in the NET data would suggest a shift in PPC, compared with a shift in total carotenoid in the BSM data. The change in PPC is probably due to light adaptation in 20 ln(C, + Cp) = a0 + a, ln(L,:j) (19) + a2ln(Lm:n) Ca + Cp = a YI [L,:j(k,)] (20)

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J. Aiken,G.F. Moore, C.C. Trees, S.B. Hooker, and D.K. Clark ratios or chlorophyll are linear. Such algorithms are more attracwhere k represents an index to two vectors of band kl and k2. The second method used linear combinations algo- in terms of pigment. Table 20 gives the results of linear of the hyperbolic estimates. Before examining these rithms, it is appropriate to make a statistical comment. First, the R 2 values returned by multiple log regression tive since the intermediate estimates can be interpreted combinations of pairs of estimates. In all cases, except the 443:555 and 510:555 combination, there was no sigare not comparable with those returned by either nonlin- nificant intercept for the regression. The best retrieval Second, was for the 443:555 and 490:555 combination, with an R 2 ear curve fitting or by linear multiple regression. the of 95.3%; this algorithm is shown in Fig. lla, compared a small improvement in R 2 can be obtained by shifting position of one outlier, and may not be a real improvement with the 443:555 hyperbolic fit. Compared with the ln-ln in the quality of calibration. With these caveats in mind, multiple regression in Fig. 10a, there is less bias at low log-log multiple regressions are considered hereafter. pigment concentrations, whilst retaining the 1:1 relation- Table 17 shows multiple regression algorithms for the ship at high pigment concentrations. The results for the NET data. The best R 2 achieved is 87.3% (for the 443:490 Antarctic data shown in Fig. llb are encouraging, with and 443:555 combination), which represents a marginal im- points pulled closer to the 1:1 line; however, there may provement of the variance explained compared with 86.8% be a slight tendancy to overestimate pigment in this data. (510:555 in Table 10). Compared to the R 2 for the 443:555 The 443:555 and 490:555 combination has advantages for ratio, however, an improvement of 6.6% is achieved. Fig- implementation as a remotely sensed algorithm; it avoids ure 10a shows comparison of this algorithm with the single the 412 nm band where atmospheric correction may be a ratio hyperbolic model; the difference between the two are problem, and tends to the 490:555 algorithm in high pigslight, but a few points are pulled closer to the 1:1 line. ment waters where Lwg(443) tends toward zero. The robustness of this algorithm was tested with some data collected in the Antarctic (BOFS Sterna), where there 6. IMPLEMENTATION are known differences in the optical properties of the water (Mitchell and Holm-Hansen 1991 and Sullivan et al. 1993). Figure 10b shows a comparison between the NASA The algorithms shown in the previous sections are all valid for the data and models considered. The development on an interim CZCS compatible algorithm involves regression consideration of the sensor aspects and state-of-the-art at- CZCS chlorophyll algorithm and the multiple algorithm. The retrieval shows a less biased estimate chlorophyll than the NASA algorithm, with a slope closer to 1:1. The retrieval is noisier, and this may be due of mospheric correction. The data to be obtained from the SeaWiFS instrument is different from modeled and seato truth data in three important aspects: radiometric problems, since the noise is additive and in- 1) The data is digitized on a limited scale--10 bits creases as more bands are used. It is not known if this will at the top of the atmosphere compared with 16 a retrieval bit resolution for the NET data and infinite resbe a problem for SeaWiFS. It is noteworthy that developed on one data set, should show an improvement with another from a different bio-optical province. Table 18 shows the algorithms developed from the BSM olution for the model data; 2) Atmospheric attenuation of the water-leaving signal will reduce the effective level still further, data, with the R 2 being in the range 93.3-98.1%. Since the especially at the shorter wavelengths, i.e., bands synthetic model assumes no error in measurements, these 1 and 2; and are the best retrievals to be expected unless Raman emisthe 3) The model and NET data are both based on sion is modeled. The difference between these R 2 and NET data R 2 is best explained by the pigment analysis methodology, since an error of 5-10% is expected in the pro- To address the first two points, digitization of the data Yentsch and Menzel (1963) chlorophyll. Table 18 also variables has been simulated by assuming a sun angle of 50 ° and an vides a list of algorithms for new biogeochemical nadir viewing geometry, whereas the SeaWiFS instrument will view up to 58.3 ° off nadir. extensive atmospheric transmission of 0.5. This assumption results that may be tested when data sets with more sea-truth measurements than the NET data become avail- in a dynamic range of about 160 counts for the waterable. Table 19 shows a test of multiple regression algorithms restricted to the SeaWiFS bands which are compatible improve- for bands 1-4, compared with pigment averaged into log with the CZCS. Except for total pigment, no list ranges. For bands 1 and 2, there is no detectible waterment could be found taking a band combination. The demonstrates the alternative interpretations that can derived from CZCS imagery; in particular it emphasizes the fact the 510:555 ratio is a carotenoid retrieval. for retically at present, since there are limited data, e.g., Po- Multiple regression is a more appropriate technique pigment larization and Directionality of the Earth's Reflectances the hyperbolic estimates, since the estimates of leaving radiance, and corresponds to fairly high atmospheric turbidity in the early spring, e.g., the North Atlantic bloom. Figure 12 shows the mean water-leaving radiance be leaving radiance above 10mgm -3, compared to bands 3 and 4 where there is significant water-leaving radiance. The issue of viewing angle can only be addressed theo- 21

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TheSeaWiFSCZCS-TypePigmentAlgorithm Table 17. NETdatalogmultipleregressionalgorithms. Variable Constant Coeff. 1 Ratio 1 Coeff. 2 Ratio 2 R 2 Ca 0.45 1.57 412:510 -2.46 443:555 84.7 Ca 0.94 3.47 443:490 -2.58 443:555 86.4 Ca + Cp 0.59 1.48 412:510 -2.41 443:555 85.4 Ca + Cp 1.09 3.39 443:490 -2.55 443:555 87.3 Table 18. BSM data log multiple regression algorithms. Coeff. 2 Ratio 2 Coeff. 3 Ratio 3 R 2 Variable Constant Coeff. 1 Ratio 1 Cs -0.040 4.60 412:510 --7.22 443:555 3.46 490:555 97.2 Cs 0.345 4.60 443:490 --2.89 443:555 96.1 Ps 0.194 --7.98 443:555 4.16 490:555 94.2 4.99 412:510 P 0.918 2.64 443:510 --2.77 412:555 97.7 P 0.229 10.77 490:510 --4.58 443:555 98.1 G 0.616 2.44 412:443 --4.43 412:555 11.12 490:510 98.2 G 0.657 12.49 412:510 --12.50 443:555 97.7 -0.133 --1.55 443:490 --3.71 510:555 97.1 Cpp nu Cps 3.93 412:443 Cpp nu Cps -0.174 --3.91 510:555 95.9 0.61 443:490 DOM -0.012 4.69 412:443 DOM -0.021 0.39 443:490 Cabc 0.724 0.58 412:443 CTp 1.063 --2.24 490:555 --1.41 412:490 --3.61 510:555 98.6 --3.77 510:555 94.7 --2.32 490:555 93.3 93.3 Table 19. BSM data CZCS compatible log multiple regression algorithms. Variable Constant Coeff. 1 Ratio I Coeff. 2 Ratio 2 R 2 Ca -0.157 443:555 93.3 -1.59 Ps 0.113 -1.60 443:555 93.2 P 1.024 -1.69 443:555 96.7 Cpp + Cps -0.251 -3.55 510:555 95.7 G 1.579 -1.70 443:555 93.2 443:555 94.2 DOM -0.069 -3.54 CTP 13.920 510:555 20.76 553:510 92.7 -40.60 Table 20. NET data multiple regression hyperbolic fit pigment. Constant H(Al:555) Coeff. 1 412 0.188+0.03 412 0.519 ± 0.15 412 0.954 :t: 0.07 443 -0.461 + 0.03 0.757 :t=0.24 443 -0.659 + 0.02 490 1.903 =t:0.13 22 H(A2:555) Coeff. 2 R 2 443 1.188 ± 0.08 84.8 490 1.121 + 0.05 92.5 510 0.759 + 0.04 91.4 490 1.821 + 0.08 95.3 510 1.101 =h0.29 66.0 510 -0.653 =1:0.06 91.6

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J. Aiken,G.F.Moore,C.C.Trees,S.B.Hooker,andD.K.Clark 10.00 O3 I 1.00 ............................................. E •. : ;.- ........ O..e.+..I. . ;......... .+.......... E Y °_ 13_ 10 .__ L- 0.10 cr • "_6 0 0.01 _ I O.01 O,10 Pigment mg.m 10.00 09 I 1+00 o MULT2 I o PIG44 1,00 10.00 -3 oi o i 0 • , • E 0.10 rr 0.01 z I 0,01 0.10 Pigment mg.m o MULT2 I o PIG44 1.00 10,00 -3 Fig. 10. Multiple regression log-log pigment [In(Ca + Cp) = 0.59 + 1.481n(LwN(412):LwN(510))- 2.411n(Lwg(443):LwN(555))] (circles and upper line) compared with hyperbolic pigment H(443:555) (diamonds and lower line): a) NET data (top) and b) Sterna data (bottom). 23

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TheSeaWiFSCZCS-TypePigmentAlgorithm I0.00 CO ! 1.00 .........................._..........:__ ................... "O If} ,$ 0.10 cr 0.01 I 0.01 O.10 Pigment mg.m 10.00 CO I 1.00 I1) E i "0 0.10 0.01 0.01 O.10 Pigment mgm i 0 MULT3 I 0 PIG44 1.00 10.00 -3 i oO oo ' o MULT3 o PIG44 1.00 10.00 -3 Fig. 11. Hyperbolic multiple regression pigment [In(Ca + Cp) = -0.461H(443:555) + 1.821H(490:555) (circles and upper line) compared with H(443:555) derived pigment (diamonds and lower line): a) NET data (top) and b) Sterna data (bottom). 24

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J. Aiken,G.F.Moore,C.C.Trees,S.B.Hooker,andD.K.Clark 40- 3O ¢- 23 o A\ %q "O 2O m E is 10 Key o Lw(412) - _ - Lw(443 ) ---t--- LW(490) ..... .@..... Lw(510 ) 0 ! I | e w|e| a I l I |l II i I I l II 0.01 0.1 10 100 Pigment [mg m-3] Fig. 12. Mean simulated SeaWiFS counts as a function of pigment, log(Ca + Cp). 1000 100 "7, 10 ----" ''''o... _ E 0.1 0.001 0.01 0.0001 0.01 0.1 1 Pigment [mg m -3] H(412:555) H(443:555) H(490:555) H(510:555) "'.. __ 10 100 Fig. 13. Derivatives of the hyperbolic model versus Ps. 25

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TheSeaWiFSCZCS-TypePigmentAlgorithm (POLDER)airborneimagery,to test publishedmodels.11, and 12 indicated there is no significant difference be- MorelandGentili(1993)indicatethat for ratios,the ef- tween the quality of fits of the ratios for either the log or fectsofviewingoff nadirarelessseverethanfor radiancehyperbolic model fit. The pigment relationship shown in inversionmethods;theviewingangleeffectis not seenin Fig. 14 indicates there are biological reasons for consider- CZCS images, but some effects may be observed with the ing the 490:555 ratio superior, since its response to chlorogreater radiometric precision of SeaWiFS. phyll will alleviate potential problems in oligotrophic wa- The precision of the retrieval can be addressed using ters. Both the hyperbolic model and log-log fits represent both the NET data and the hyperbolic model. The hy- adequate models for the data, but the hyperbolic model perbolic model can be differentiated analytically to show shows erroneous retrievals at high chlorophyll where there is little data. how dL_:j/dPs varies with pigment. The rate of change in ratio per unit pigment gives the inherent accuracy of data has been used to produce a revised set of coefficients the retrievals. Figure 13 shows this rate of change is high for the hyperbolic model. The constrained fits shown in for low pigment concentrations, and also shows a log-log Tables 21 and 22 give a better R 2 for the 490:555 ratio decrease with increasing pigment concentration. At low compared with the 510:555 ratio. Using these revised tapigment dLi:j/dP s is higher for the 412:555 and 443:555, bles, the error structure can be examined. Figure 15 shows but the rate of change is similar for all band ratios at pigthe mean residual error for both the hyperbolic model and ments greater the 1 mg m -3. The log-log relationship also log-log regression. Although the hyperbolic model perindicates that the error structure of the pigment retrievals forms better at pigments less than 2mgm -3, the log-log will be log normal, even if local variability in pigment is regression covers the whole range of pigment. The final normally distributed. algorithm uses the hyperbolic estimates at low pigment to The relative sensitivities shown in Fig. 8 indicate the account for the deviation from log linearity at low chloro- 412:555 and 443:555 ratios respond to chlorophylls and alphyll (see Fig. 6), and the log-log regression at pigments most as strongly to DOM, and the 490:555 and 510:555 greater than this. ratios respond principally to carotenoids, with the 490:555 ratio showing some response to chlorophylls. Figure 14 shows the relationship between total chlorophyll and total carotenoid, showing the ranges of pigment where each band ratio may be used. The carotenoid chlorophyll ratio seems to break down at total chlorophylls below 0.1 mg m -3. This effect may be due to different species assemblages in highly oligotrophic waters (possibly due to phycobiliproteins replacing carotenoids), or it may be an analytical artifact, since the total carotenoids are a sum of a greater number of components, each of which may be below the detection limits of HPLC. More data will be needed to resolve the problem, but the present data suggest some caution is required when using the 490:555 (and 510:555) ratio to determine chlorophyll in oligotrophic waters. These considerations limit the choice of final algorithm, from those suggested in the previous sections. The following conclusions can be made. 1) If algorithm switching is to be avoided, only combinations of the 490, 510, and 555 bands ment compared with R 2 values of 87.5% and 89.6% for can be used. Table 17 shows that no multiple the hyperbolic and log regressions, respectively. Although A model constrained with high chlorophyll simulated Table 21. Revised NET data pigment curve fits, where bands refer to the band:555 (band 5) ratio. Band B Al A2 R 2 412 13.14 0.019 12.48 88.6 443 9.55 0.045 8.59 89.1 490 5.29 0.112 3.48 87.5 510 3.17 0.140 1.79 84.9 Table 22. Revised NET data chlorophyll curve fits, where bands refer to the band:555 (band 5) ratio. Band B A1 A2 R 2 412 13.14 0.022 14.45 86.6 443 9.55 0.054 10.31 87.5 490 5.29 0.136 4.23 85.7 510 3.17 0.169 2.16 82.7 This combined method gives an R 2 of 90.9% for pigregression log algorithm could be derived using this is only a small improvement over the whole range, for these bands. In essence, the 490:555, 490:410, pigment concentrations less than 2 mg m -3 the hyperbolic and 510:555 contained no extra information to model gives an R 2 of 82.3% compared with an R 2 of 74.1% determine pigment. for the log regression. This improvement adds consider- 2) If algorithm switching can be implemented, then ably to the accuracy of pigment retrievals in oligotrophic Table 17 indicates the 443:490 and 443:555 ra- waters. tios can be used. It will not be necessary to use Explicitly, the algorithms for chlorophyll and pigment the 412nm band where atmospheric correction are computed as follows: 1) The log regressions are determay be a problem. mined as For the single algorithm, it is a matter of choosing between the 490:555 and 510:555 combinations. Tables 10, 26 Ca = exp 0.464- 1.w_wln|-7-----------7-;-;_ (21) [ .... ,[LwN(490))]Z_WN,OO_)

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J. Aiken,G.F.Moore,C.C.Trees,S.B.Hooker,andD.K.Clark 10 p, vs Carotenoid 0 Chlorophyll Suspect E E 0.10 + 8 0.01 i i i i 0.001 0.001 0.01 C_c [mg m-3] Fig. 14. The global variation of total chlorophyll o o o o o o o o o o o o o oo LWN (443) Suspect LWN (443) and LWN (490) Valid ,'l ........ i ........ 0.1 1 10 (Cabc) versus carotenoids (Cpp + CPs) , showing the range of validity for LWN(443) and LwN(490); the ranges where the relationship is suspect are to the right and left. 27

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TheSeaWiFSCZCS-TypePigmentAlgorithm 200 - 150 - 100 - 50- 0 - -50 - -100 - Log - Log 4k H(490:555) -150 - C, + Cp [mg m"3] ° .......o Fig. 15. Percent error in retrieval for the log-log and hyperbolic fits of Ca + Cp for the 490:555 ratio. = exp 0•696-2.0851n_ ;(22) and 2) if Ca or Ca+Cp are less than 2.0 mgm -3, the inversion of the hyperbolic model,i.e., C = (L_:j- B)/(AIB- A2L,:j), is used to calculate Cp and Ca as - 5.29 Ca = LWN (555) 0.719 - 4 23_ (23) • LWN (555) and LwN(490) _ 5.29 Ca "I- Cp = LWN (555) 0.592 - 3 48 L_N(49°)" (24) • LWN (555) It should be emphasized that the split represents a curve fitting method, rather than a split algorithm. With a more extensive high pigment data set, the hyperbolic model can be better constrained to cover the entire dynamic range of pigment• Figure 16 shows a comparison of the 490:555 ratio algorithm with the OCTS algorithm, the Clark combined ratio model, and the 443:555 ratio algorithm. The algorithm is, as expected, highly sensitive to carotenoids; it shows the least response to Gelbstoff, scattering, and detritus. Although a number of the multiband algorithms shown in previous sections show high R 2 values, they also display problems when used as inversion algorithms in that they 28 can produce negative retrievals. The final multiband algorithm that was developed avoids these problems; it is based on a correction to the 490:555 ratio algorithm shown above. By using the same band ratios as the hyperbolic multiple pigment algorithms shown in Fig. 11 (see Table 19), this algorithm can be normalized to the pigment retrievals as described below. The multiple regression algorithm for chlorophyll is: Ca = A1H(490:555) + AzH(443:555) (25) or Ca "H(443:555) - Az +A2 (26) H(490:555) H(490:555) which may be approximated by c_ - Ai H(490:555) "LwN(443)LWN(555) + A'2 ] (27) ½ = "LwN(443)LWN(555) + A_'] where the square root is an arbitrary scaling of the LWN ratio, and A1 and A2 are arbitrary constants• The use of the LWN ratio rather than the ratio of the hyperbolic pigments has the advantage of reducing noise, since only two bands are required for the final correction.

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J. Aiken, G.F. Moore, C.C. Trees, S.B. Hooker, and D.K. Clark Cpp y///////////'///'////j//////#//////////////////_ C p s 3)3)355 z/////////////////////////"//////////////////////////"////////X/'////////////////,"////////x///////////AY// Cc cb ca Y///////F///AZ////////////Y/////////A Detritus Scattering IIM"III/III_III11111111............................ y//////////M.;///////A_///////////////////MY//,'I Gelbstoff /////////////////X///////,. //////////////////////////, 0.00 0.02 0.04 H(490:555) %,\\,\\. ,\",'! H(443:555) OCTS Clark 0.06 0.080 0.10 0.12 Error in Pigment Retrieved per Unit Pigment Fig. 16. Relative error in retrieval of Ca + Cp at unit chlorophyll for the recommended 490:555 algorithm, the H(443:555) algorithm, the Ocean Color Temperature Sensor (OCTS) algorithm, and the Clark four-band algorithm at unit pigment for individual biogeochemical measures. The final algorithm adjustment is (28) c: = 1.4556_LLwN(555rLwN(443) ) 0.279 1 and C_a + C_ = 1.280[Ca + Cp] "LwN(443) LWN(555) 0.163] ½(29) The correction is applied where pigment concentration is less than 2 mgm -_, i.e., when LWN(443) is valid. This corrected pigment concentration results in an overall (0.02- 50 mg m- 3) R 2 of 91.1%, and an R 2 of 90.9% when pigment concentration is less than 2 mgm -3. The improvement is 8.6% when compared with the composite single-band algorithm. It is envisaged that when the algorithm is used for biogeochemical provinces with greater variation in pigment type, the algorithm will show a greater improvement in the explained variance. 7. CONCLUSIONS 1. Bio-optical models, show that there is a sound theoretical basis for band-ratio algorithms with explicit solutions which relate to the IOPs of water and its constituents. This indicates that the measurement of IOPs for algorithm development may be as appropriate as the measument of apparent optical properties (AOPs), e.g., R(A). 2. The BSM developed for this study has proved a powerful tool for the development of algorithms, and has the potential for further parameterization, so as to achieve closure with in situ data. 3. Biological coupling, as indexed by the major pigment groups, constrains and restricts the bio-optical variability (i.e., there is less bio-optical variability than would be expected); thus, there are sound grounds for an unbiased composite photosynthetic pigment algorithm. 4. Although globally robust, there is interprovince variability in the chlorophyll to carotenoid ratio. This variability implies that the accuracy of two band single pigment algorithms, e.g., chlorophyll a or PSC will be limited. This fact does not affect the utility of global photosynthetic pigment algorithms, however, and provides a basis for constructing high accuracy province-specific algorithms. 5. Factor analysis of band ratio combinations (all Sea- WiFS two-band combinations) demonstrates that discrete band ratios contain the same variance as similar studies using complete spectra. 29

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The SeaWiFS CZCS-Type 6. Optimal band ratio combination algorithms provide some measure of confidence that a universal algorithm can be derived (within the bounds of current data sets), i.e., both empirical methods (conclusion 1) and bio-optical models (conclusion 2) point to a similar conclusion with respect to a universal photosynthetic pigment algorithm. 7. Within the radiometric constraints of SeaWiFS, the 490:555 band combination provides the most robust retrieval of pigment or chlorophyll over five decades of pigment level. 8. With the exception of the total pigment regression (Table 19), the BSM data indicates there is no extra information to be gained by using multiband algorithms with CZCS data. The hyperbolic 443:555 and 510:555 (with ammended coefficients) algorithms can be used with CZCS data, as an alternative to log-log regression." ACKNOWLEDGMENTS The authors thank Jim Mueller of CHORS for advice and suggestions, especially for Section 4. In addition, Ray Barlow of PML, is acknowledged for supplying unpublished pigment data and comments on Section 3. GLOSSARY AOL Airborne Oceanographic Lidar AOP Apparent Optical Property BATS Bermuda Atlantic Time-Series Station BOAWG Bio-Optical Algorithm Working Group BOFS Biogeochemical Ocean Flux Study BSM Bio-Optical Synthetic Model Case- 1 Water whose reflectance is determined solely by absorption. Case-2 Water whose reflectance is significantly influenced by scattering. CZCS Coastal Zone Color Scanner DOM Dissolved Organic Matter EqPac Equatorial Pacific GIN Greenland, Iceland, and Norwegian (Seas) HPLC High Performance Liquid Chromatography IOP Inherent Optical Property IR Infrared NABE North Atlantic Bloom Experiment NASA National Aeronautics and Space Administration NEAT Northeast Atlantic NET NIMBUS Experiment Team NIMBUS Not an acronym, but a series of NASA experimental weather satellites containing a wide variety of atmosphere, ice, and ocean sensors. OCTS Ocean Color Temperature Sensor (Japan) 3O Pigment Algorithm PML Plymouth Marine Laboratory (United Kingdom) POLDER Polarization and Directionality of the Earth's Reflectances (France) PPC Photoprotectant Carotenoids PSC Photosynthetic Carotenoids SeaWiFS Sea-viewing Wide Field-of-view Sensor SPO SeaWiFS Project Office SPSWG SeaWiFS Prelaunch Science Working Group Sterna Not an acronym, but a BOFS Antarctic research project. SYMBOLS The absorption coefficient. (1 I The absorption at the Raman excitation wavelength. aa The specific absorption of chlorophyll a. aabe The specific absorption of chlorophylls a, b, and c. ab The specific absorption of chlorophyll b. ae The specific absorption of chlorophyll c. ag The DOM/detritus specific absorbance. app The specific absorption of PPC. aps The specific absorption of PSC. aw The absorption coefficient of water. as The DOM/chlorophyll combined absorbance. A, An arbitrary constant. Aj An arbitrary constant. A_ An arbitrary constant. A; An arbitrary constant. b_(,) The backscatter coefficient. bbp The particle specific backscatter coefficient (usually normalized to chlorophyll a concentration). bbw The backscatter coefficient of water. brain Scattering associated with phytoplankton (Prieur and Sathyendranath, 1981). ba The Raman scattering coefficient. B An empirical constant. Bb An empirical constant dependant on the backscatter ratio. The spectral attenuation coefficient. c(A) C Chlorophyll concentration. The concentration of chlorophyll a. Ca The concentration of chlorophylls a, b, and c. Cabc Cb The concentration of chlorophyll b. Cc The concentration of chlorophyll c. Cp Phaeopigment concentration. Cpp PPC concentration. Cps PSC concentration. Cs Simulated C. CTP Total pigment concentration. E The downwelling irradiance at the Raman excitation wavelength. Fo Extra terrestrial irradiance. Vl Pigment biomass loading factor. F2 Detritus concentration loading factor. F3 Carotenoid concentration (or relative pigment abundance) loading factor. A constant that consists of the ratios of the air-sea g interface effects, the effects of the light field, and the relative spectral variation of Q. G The concentration of DOM and DOM-like absorbers. G(#0, A) The effect of the downwelling light field.

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J. Aiken, G.F. Moore, C.C. Trees, S.B. Hooker, and D.K. Clark H(Ai:Aj) Pigment calculated from the hyperbolic transform of L_:j. k An index to two vectors of band ratios kl and ks. kl A band ratio vector. ks A band ratio vector. L,:j The ratio of normalized water-leaving radiances at wavelengths i (hi) to j (j): LWN(Ai)/LwN()j). LwN()O Normalized water-leaving radiance. L'WN Normalized water-leaving radiance at the Rarnan excitation wavelength. n The index of refraction. P The particulate concentration including detrital material. Ps Simulated Ca + Cp (q.v.). Q The ratio of upwelling irradiance to radiance, which varies with the angular distribution of the upwelling light field, and is rr for an isotropic distribution. r The air-water reflectance for diffuse irradiance. R s The regression coefficient. R(;) The irradiance reflectance at a particular wavelength. S The slope of a line. a' A power law constant. a0 A curve fitting constant. a A curve fitting constant. as A curve fitting constant. 3' A power law constant. X' Raman excitation wavelength. ,k, Wavelength of light at a particular band. ,ki Wavelength of light at a particular band. p The Fresnel reflectance at normal incidence. The Fresnel reflectance for sun and sky irradiance. REFERENCES Aiken, J., and G. Moore, 1995: Special requirements for the validation of ocean colour information. Proceedings of the WMO/IOC Conference on Space-Based Ocean Observation, WMO/PD-No. 649, 93-101. Bidigare, R.R., M.E. Ondrusek, J.H. Morrow, and D.A. Kiefer, 1990: In vivo absorption properties of algal pigments. SPIE Ocean Optics, 1302, 290-302. Bricaud, A., A. Morel, and L. Prieur, 1981: Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains. Limnol. Oceanol., 26, 45-53. Carder, K.L., R.G. Steward, J.H. Paul, and G.A. Vargo, 1986: Relationships between chlorophyll and ocean color constituents as they affect remote-sensing reflectance models. Limnol. Oceanogr., 31,403-413. , R.G. Steward, G.R. Harvey, and P.B. Ortner, 1989: Marine humic and fulvic acids: Their effects on remote sensing of ocean chlorophyll. Limnol. Oceanogr., 34, 38-81. Clark, D.K., 1981: Phytoplankton pigment algorithms for the Nimbus-7 CZCS. Oceanography from Space, J.F.R. Gower, Ed., Plenum Press, 227-238. Duysens, L.N.M., 1956: The flattening of the absorption spectrum of suspensions as compared with that of solutions. Biochim. Biophys. Acta., 19, 255, 257, 261. Garver, S.A., D.A. Siegel, and B.G. Mitchell, 1995: Variability in near surface particulate absorption spectra: What can a satellite imager see? Limnol. Oceanogr., 39, 1,349-1,367. Gordon, H.R., O.B. Brown, R.H. Evans, J.W. Brown, R.C. Smith, K.S. Baker, and D.K. Clark, 1988: A semianalytic radiance model of ocean color. J. of Geophys. Res., 93, 10,909-10,924. Hoepffner, N., and S. Sathendranath, 1992: Bio-optical characteristics of coastal waters: Absorption spectra of phytoplankton and pigment distribution in the Western North Atlantic. Limnol. Oeeanogr., 37, 1,660-1,679. Hoepffner, N., and S. Sathyendranath, 1993: Determination of the major g'oups of phytoplankton pigments from the absorption spectra of total particulate matter. J. Geophys. Res., 98, 22,789-22,803. Hoge, F.E., and R.E. Swift, 1993: The influence of chlorophyll pigment upon the upwelling spectral radiances from the North Atlantic Ocean. Deep-Sea Res., 40, 265-278. Holligan, P.M., E. Fernandez, J. Aiken, W.B. Balch, P. Boyd, P.H. Burkill, M. Finch, S.B. Groom, G. Malin, K. Muller, D.A. Purdie, and C. Robinson, 1993: A biogeochemical study of Emiliania huxleyi, in the North Atlantic. Global Biogeochemical Cycles, 7, 879-900. , G. Moore, and P. Holligan, 1992: Remote sensing of Hooker, S.B., C.R. McClain, and A. Holmes, 1993: Ocean color oceanographic biology in relation to global climate change, J. Phycol., 28, 579--590. Barnes, R.A., A.W. Holmes, W.L. Barnes, W.E. Esaias, C.R. McClain, and T. Svitek, 1994: SeaWiFS Prelaunch Radiometric Calibration and Spectral Characterization. NASA Tech. Memo. 104566, Vol. 23, S.B. Hooker, E.R. Firestone, and J.G. Acker, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 55 pp. imaging: CZCS to SeaWiFS. Marine. Tech. Soc. J., 2'/, 3- 15. Mantoura, R.F.C., and C.A. Llewellyn, 1983: The rapid determination of algal chlorophyll and carotenoid pigments and their breakdown products in natural waters by reversephase high-performance liquid chromatography. Anal. Chim. Acta., 151,297-314. Marshall, B.R., and R.C. Smith, 1990: Raman scattering and in-water ocean optical properties. Appl. Opt., 29_ 71-84. 31

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TheSeaWiFSCZCS-TypePigmentAlgorithm Mitchell,B.G.,andO. Holm-Hansen,1991:Bio-opticalpropertiesofAntarcticPeninsulawaters:differentiationfrom temperateoceanmodels.Deep-Sea Res., 39, 1,009-1,028. Morel, A., 1974: Optical properties of pure water and sea water. Optical Aspects of Oceanography, N.G. Jerlov and S. Nielsen, Eds., Academic Press, 1-24. --, and L. Prieur, 1977: Analysis of variation in ocean colour. Limnol. Oceanogr., 22, 709-722. --, and B. Gentili, 1991: Diffuse reflectance of oceanic waters. I. Its dependence on sun angle as influenced by the molecular scattering contribution. Appl. Opt., 30, 4,427- 4,438. --, and B. Gentili, 1993: Diffuse reflectance of oceanic waters. II. Bidirectional aspects. Appl. Opt., 32_ 6,864-6,879. Mueller, J.L., 1976: Ocean color spectra measured off the Oregon coast: characteristic vectors. Appl. Opt., 15, 394- 402. , and R.W. Austin, 1995: Ocean Optics Protocols for Sea- WiFS Validation, Revision 1. NASA Tech. Memo. 104566, Vol. 25, S.B. Hooker, E.R. Firestone, and J.G. Acker, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 66 pp. Petzold, T.J., 1972: Volume scattering functions for selected ocean waters. Scripps Inst. Oceanogr. Rep. 75-78, 79 pp. Prieur, L., and S. Sathyendranath, 1981: An optical classification of coastal and oceanic waters based on the specific spectral absorption curves of phytoplankton pigments, dissolved organic matter, and other particulate materials. Limnol. Oceanogr., 26, 671-689. Sathyendranath, S., 1981: Influence des substances en solution et en suspension dans les eaux de mer sur l'absorption et la Vol. 6 reflectance. Modelisation et application a la teledetection. Ph.D. Thesis, University of Paris, 123 pp (in French). Smith, R.C., and K.S. Baker, 1981: Optical properties of the clearest natural waters (200-800nm). Appl. Opt., 20, 177- 184. Strickland, J.D.H., and T.R. Parsons, 1972: A Practical Handbook o] Sea Water Analysis. Fish. Res. Board. Canada, 310pp. Sullivan, C.W., K.R. Arrigo, C.R. McClain, J.C. Comiso, and J.K. Firestone, 1993: Distributions of phytoplankton blooms in the Southern Ocean. Science, 262_ 1,832-1,837. Trees, C.C., M.C. Kenicutt, and J.M. Brooks, 1985: Errors associated with the standard fluorometric determination of chlorophylls and phaeopigments. Mar. Chem., 17, 1-12. , D.K. Clark, R. Bidigare, and M. Ondrusek, 1995: Chlorophyll a versus accessory pigment concentrations within the euphotic zone: A ubiquitous relationship? Science, (submitted). Yentsch, C.S., and D.W. Menzel, 1963: A method for determination of phytoplankton, chlorophyll and phaeophytin by fluorescence. Deep-Sea Res., 10, 221-231. 32 THE SEAWIFS TECHNICAL REPORT SERIES Vol. 1 Hooker, S.B., W.E. Esaias, G.C. Feldman, W.W. Gregg, and C.R. McClain, 1992: An Overview of SeaWiFS and Ocean Color. NASA Tech. Memo. 104566, Vol. 1, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 24 pp., plus color plates. Vol. 2 Gregg, W.W., 1992: Analysis of Orbit Selection for SeaWiFS: Ascending vs. Descending Node. NASA Tech. Memo. 104566, Vol. 2, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 16 pp. Vol. 3 McClain, C.R., W.E. Esalas, W. Barnes, B. Guenther, D. Endres, S. Hooker, G. Mitchell, and R. Barnes, 1992: Calibration and Validation Plan for SeaWiFS. NASA Tech. Memo. 104566, Vol. 3, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 41 pp. Vol. 4 McClain, C.R., E. Yeh, and G. Fu, 1992: An Analysis of GAC Sampling Algorithms: A Case Study. NASA Tech. Memo. 104566, Vol. 4, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 22 pp., plus color plates. Vol. 5 Mueller, J.L., and R.W. Austin, 1992: Ocean Optics Protocols for SeaWiFS Validation. NASA Tech. Memo. I0,_566, Vol. 5, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 43 pp. Firestone, E.R., and S.B. Hooker, 1992: SeaWiFS Technical Report Series Summary Index: Volumes 1-5. NASA Tech. Memo. 104566, Vol, 6, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 9 pp. Vol. 7 Darzi, M., 1992: Cloud Screening for Polar Orbiting Visible and IR Satellite Sensors. NASA Tech. Memo. 104566, Vol. 7, S.B. Hooker and E.R Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 7 pp. I/ol. 8 Hooker, S.B., W.E. Esaias, and L.A. Rexrode, 1993: Proceedings of the First SeaWiFS Science Team Meeting. NASA Tech. Memo. 104566, Vol. 8, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 61 pp. Vol. 9 Gregg, W.W., F.C. Chen, A.L. Mezaache, J.D. Chen, J.A. Whiting, 1993: The Simulated SeaWiFS Data Set, Version 1. NASA Tech. Memo. 104566, Vol. 9, S.B. Hooker, E.R. Firestone, and A.W. Indest, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 17 pp.

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J. Aiken,G.F.Moore,C.C.Trees,S.B.Hooker,andD.K.Clark Vol. 10 Woodward, R.H., R.A. Barnes, C.R. McClain, W.E. Esaias, W.L. Barnes, and A.T. Mecherikunnel, 1993: Modeling of the SeaWiFS Solar and Lunar Observations. NASA Tech. Memo. 104566, Vol. 10, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 26pp. Vol. 11 Patt, F.S., C.M. Hoisington, W.W. Gregg, and P.L. Coronado, 1993: Analysis of Selected Orbit Propagation Models for the SeaWiFS Mission. NASA Tech. Memo. 104566, Vol. 11, S.B. Hooker, E.R. Firestone, and A.W. Indest, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 16 pp. Vol. 12 Firestone, E.R., and S.B. Hooker, 1993: SeaWiFS Technical Report Series Summary Index: Volumes 1-11. NASA Tech. Memo. 104566, Vol. 12, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 28 pp. Vol. 13 McClain, C.R., K.R. Arrigo, J. Comiso, R. Fraser, M. Darzi, J.K. Firestone, B. Schieber, E-n. Yeh, and C.W. Sullivan, 1994: Case Studies for SeaWiFS Calibration and Validation, Part 1. NASA Tech. Memo. 104566, Vol. 13, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 52pp., plus color plates. Vol. 14 Mueller, J.L., 1993: The First SeaWiFS Intercalibration Round- Robin Experiment, SIRREX-1, July 1992. NASA Tech. Memo. 104566, Vol. 14, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 60 pp. Vol. 15 Gregg, W.W., F.S. Patt, and R.H. Woodward, 1994: The Simulated SeaWiFS Data Set, Version 2. NASA Tech. Memo. 104566, Vol. 15, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 42 pp., plus color plates. Vol. 16 Mueller, J.L., B.C. Johnson, C.L. Cromer, J.W. Cooper, J.T. McLean, S.B. Hooker, and T.L. Westphal, 1994: The Second SeaWiFS Intercalibration Round-Robin Experiment, SIRREX-2, June 1993. NASA Tech. Memo. 104566, Vol. 16, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 121 pp. Vol. 17 Abbott, M.R., O.B. Brown, H.R. Gordon, K.L. Carder, R.E. Evans, F.E. Muller-Karger, and W.E. Esaias, 1994: Ocean Color in the 21st Century: A Strategy for a 20-Year Time Series. NASA Tech. Memo. 104566, Vol. 17, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 20 pp. Vol. 18 Firestone, E.R., and S.B. Hooker, 1995: SeaWiFS Technical Report Series Summary Index: Volumes 1-17. NASA Tech. Memo. 104566, Vol. 18, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 47 pp. Vol. 19 McClain, C.R., R.S. Fraser, 3.T. McLean, M. Darzi, J.K. Firestone, F.S. Patt, B.D. Schieber, R.H. Woodward, E-n. Yeh, S. Mattoo, S.F. Biggar, P.N. Slater, K.J. Thome, A.W. Holmes, R.A. Barnes, and K.J. Voss, 1994: Case Studies for SeaWiFS Calibration and Validation, Part 2. NASA Tech. Memo. 104566, Vol. 19, S.B. Hooker, E.R. Firestone, and J.G. Acker, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 73 pp. Vol. 20 Hooker, S.B., C.R. McClaln, J.K. Firestone, T.L. Westphal, E-n. Yeh, and Y. Ge, 1994: The SeaWiFS Bio-Optical Archive and Storage System (SeaBASS), Part 1. NASA Tech. Memo. 104566, Vol. 20, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 40 pp. Vol. 21 Acker, 3.G., 1994: The Heritage of SeaWiFS: A Retrospective on the CZCS NIMBUS Experiment Team (NET) Program. NASA Tech. Memo. 104566, Vol. 21, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 43 pp. Vol. 22 Barnes, R.A., W.L. Barnes, W.E. Esaias, and C.R. McClain, 1994: Prelaunch Acceptance Report for the SeaWiFS Radiometer. NASA Tech. Memo. 104566, Vol. 22, S.B. Hooker, E.R. Firestone, and J.G. Acker, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 32 pp. Vol. 23 Barnes, R.A., A.W. Holmes, W.L. Barnes, W.E. Esaias, C.R. McClain, and T. Svitek, 1994: SeaWiFS Prelaunch Radiometric Calibration and Spectral Characterization. NASA Tech. Memo. 104566, Vol. 23, S.B. Hooker, E.R. Firestone, and J.G. Acker, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 55 pp. Vol. 24 Firestone, E.R., and S.B. Hooker, 1995: SeaWiFS Technical Report Series Summary Index: Volumes 1-23. NASA Tech. Memo. 104566, Vol. 24, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 36 pp. Vol. 25 Mueller, J.L., and R.W. Austin, 1995: Ocean Optics Protocols for SeaWiFS Validation, Revision 1. NASA Tech. Memo. 104566, Vol. 25, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 66 pp. Vol. 26 Siegel, D.A., M.C. O'Brien, J.C. Sorensen, D.A. Konnoff, E.A. Brody, J.L. Mueller, C.O. Davis, W.J. Rhea, and S.B. Hooker, 1995: Results of the SeaWiFS Data Analysis Round-Robin (DARR), July 1994. NASA Tech. Memo. 104566, Vol. 26, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 58 pp. Vol. 27 J.L. Mueller, R.S. Fraser, S.F. Biggar, K.J. Thome, P.N. Slater, A.W. Holmes, R.A. Barnes, C.T. Weir, D.A. Siegel, D.W. Menzies, A.F. Michae|s, and G. Podesta, 1995: Case Studies for SeaWiFS Calibration and Validation, Part 3. NASA Tech. Memo. 104566, Vol. 27, S.B. Hooker, E.R. Firestone, and J.G. Acker, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 46 pp. 33

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TheSeaWiFSCZCS-TypePigmentAlgorithm McClain, C.R., K.R. Arrigo, W.E. Esaias, M. Darzi, F.S. Patt, R.H. Evans, J.W. Brown, C.W. Brown, R.A. Barnes, and L. Kumar, 1995: SeaWiFS Algorithms, Part 1. NASA Tech. Memo. 104566, Vol. 2,8, S.B. Hooker, E.R. Firestone, and J.G. Acker, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 38 pp., plus color plates. 34 Vol. 29 Aiken, J., G.F. Moore, C.C. Trees, S.B. Hooker, and D.K. Clark, 1995: The SeaWiFS CZCS-Type Pigment Algorithm. NASA Tech. Memo. 10,_566, Vol. 29, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 34 pp.

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Form Approved REPORT DOCUMENTATION PAGE OMB No. 0704-0188 Public repoding burden for this collection ot information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, end completing and reviewing the collection of information Send comments regarding this burden estimate or any other aspect of this collection of infermation, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Informatio_ Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlin_lton, VA 22202-4302, and to the Office of Manacdement and Budcdet, Paperwork Reduction Proiect I0704-01881, Washin_lton, DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE June 1995 4. TITLE AND SUBTITLE SeaWiFS Technical Report Series Volume 29-The SeaWiFS CZCS-Type Pigment Algorithm 6. AUTHOR(S) 3. REPORT TYPE AND DATES COVERED Technical Memorandum 5. FUNDING NUMBERS Code 970.2 James Aiken, Gerald F. Moore, Charles C. Trees, Stanford B. Hooker, and Dennis K. Clark Series Editors: Stanford B. Hooker and Elaine R. Firestone 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Laboratory for Hydrospheric Processes Goddard Space Flight Center Greenbelt, Maryland 20771 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) National Aeronautics and Space Administration Washington, D.C. 20546-0001 8. PERFORMING ORGANIZATION REPORT NUMBER 95B00(6 10. SPONSORING/MONITORING AGENCY REPORT NUMBER TM- 104566, Vol. 29 11. SUPPLEMENTARY NOTES Elaine R. Firestone: General Sciences Corporation, Laurel, Maryland; James Aiken and Gerald Moore: Plymouth Marine Laboratory, Plymouth, United Kingdom; Charles C. Trees: San Diego State University, San Diego, California; Dennis K. Clark: NOAA/National Environmental Satellite Data Information Service, Camp Springs, Maryland 12a. DISTRIBUTION/AVAILABILITY STATEMENT Unclassified-Unlimited Subject Category 48 12b. DIsiRiBUTION CODE Report is available from the Center for AeroSpace Information (CASI), 800 Elkridge Landing Road, Linthicum Heights, MD 21090; (301)621-0390 13. ABSTRACT (Ma 200 rds) The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission will provide operational ocean color that will be superior to the previous Coastal Zone Color Sensor (CZCS) proof-of-concept mission. An algorithm is needed that exploits the full functionality of SeaWiFS whilst remaining compatible in concept with algorithms used for the CZCS. This document describes the theoretical rationale of radiance bandratio methods for determining chlorophyll a and other important biogeochemical parameters, and their implementation for the SeaWIFS mission. Pigment interrelationships are examined to explain the success of the CZCS algorithms. In the context where chlorophyll a absorbs only weakly at 520 nm, the success of the 520 nm to 550 nm CZCS band ratio needs to be explained. This is explained by showing that in pigment data from a range of oceanic provinces chlorophyll a (absorbing at less than 490 nm), carotenoids (absorbing at greater than 460 nm), and total pigment are highly correlated. Correlations within pigment groups particularly photoprotectant and photosynthetic earotenoids are less robust. The sources of variability in optical data are examined using the NIMBUS Experiment Team (NET) bio-optical data set and bio-optical model. In both the model and NET data, the majority of the variance in the optical data is attributed to variability in pigment (chlorophyll a, and total particulates, with less than 5% of the variability resulting from pigment assemblage. The relationships between band ratios and chlorophyll is examined analytically, and a new formulation based on a dual hyperbolic model is suggested which gives a better calibration curve than the conventional log-log linear regression fit. The new calibration curve shows the 490:555 ratio is the best single-band ratio and is the recommended CZCS-type pigment algorithm. Using both the model and NET data, a number of multiband algorithms are developed; the best of which is an algorithm based on the 443:555 and 490:555 ratios. From model data, the form of potential algorithms for other products, such as total particulates and dissolved organic matter (DOM), are suggested. 14. SUBdECT TERMS 15. NUMBER OF PAGES 34 SeaWiFS, Oceanography, Algorithms, Pigments, CZCS, Band-Ratio, Bio-Optical Models Dam Analyses, NET Data 17. llECtJRITY Ct.AINIBqCAllON l& SECURITY Ct.ASSlFICATION OF REPORT OF THIS PAGE Unclassified Unclassified NRN -2BO4B) 16. PRICE CODE 19. SECIbmTT TION 20. LIMITA'i-JNOF A_TT_I_CT OF ABSTRACT Unclassified Unlimited S_naard Form 2118 (,'. 2-,_)

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National Aeronautics and Space Administration Goddard Space Flight Center Greenbelt, Maryland 20771 ofrcial Business Penalty for Privato Use, $300 SPECIAL FOU_ RATE POSTAGE & FEES PAID NASA PERMIT No. G27 POSTMASTER: If Undeliverable (Section 158, Portal Manual) Do Not Fletum