Coastal Transport of Organic and Inorganic Matter

Introduction: Ocean optical properties provide the link between the spectral signature of ocean color and the composition of the water. By unraveling this signature, we can decouple the individual components that contribute to the signal and obtain a wealth of information about the concentration of the dissolved and particulate matter in the water. Furthermore, we can measure ocean color from space using satellites, so we can synoptically map spatial distributions of the bio-optically active components at ecologically relevant time scales. For the first time, using new satellite bio-optical algorithms that we have developed to partition the dissolved and particulate matter into organic and inorganic components, we are unraveling the complicated spectral signature and gaining valuable insight into a variety of physical and biogeochemical processes occurring in coastal and shelf regions.

Ocean Color Remote Sensing: Starting with the Coastal Zone Color Scanner (CZCS), and more recently with the sea-viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and MEdium Resolution Imaging Spectrometer (MERIS), ocean color satellites have provided synoptic estimates of bio-optical parameters such as chlorophyll for nearly 25 years.^1,2 It is possible to estimate water composition from space because the water-leaving radiances at visible wavelengths measured by the satellite sensors are related to the particulate and dissolved substances in the water through the radiative transfer equations. These relationships enable us to estimate the inherent optical properties (IOPs) of the water, such as the absorption and backscattering coefficients, which affect phytoplankton primary production, biomass, heat flux, convective mixing, and naval applications (laser mine detection systems, diver visibility). Furthermore, the optical properties vary over short spatial and temporal scales in the coastal environments, so we can use these remote sensing estimates of the IOPs to optically track and classify water masses.

Before we can unravel the components and optical characteristics of the water, however, the satellite data must be atmospherically corrected. The atmosphere can contribute up to 90% of the total signal measured by the sensor at the top of the atmosphere, so accurate removal of this signal is essential over the ocean where reflected radiances are lower than over land. The atmospheric correction and bio-optical algorithms were originally developed for open-ocean waters, where optical properties are primarily controlled by phytoplankton and correlated properties. Frequently, the algorithms failed near shore, precluding satellite retrievals along the coast, because the assumptions used for open-ocean areas are not valid in turbid coastal waters. Recently, however, algorithm modifications have extended estimates shoreward, enabling pixel retrievals all the way into turbid coastal waters, bays, and even estuaries where optical prop-erties are controlled by a complex mix of phyto-plankton, suspended sediments, and colored dissolved organic matter (CDOM).³ With a new suite of aircraft and satellite ocean color sensors currently on-line or planned for the near future, with increased spectral and spatial resolution, new algorithms are required to take advantage of these improved capabilities and provide new products in coastal areas.

Partitioning Organic and Inorganic Matter in Coastal Areas: Although optical instrumentation and remote sensing algorithms have advanced tremendously in recent years, the separation of the optical signature into organic and inorganic components has been problematic and has only recently been addressed. The concentration of total suspended solids (TSS) and its partitioning into particulate organic and inorganic matter (POM, PIM) is of interest from both remote sensing and modeling aspects. The concentration and space/time distribution of the inorganic component (including silts, clays, sand, and phytoplankton tests) can be used to trace river plumes and fronts and can indicate regions of particle resuspension from wave and storm-induced turbulence. The distribution of the organic component (including living phytoplankton, zooplankton, and their decay products) impacts the development of anoxic "dead zones" and is required for carbon flux estimates; it does not necessarily mirror the distribution of the inorganic component because they are influenced by different processes (physical vs biological controls). Carbon is the currency used in the exchange processes between the biosphere, hydrosphere, atmosphere, and lithosphere, so its distribution has far-reaching effects on global warming and ocean circulation.

(a) Particulate inorganic matter concentration.

(b) Particulate organic matter concentration.

FIGURE 6 New optical products from SeaWiFS imagery. Northern Gulf of Mexico, May 20, 2002. Color scale indicated on each image with units of mg/l.

During 2001 and 2002, we collected a suite of optical measurements in Mississippi Bight, in both clear offshore waters and turbid coastal waters (Fig. 6). We used this data set of in situ measurements to develop new algorithms to estimate the concentrations of PIM, POM, and TSS, and we have applied these algorithms to SeaWiFS satellite ocean color imagery.

Tracing Water Masses with Optics: We can use these new satellite products to optically characterize and trace water masses. For example, if we take two sequential images of the TSS product and form a "difference" image (i.e., at each pixel in the image, subtract the value on day one from the value on day two), we can follow river plume advection and trace the fate of the associated effluent (Fig. 7(a)). Furthermore, if we create images of the ratio of the PIM and POM products and create another difference image for the same two days, we can assess the relative changes in the particle composition (Fig. 7(b)). An increase in TSS between the two days could be due to an increase in the organic component of the particulate load, the inorganic component, or both. The PIM/POM difference image helps us determine which case occurred, and even helps us distinguish between competing physical and biological processes (an increase in the inorganic component indicates wave resuspension of bottom sediments or river discharge, whereas an increase in the organic component indicates phytoplankton growth).

FIGURE 7
(a) Percent difference in concentration of total suspended solids between 12 and 14 June, 2002. (b) Percent difference in the ratio of particulate inorganic matter concentration to particulate organic matter concentration (PIM/POM). Yellow-to-red color scale in each panel indicates pixels where the TSS concentration or PIM/POM ratio increased over the 2-day period; black-to-white color scale indicates pixels where they decreased. The changes in TSS concentration and PIM/POM ratio indicate advection of the Mobile Bay outflow plume as well as changes in the composition of the particulate matter. For example, the pixels in the circled areas (both panels) showed an increase in the TSS load and a decrease in the PIM/POM ratio, indicating an increase in the organic component relative to the inorganic component, possibly due to phytoplankton growth or settling of suspended sediments.

Optical Water Mass Classification: The total absorption coefficient can be partitioned into individual components due to phytoplankton a_j, detritus a_det, and colored dissolved organic matter a_CDOM. We have developed new algorithms to estimate each of these components from the satellite ocean color imagery. We can create a combined image of these three parameters to help us visualize the spatial distribution of the components. First we sum the three coefficients together at each pixel in an image, then calculate the percentage of the total due to each component. In Fig. 8(a), the red pixels indicate areas of relatively high detrital and CDOM absorption; the blue pixels correspond to relatively high CDOM absorption and lower phytoplankton and detrital absorption; and the green pixels repre-sent areas of relatively high phytoplankton and CDOM absorption and low detrital absorption. For a more quantitative characterization of the optical characteristics of the water masses, we form ternary diagrams (Fig. 8(b)) to classify each image pixel into one of 16 classes based on the percentages of each of the absorption components. We can classify and trace the temporal and spatial variability of the water masses in a region by performing these analyses on multiple scenes over time.

FIGURE 8
(a) SeaWiFS image from 20 May, 2002, representing the contributions of phytoplankton, detritus, and CDOM absorption at each pixel (red pixels are dominated by detrital absorption a_det, blue pixels by CDOM absorption a_CDOM, and green pixels by phytoplankton absorption a_j). (b) Ternary diagram for Fig. 8(a). Each axis represents the percentage of the total absorption coefficient that is due to the individual component. Colors correspond to pixel colors in Fig. 8(a). We can quantitatively classify each pixel in an image into one of 16 unique water classes based on the percentage of each absorption component. For example, the green pixels in Fig. 8(a) correspond predominately to class 9 in Fig. 8(b); thus, 0-25% of the total absorption coefficient for those pixels is due to detrital absorption, 25-50% is due to phytoplankton absorption, and 50-75% is due to CDOM absorption.

Summary: Our research has led to the development of new algorithms to assess water properties from space. Specifically, we estimate partitioned absorption, TSS, POM, and PIM from satellite ocean color imagery. For the first time, we have the capability to monitor geochemical and optical processes and the impact of human activity on our coastal zone. The development of this capability to remotely estimate both the concentrations and the optical characteristics of the organic and inorganic constituents of the water, coupled with the new ocean color sensors coming online, helps us trace and classify water masses and monitor river discharge, circulation patterns, sediment resuspension, phytoplankton growth, and the carbon cycle.

[Sponsored by ONR]