Optical Water Mass Classification for Interpretation of Coastal Carbon Flux Processes

R.W. Gould, Jr. & R.A. Arnone

Naval Research Laboratory, Code 7333, Stennis Space Center, MS 39529

(228-688-5587, gould@nrlssc.navy.mil)

1. ABSTRACT With the coverage provided by the SeaWiFS, MODIS-Terra, and MODIS-Aqua sensors, we now have the capability to monitor coastal ocean color processes on unprecedented spatial and temporal scales. We will address the natural variability of bio-optical processes in coastal regions, the dominant forcing factors involved, and the coupled coastal/open-ocean system response to the forcing. Our goal is to understand how the terrestrial and ocean systems are coupled by examining the interactions occurring in the coastal margin. Coastal ocean properties require extension of existing MODIS algorithms. Initially, we will develop an iterative, near-infrared (NIR) coastal atmospheric correction scheme for MODIS imagery that is analogous to a scheme we developed and implemented for SeaWiFS under NASA SIMBIOS funding. This atmospheric correction significantly improves estimates of water-leaving radiances at blue wavelengths in coastal regions, as well as derived bio-optical products based on the radiance values (chlorophyll concentration, absorption and scattering coefficients). Next, we will develop new satellite bio-optical algorithms to estimate particulate and dissolved organic matter concentration, for application to carbon flux studies. Increased spatial resolution is required to understand the terrestrial and coastal processes. We will develop new satellite bio-optical algorithms for absorption and scattering at the higher resolution (250 m) available with MODIS imagery, by coupling the 250, 500, and 1000 m resolution channels. This will include extending the atmospheric correction to the 250 m channels. These new algorithms are necessary to accurately develop a regional, multi-year, multi-sensor (SeaWiFS and MODIS Terra and Aqua) time series covering the northern Gulf of Mexico to examine optical variability. This time series will be used to develop a new optical water mass classification system, based on these new bio-optical products, to improve the traditional Case1/Case 2 distinctions. This classification will provide the methods to trace the spatial and temporal evolution of dissolved and particulate carbon through the coastal margin. These analyses will provide insight into the complex processes (resuspension/settling, river discharge, phytoplankton growth, advection) that influence the transport of particulate and dissolved species in coastal waters.

2. INTRODUCTIONUnderstanding changes in ocean biogeochemical

properties represents a significant contribution to our knowledge of the global carbon cycle. Our ability to monitor and detect these changes at regional and smaller scales will determine our understanding of the processes that control the distributions at global scales. Although the coastal margin represents a small percentage of the total ocean area, it exhibits the strongest and most immediate response to terrestrial fluxes and intrusion of open-ocean sources. Developing an understanding of the coastal margin responses to external and internal forces will enable us to define consequences of these changes. Ocean color remote sensing can be used to characterize the optical processes within the coastal margin. Satellite radiances represent a mixing of backscattered light from a mixture of in-water constituents. It is only through an understanding of the influence of the optical properties on ocean color that satellite imagery can be used to monitor changes in the coastal margin. New capabilities in coastal optics (instrumentation, observations, experimentation, algorithms, etc.) have enabled us to spectrally decompose the signatures into optical components of the water constituents. Understanding the spatial and temporal distributions of these components provide new capability to assess terrestrial flux and understand coastal processes through water mass classification. The capabilities of ocean color satellite sensors far exceed the current uses to simply estimate chlorophyll concentration on the shelf and beckons further research.

3. OBJECTIVES

• Improve remote sensing reflectance in coastal waters by applying a NIR coupled ocean –atmospheric correction to MODIS (Aqua and Terra).

• Develop new satellite bio-optical algorithms to estimate particulate and dissolved organic matter concentration, for application to carbon flux studies and apply to MODIS (Terra and Aqua).

• Extend bio-optical algorithms of absorption and scattering to the higher resolution (250 m) available with MODIS imagery by using improved atmospheric correction and extending 1km spectral channels to 250 m.

• Describe regional bio-optical variability through time-series analysis of multi-year, multi-sensor (SeaWiFS and MODIS) imagery for the Mississippi Gulf Coast.

• Characterize the changes occurring on the coastal margin by developing and applying an optical water mass classification system from this time series.

4. ATMOSPHERIC CORRECTION

Figure 1. MODIS diffuse attenuation coefficient at 532 nm, 1 Figure 1. MODIS diffuse attenuation coefficient at 532 nm, 1 October, 2001. Top panel from standard atmospheric October, 2001. Top panel from standard atmospheric correction. Bottom panel from NIR correctioncorrection. Bottom panel from NIR correction..

Standard Processing

NIR -Iteration

Figure 1 shows an example of the improved chlorophyll retrievals in coastal regions after applying the NIR correction scheme to SeaWiFS imagery. The procedure is independent of knowledge of the waters properties, or the aerosol type, and can be applied to all coastal areas as it is based on basic ocean optical properties. The SeaWiFS results have shown a significantly improved match up between satellite remote sensing reflectance and the in-situ measured reflectance. Both the NIR correction (Arnone et al., 1998; Stumpf et al., 2003) and the spectral scattering model (Gould et al., 1999) we developed have been implemented in the 4th SeaWiFS reprocessing by NASA Goddard (http://seawifs.gsfc.nasa.gov/SEAWIFS/RECAL/Repro4/NIR.html). Our algorithm modifications have extended estimates shoreward, enabling pixel retrievals all the way into turbid coastal waters, bays, and even estuaries where optical properties are controlled by a complex mix of phytoplankton, suspended sediments, and colored dissolved organic matter (CDOM). 5. PARTITIONING

ORGANIC/INORGANIC MATTER

Figure 2. Examples of new optical products Figure 2. Examples of new optical products from SeaWiFS imagery. Northern gulf of from SeaWiFS imagery. Northern gulf of Mexico, 20 May 2002. A. PIM. B. POM. Mexico, 20 May 2002. A. PIM. B. POM. Color scale indicated on each image in Color scale indicated on each image in units of mg/l.units of mg/l.

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% 3A. % 3B.

Figure 3. Examples of new optical Figure 3. Examples of new optical products derived from SeaWiFS imagery in products derived from SeaWiFS imagery in the northern Gulf of Mexico. A. Percent the northern Gulf of Mexico. A. Percent difference in concentration of total difference in concentration of total suspended solids between 12 and 14 June, suspended solids between 12 and 14 June, 2002. B. Percent difference in the ratio of 2002. B. Percent difference in the ratio of particulate inorganic matter concentration particulate inorganic matter concentration to particulate organic matter concentration to particulate organic matter concentration (PIM/POM) for the same time period.(PIM/POM) for the same time period. Yellow-to-red color scale in each panel indicates pixels where the TSS concentration or PIM/POM ratio increased over the two 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.

6. 250 METER RESOLUTION OPTICAL PRODUCTS

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Figure 4. A. MODIS Terra backscattering coefficient at 551nm Figure 4. A. MODIS Terra backscattering coefficient at 551nm for 1 October 2001 at 1 km resolution. B. Backscattering for 1 October 2001 at 1 km resolution. B. Backscattering coefficient for the same image at 250 m resolution.coefficient for the same image at 250 m resolution. In this high-resolution image, increased structure is apparent, including more highly defined frontal regions and river discharge plumes. Insets in each image show coastal details.

7. OPTICAL WATER MASS CLASSIFICATION

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Figure 5. Example of optical water mass classification in the Figure 5. Example of optical water mass classification in the northern Gulf of Mexico. A. SeaWiFS satellite image from 20 northern Gulf of Mexico. A. SeaWiFS satellite image from 20 May, 2002.May, 2002. The classification is based on the absorption properties of the major particulate and dissolved components of water: phytoplankton, detritus (unpigmented living and non-living particles), and colored dissolved organic matter (CDOM). Red pixels in the image are dominated by detrital absorption (adet), blue pixels by CDOM absorption (aCDOM), and green pixels by phytoplankton absorption (a). B. Ternary diagram for the image in A.B. Ternary diagram for the image in A. Each axis represents the percentage of the total absorption coefficient that is due to the individual component. Colors correspond to pixel colors in panel 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 total absorption coefficient for each of the green pixels in class 9 is partitioned into 0-25% detrital absorption, 25-50% phytoplankton absorption, and 50-75% CDOM absorption.

8. SummaryWe are developing NIR atmospheric correction techniques for MODIS to improve estimates of water-leaving radiances and derived optical properties. We will extend the atmospheric correction and bio-optical algorithms developed for the 1km channels to the 250m channels. In addition, we are developing new algorithms to estimate concentrations of inorganic and organic matter in coastal regions. We will construct a three-year, multi-sensor, ocean color time series of the northern Gulf of Mexico. Finally, we will apply a new optical water mass classification system to characterize coastal optical variability and trace water masses.

PIM POM