VI. Remote sensing

ICES mar. Sei. Symp., 197: 236-243. 1993

Remote sensing of water-column primary production

Shubha Sathyendranath and Trevor Platt

Sathyendranath, S., and Platt. T. 1993. Remote sensing of water-column primary production. - ICES mar. Sei. Symp., 197: 236-243.

Satellite observations of ocean colour at selected wavelengths have made it possible to map near-surface distribution of phytoplankton pigments at the global scale. While the advantage of remote sensing in providing synoptic coverage of large-scale surface features is incontestable, the estimation of primary production from these data requires additional information inaccessible to present-day satellite remote sensing, such as the parameters for conversion of biomass to growth rates, and the parameters describing the vertical structure of biomass. The value of remote sensing would therefore be enhanced considerably if the satellite data could be combined with in situ data to provide the missing information. Since satellite and in situ data are collected at very different time and space scales, conceptual schemes are necessary to render the two data sets compatible. The idea of bio-geochemical provinces has proved to be very useful in this context. Both empirical and analytic approaches have been used to address the problem of estimating primary production from satellite-derived biomass estimates. The various analytic models that have been proposed can be classified according to their level of complexity. In any application, a suitable model has to be selected, based on: (1) validity of the model assumptions in the particular context, (2) computational requirements, (3) availability of auxiliary data, and (4) acceptable levels of error.

Shubha Sathyendranath: Department of Oceanography, Dalhousie University, Halifax, Nova Scotia, Canada B3H 4J1. Trevor Platt: Biological Oceanography Division, Bedford Institute of Oceanography, Box 1006, Dartmouth, Nova Scotia, Canada B2Y4A2.

Introduction Zone Color Scanner (CZCS), the first satellite to be launched to monitor ocean colour from space, was Remote sensing of primary production is a science in its operational from November 1978 to May 1986, and infancy: it has a very short history, and it is in a state of proved the feasibility of monitoring chlorophyll distri­ rapid growth. A review of the field today is likely to be butions from space (Gordon and Morel, 1983). Even as soon outmoded. Nevertheless, a clear philosophy of algorithms for chlorophyll retrieval from ocean colour approach has been emerging in this area, which will were being developed in the early 1980s, biological probably have a longer life span than many of the details oceanographers were quick to realize that empirical regarding its implementation. In this paper, therefore, relationships which often exist between surface biomass we will try to outline the philosophy, which will in turn and water-column primary production could be suggest a basic methodology. We will then examine the exploited to estimate oceanic production by remote progress to date on the implementation of various sensing (Smith et al., 1982; Platt and Herman, 1983; aspects of the methodology. Eppley et al. ,1985). The latter half of the last decade saw In the late 1960s and the 1970s, it was shown that further developments in this area, with a number of changes in ocean colour, observed from aircraft or from analytical or semi-analytical models being proposed. In ship, are related to variations in phytoplankton pigment any field that is in a state of rapid development, analyti­ concentrations (Morel and Prieur, 1977). The Coastal cal models are to be preferred over purely empirical ICES mar. Sei. Symp., 197 (1993) Remote sensing of water-column primary production 237 ones, since the former give better insights into problems The principles as outlined above suggest a basic when models fail to perform well. Besides, with analyti­ methodology that would consist of the following steps: cal models, it is easier to make intelligent extrapolations of results from one region to another. Analytical and (1) Compute the light available at the sea surface and semi-analytical models are therefore the focus of this account for losses at the air-sea interface. paper. (2) Estimate biomass at the surface. (3) Define the biomass profile. This calls for a pro­ cedure for extrapolating from the surface to the base Basic approach and methodology of the productive zone (say the photic zone, or the depth within which the light is reduced to 1% of its The analytical models suggest that remote sensing of surface value). primary production can be posed as a problem in classi­ (4) Provide estimates of parameters of the photo­ cal plant physiology: the study of the photosynthetic synthesis-light model. response of plants to available light. According to (5) Compute parameters of light transmission under­ photosynthesis-light models, primary productivity can water. The most crucial parameter is K, the diffuse be expressed as a function of biomass (B) and available attenuation coefficient for downwelling light. light (I). Of all the indices that may be chosen to indicate (6) Compute primary production of the water column phytoplankton biomass, chlorophyll concentration has (or mixed layer) using photosynthesis-light models. been a preferred one, because of the central role it plays in the photosynthetic processes, and because of the Of these six steps in calculation, remote sensing can relative ease with which it can be measured. It also provide useful information on steps 1 and 2, while steps 3 happens to be the biological variable that is most easily and 4 clearly have to come from in situ observations. The monitored from space. The light available at the sea distribution of biomass, from steps 2 and 3, is a necessary surface can be computed from atmospheric transmission input to step 5, the computation of underwater light models, and satellite data have proved useful in this area transmission. Obviously, the accuracy of the computed as well. Models are also available now that compute light primary production (step 6) would depend on the accu­ available at depth in the sea, given information on racy of the first five steps, in addition to the validity or chlorophyll concentration. Thus, current satellite tech­ suitability of the model itself that is selected for comput­ niques and optical models can provide information on ing primary production. While recent years have seen the two most important variables necessary for compu­ considerable progress in all of the six steps outlined tation of primary production: biomass and light. How­ above, the quest for the best solution is by no means ever, no current remote-sensing technique provides in­ over. In the following sections, we examine the progress formation on the rate constants that are essential to fix so far, point out the pitfalls, and explore possible av­ the functional relationship between light and photo­ enues for further improvement. Since the inputs synthesis. Fortunately, in nature, these rate constants required from steps 1 to 5 would depend to some extent vary over a much smaller dynamic range than either on the choice of model in step 6, we examine the chlorophyll or light, and so they may be treated as quasi­ photosynthesis-light models first. stable parameters. From these considerations emerges a protocol for the estimation of remote sensing from satellites: use satellite technology and optical models to Light dependence of photosynthesis provide information on the rapidly changing variables (biomass, light), and supplement them with information In the remote-sensing context, we recognize that the on the more stable parameters of the photosynthesis- need is to arrive at estimates over large scales, in both light models from ship-borne observations. space and time; and therefore the emphasis here is on Satellites provide information only on the near­ arriving at estimates of daily primary production inte­ surface layer of the water column. Typically, this layer is grated over the water column (or the upper, dynamically about one-fifth of the productive part of the water mixed layer). column. This introduces the additional requirement that The production P(z,t) at depth z and time t may be the vertical structure of the productive zone (whether it expressed as: be with regard to biomass distribution or with regard to change in the photosynthetic parameters with depth) be P(z,t) = PB(z,t)B(z) (1) defined based on in situ observations as well. Note that this problem would not be significant in calculations of where B(z) is the biomass and PB(z,t) is the rate of primary production for the mixed layer, but it cannot be primary production per unit biomass. Light is the driving ignored in calculations for the entire water column. force for photosynthesis, and so specific productivity 238 S. Sathyendranath and T. Platt ICES mar. Sei. Symp.. 197 (1993)

PB(z,t) may be expressed as a function p of available point of view, it has been shown by Platt etal. (1977) that light I(z,t): most of these equations yield similar results for water- column integrals of primary production, which would PB(z,t) = p(I(z,t)) (2) suggest that the choice of equation for p(I) is not a crucial one: there are many good ones to choose from, To obtain daily, water-column primary production P7 T, and one may be guided here entirely by convenience. we have to integrate P(z,t) over all depths and over all daylight hours: Non-spectral, uniform-biomass models All P-I equations have the common feature that I and a B P 7 T — B(z)p(I(z,t)) dz dt. (3) always appear as products. The initial slope « B and the light transmission under water (and therefore I ) are Note that we have assumed here that B is independent of both wavelength selective. If such wavelength depen­ time for the period of one day over which the compu­ dencies are ignored, we can define a new quantity tations are to be carried out - a constraint which can Il(z,t), given by: easily be relaxed if required. Photosynthesis models take various forms depending Il(z,t) = I(z,t)aB. (5) on how the function p and the underwater light field I(z,t) are specified. The function p, which describes the Note that II has the same dimensions as PB, and photosynthetic response of phytopiankton to light, is Equation (4) becomes: generally referred to as the photosynthesis-light curve, or the P-I curve. pB(z,t) = P(n(z,t),pB ) (6) It is well known that at low light levels, photosynthesis increases linearly with available light (linear response For the non-spectral case, Platt et al. (1990) have phase), while at high light levels, photosynthetic rate shown that Equation (3) has an analytical solution, for a becomes independent of the light level (light saturation uniform water column (biomass is independent of phase), such that there is no increase in production with depth; B(z) = a constant), provided that the light at the increase in light. There may be yet a third phase at sea surface varies as a sine function with time (1(0,t) = extremely high light levels, when photosynthesis begins 1(7 sin (jrt/D), where I™ is the maximum irradiance at to decrease with increase in light level (photoinhibition local noon and D is the daylength). They further showed phase). that the solution may be expressed using a simple poly­ We recognize the need for at least one parameter to nomial approximation: describe each one of these three phases. The following three are commonly used: the initial slope a B of the P-I P z .t = A 2 5|=1 n,(im)j (7) curve at very low light levels, the asymptotic maximum PB attained at high light levels, and ßB, the negative where A = BPbD/(jtK), and Im = I™/Ik, with Ik = PB/ slope of the P-I curve when photoinhibition occurs. a B. Note that Inl is dimensionless, and so all the dimen­ While photoinhibition is known to occur sometimes near sions of the result are carried by the scale factor A. Platt the surface, especially in tropical waters, it has not yet et al. (1990) also showed that their analytical solution is been shown that it influences significantly the compu­ very close to the semi-analytical solution proposed by tations of water-column production. On the other hand, Tailing (1957), when Im 2= 4. Evans and Parslow (1985) there is some evidence that the opposite may be true have also given an analytical solution to daily water- (Platt etal., 1990). Therefore, the photoinhibition phase column production, assuming a triangular function to is not discussed further in this paper. But this again is a describe the time dependence of light at the sea surface. restriction that is easily removed if necessary (Platt etal., We are not aware of any other solution to Equation (3) 1990). The requirement from step 4 would therefore be that does not require additional simplifying assumptions information on «B and PB. (e.g. that of Rodhe (1966), which is derived from The equation for the P-I curve (Equation (2)) may Tailing’s solution, with the assumption that Im is a then be rewritten as: constant equal to 5; that of Ryther (1956), which assumes a constant value for Ik; or that of Platt (1986), PB(z,t) = p(I(z,t); a B(z), PB (z)) (4) which is a linear approximation to the curvilinear func­ tion represented by Equation (7)). Many equations have been proposed to represent the Given the simplicity of Equation (7), it appears need­ function p, with varying degrees of analytical expla­ less to impose additional approximations, unless it is due nations to support them. However, from a practical to insufficient knowledge of the parameters a B and PB. ICES mar. Sei. Symp., 197 (1993) Remote sensing of water-column primary production 239

Note that the equation of Rodhe (1966) can be imple­ spectrum routinely at sea (Lewis et al., 1985), and we mented without knowledge of a B, while the approach of probably have much to learn yet about its variability in Platt (1986) can be implemented without knowledge of the natural environment. Values of a B from white-light either a B or PB, provided that the empirical linear experiments can be used in spectral models if we assume relationship between PZ T and 1(0) has been established that the variations in the shape of the action spectrum for the region. Both, however, should be recognized for are unimportant, compared to the possible variations in the approximations that they are, and the results inter­ the magnitude at each wavelength. We find this to be a preted accordingly. useful approximation (Sathyendranath et al., 1989).

Spectral models Non-uniform biomass The models discussed so far are non-spectral; i.e. they Yet another problem, not accounted for in the models ignore the spectral effects in light transmission, and the discussed above, is the effect of non-uniformity in the spectral dependence of a B. However, such effects are a vertical structure of biomass. A first step in developing reality, and spectral models are to be preferred over models that will account for the effect of this non­ non-spectral ones. The incorporation of spectral effects uniformity on daily, water-column primary production requires the function II (Equation (5)) to be redefined is to parameterize the vertical pigment profile (step 3). One would require a minimum of four parameters to define a chlorophyll profile characterized by a surface, n(z,t) = I(A,z,t)a (A)d/l (8) or subsurface peak: one parameter to fix the depth of occurrence of chlorophyll maximum; one to define the where A is the wavelength, and the integration is carried width of the peak, one to define the relative importance out over the photosynthetically active region (about of the peak with respect to the background, and yet 400-700 nm). Equation (8) can also be modified to another to fix the absolute magnitude. Platt etal. (1988) include effect of angular distribution of light under water proposed a Gaussian curve, superimposed on a constant (Sathyendranath and Platt, 1989a). Once II has been background, to parameterize B(z): redefined, then Pz T can be computed, for the spectral case, using the same formulation of the P-I curve as for B(z) = B0 -I- (h/(oV2jr)) exp [-(z — zm)2/2<72] (9) the non-spectral case (Equation (6)). No analytical solu­ tions are available for the spectral models of daily water- where B() is the background biomass, zm is the depth of column primary production (Equation (3)), and so the the chlorophyll maximum, h/(aV br) is the height of the solutions have to be obtained by numerical integration peak, and the width of the peak is determined by o. They (Platt and Sathyendranath, 1988; Sathyendranath and showed that this parameterization is able to reproduce Platt, 1989a; Platt et al., 1991). This can be time- faithfully many types of observed profiles. This para­ consuming, even with modern computers, if the compu­ meterization has also been adopted by Morel and tations are to be repeated for every pixel in a satellite Berthon (1989). Mueller and Lange (1989), on the other image. It is therefore worthwhile to examine means of hand, have proposed a triangular parameterization of achieving economy of computation. One approach, pro­ the subsurface peak, and shown it to be very successful posed by Platt and Sathyendranath (1991), is to apply a in representing data from the Pacific. Neither the Gaus­ correction factor to results from the non-spectral model. sian nor the triangular parameterization allows for The correction factors would be established based on a asymmetry in the vertical distribution. To describe such systematic comparison of results of the spectral and non­ distributions, one would have to use functions with more spectral models, for the range of parameters and vari­ parameters, like the five-parameter function used by Li ables of interest. Another possibility, suggested by Platt and Wood (1988). No analytical solutions have yet been et al. (1991), is to adjust the value of K in the non­ proposed for computing water-column primary pro­ spectral model, such that the answers from spectral and duction for cases of non-uniform biomass, either for the non-spectral models give similar results (see also Kye- triangular or the Gaussian parameterization. Therefore, walyanga et al., 1992). in the case of non-uniform profiles also the solution to Note that implementation of the spectral model also Equation (3) has to be obtained by numerical integra­ requires additional information from step (4). It would tion, whether the model used is spectral or non-spectral. no longer be sufficient to have an estimate of a B for In a series of papers, we have shown (Platt et al., 1988; white light as in the case of non-spectral models; we Sathyendranath et al., 1989; Platt et al., 1991) that would now require the action spectrum (a description of ignoring the deep chlorophyll maximum in compu­ a B as a function of wavelength). It is only since the last tations of primary production can often lead to signifi­ decade that it has become possible to measure the action cant errors. Non-uniformity in vertical structure is 240 S. Sathyendranath and T. Platt ICES mar. Sei. Symp., 197 (1993) therefore well worth accounting for, whenever the point of view of primary production studies, it is the necessary information is available. photosynthetically active radiation (PAR, from about As noted earlier, the satellite signal does not pene­ 400 to 700 nm) that is of interest. For application of trate very deep (the depth of penetration of the satellite spectral models, one would require spectral decomposi­ signal is estimated to be about 20% of the photic zone, tion within that range as well. Some recent papers have according to Gordon and McCluney, 1975). The satellite begun to address this problem (Pinker and Laszlo, data will therefore have to be combined with in situ 1990). Once we have information on the light reaching information on vertical structure. Morel and Berthon the sea surface, it is a relatively easy matter to correct for (1989) have suggested that the shape of the pigment losses due to reflection at the air-sea interface using profile is related to the surface biomass, and so the Fresnel’s Law. For low solar elevations, this correction complete vertical profile can be recovered from the should include wind effect on sea-surface roughness satellite-derived biomass. Sathyendranath and Platt (Gregg and Carder, 1990). (1989b) have suggested that, since vertical structure in Given the surface light field, the flux at any depth z in biomass is a slowly varying property, both in space and the ocean can be computed, given the attenuation coef­ time, information on the shape of the biomass can be ficient for downwelling light, K (step 5). Phytoplankton built into regional biomass algorithms. They use a semi- biomass is considered the most important factor respon­ analytical biomass algorithm to demonstrate the sible for changes in the optical properties of sea water, at approach. They have also pointed out some of the least in the open ocean, and, therefore, satellite data problems that exist with such approaches, which arise contain information on the parameters of light trans­ from mismatch between theoretical predictions of ocean mission also. The parameter K can be computed either colour and the empirical algorithms currently in use. We directly from satellite data (Austin and Petzold, 1981) or hope that the planned launch of the next satellite for indirectly, using the biomass data (Smith and Baker, ocean colour measurements (the SeaWiFS, scheduled to 1978a, b; Sathyendranath and Platt, 1988; Morel, 1988). be launched in August 1993) will give a new impetus to Note that, when using spectral models of primary pro­ research on ocean colour, and that we will soon see duction, K also has to be established as a function of improvements in our understanding of factors that influ­ wavelength; and when using non-uniform biomass pro­ ence ocean colour, and consequently, in biomass algor­ files, the K values will vary with depth as well. It must be ithms. pointed out that, for computation of light transmission, knowledge of the vertical distribution of biomass (from steps 2 and 3) is a minimum requirement. To account for Underwater light field what might be considered second-order effects, any additional information on the optical properties of sub­ In the remote-sensing context, primary production stances present in the water column would be useful. models cannot rely on in situ measurements of the light Such information would include the pigment compo­ field; so one has to rely on models to estimate the light sition and concentrations of yellow substances and par­ available at the sea surface (step 1). The light reaching ticulate material other than phytoplankton. The CZCS the surface can be computed, for clear-sky conditions, does not provide such information, but this situation given latitude, day number, and local time. Information should improve with the next generation of ocean- on aerosol optical thickness and ozone concentration, colour satellites, like the SeaWiFS. required in these calculations, could come from satellite data. A choice of spectral, clear-sky models has appeared in the last decade (Bird, 1984; Bird and Rior- Bio-geochemical provinces dan, 1986; Tanré et al., 1979), and Gregg and Carder (1990) have adapted the model of Bird (1984) for appli­ A given model of primary production, and its applica­ cation in oceanography. Correction for clouds is con­ bility in a given situation, can (and should) be tested siderably more difficult, not just from the increased using local measurements of the P-I parameters and computational load, but also because of the difficulty of pigment-profile parameters. But, application of the meeting the additional data requirements. Remote- model to large scales, using remotely-sensed data, sensing techniques, which use cloud information from imposes an additional requirement: a method has to be geostationary satellites (Gautier et al., 1980; Bishop and devised for extrapolating these parameters to those Rossow, 1991), hold the best potential for obtaining pixels where no direct measurements exist. This is a very operational estimates of surface short-wave radiation in important aspect of the problem of remote sensing of the presence of clouds. Most of the efforts to date in primary production. The in situ and satellite data are estimating surface radiation from satellite data have acquired at very different spatial and temporal scales, focused on total short-wave radiation, while, from the and all available oceanographic knowledge and intuition ICES mar. Sei. Symp.. 197(1993) Remote sensing of water-column primary production 241 have to be used to make the match between these tude, then it is easy to appreciate that, at large scales, different data sets. The methods proposed so far to such information is indeed valuable. The performance achieve this goal are very simple; to some extent, this is of the CZCS admittedly deteriorates in coastal waters. imposed by our limited knowledge on the global distri­ However, one should not forget that the CZCS rep­ bution of the parameters. resents our first foray into remote sensing of phyto­ The method proposed by Platt and Sathyendranath plankton biomass from satellites. The next generations (1988) consists of partitioning the ocean into a number of ocean colour sensors will have higher spectral and of bio-geochemical provinces and of classifying these radiometric resolution, and we believe that we can look provinces according to their P-I properties and vertical forward to better performances from these satellites. biomass structure (very much like the use of tempera­ Remote sensing has neither generated nor requires ture and salinity to classify water masses in classical new models of photosynthesis. What is required of such physical oceanography). The rationale for this approach models is the same, whatever the application: we look is that the distribution of P-I parameters shows some for models capable of describing the photosynthetic distinct regional trends. For estimating the average response of phytoplankton to changes in the quality and production within that region (or province) using satel­ quantity of light available. Therefore, the discussion of lite data, it would be safe to use the average parameters the models undertaken in this paper is by no means for that region rather than, say, a global average. Admit­ limited to remote-sensing applications. However, in the tedly, such a classification would have to allow for remote-sensing field, the picture has been clouded by seasonal variations in the parameters and in their verti­ the fact that models are sometimes tested with param­ cal structure (Platt etal., 1992), and for possible seasonal eters that are guessed rather than directly measured variations in the boundaries of the province. Platt et al. (Balch et al., 1992), and poor results due to bad param­ (1992) have shown that the P-I parameters for the eter estimates might then be interpreted as poor Sargasso Sea during spring bloom are significantly performance of the model itself. This is a dangerous higher than those typical of other seasons. Since spring tendency, and problems with estimating parameters blooms are associated with high biomass, there is a have to be clearly separated from problems with the covariance between biomass and P-I parameters; there­ model itself if we are not to reject useful models for the fore, if primary production is computed from satellite wrong reasons. The right way to test a model against data at the annual scale without accounting for seasonal observations is with the best parameters available, and trends in the parameters, the results would be biased. If any uncertainties in the parameter estimates have to be the boundaries between provinces are demarcated by borne in mind when interpreting the model results. fronts, then satellite data could prove useful for identify­ It is also important, in the remote-sensing context, to ing these boundaries. avoid comparing results at scales that are mismatched. The large-scale estimates of primary production from For example, consider the case where average param­ remotely-sensed data would obviously be sensitive to eters have been proposed for a biogeochemical prov­ the parameter values assigned to provinces. The limited ince. The only use of those parameters should be for information currently available on these parameters is estimating the average productivity of that province. inadequate for arriving at a global classification of bio­ The use of average parameters can lead to significant geochemical provinces. This is one area where much in errors at the pixel level (for example, see Babin et al., situ work is still required to complement satellite data. 1993); but that would not automatically invalidate the The advent of new methods such as the active laser usefulness of the average parameter at large scale. At technique (Falkowski and Kolber, this volume), which this scale, the average parameters would yield wrong provide quick estimates of P-I parameters, should go a results only if (i) the estimated average for the province long way towards addressing the problem of undersam­ is not the true average, or (ii) the non-linear effects pling in these parameters. (which are ignored when averaging the parameters) introduce a bias in the computed primary production. Such a bias can, in principle, be estimated and corrected Concluding remarks for. The accuracy of satellite-based estimates of chloro­ The biggest source of error in remote sensing of primary phyll concentration does not approach that attainable production is the error associated with biomass retrieval from in situ observations. Clearly, therefore, satellite from satellite. With the CZCS data, the accuracy in data are no substitute for in situ measurements. Their biomass retrieval is estimated to be about ±35% at best. usefulness is in providing synoptic information on vari­ This may appear to be disappointingly poor at a first ability in surface biomass that is impossible to come by glance, but when one considers that pigment concen­ from in situ measurements. In other words, in situ and trations in the oceans vary over four orders of magni­ satellite observations are complementary and, together, 242 S. Sathyendranath and T. Platt ICES mar. Sei. Symp., 197 (1993) they provide a more complete picture of the distribution Gordon, H. R., and McCluncy. W. R. 1975. Estimation of the of biomass and primary production in the ocean than depth of sunlight penetration in the sea for remote sensing. would otherwise be possible. Appl. Opt., 14: 413—416. Gordon, H. R., and Morel, A. 1983. Remote assessment of The approach as outlined here has some obvious ocean color for interpretation of satellite visible imagery. 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