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Remote Sensing of Water-Column Primary Production

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Remote Sensing of Water-Column Primary Production 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.
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