The New Age of Hyperspectral Oceanography

C O A S TA L O C E A N O P T I C S A N D D Y N A M I C S The New Age of Hyperspectral Oceanogra B Y G R A C E C H A N G , K E V I N M A H O N E Y, A M A N DA B R I G G S W H ITM I R E , DAV I D D. R . KO H L E R , C U R T I S D . M O B L E Y, M A R L O N L E W I S , M A R K A . M O L I N E , E M M A N U E L B O S S , M I N S U K I M , W I L L I A M P H I L P O T, AND TOMMY D. DICKEY been published in Oceanography, Volume 17, Number 2, a quarterly journal of The Oceanography Society. Copyright 2003 by The Oceanography Society. All rights reserved. Reproduction of any portion of this artiJune 2004 16This article hasOceanography cle by photocopy machine, reposting, or other means without prior authorization of The Oceanography Society is strictly prohibited. Send all correspondence to: [email protected] or 5912 LeMay Road, Rockville, MD 20851-2326, USA. aphy A multispectral optical sensor collects data coastal and open-ocean studies. Advances in at select wavebands or channels. An example computer technology in the last decade have is the Sea-viewing Wide-Field-of-view Sen- enabled more rapid processing of hyperspec- sor (SeaWiFS) ocean color satellite, which tral data and greatly improved the storing measures eight wavebands between 402 and and archiving capability of these large, and 885 nm (20-40 nm bandwidth with peaks often difficult-to-manage data sets. centered around 412, 443, 490, 510, 555, 670, Hyperspectral technology has expanded 765, and 865 nm). Optical oceanographers from hand-held radiometers to submerged have been using multispectral sensors since sensors for measurements of inherent op- 1 the 1980s with great success . A hyperspectral sensor gives continuous tical properties (IOPs), optical properties that depend on only the aquatic medium spectral coverage over a broad wavelength itself (e.g., absorption and scattering; Mo- range [at least over visible wavelengths, bley, 1994) and apparent optical properties and preferably from near ultraviolet (UV) (AOPs), which depend on the IOPs and the to near infrared (IR)] with better than 10 geometry of the light field. Recently, hy- nm resolution. The utility of hyperspectral perspectral airborne detectors have been measurements has long been recognized in enhanced for high spectral and spatial reso- fields as diverse as geology and astronomy, lution measurements of ocean radiance and and hyperspectral instruments have been reflectance. Although multispectral sensors used in oceanographic research for about 30 have a higher signal to noise ratio for the years. However, most of these instruments same quality of optical components (be- have been laboratory bench-top spectro- cause they integrate over a larger bandwidth photometers and radiometers that measure and thus collect more photons each band), absorption and radiance or irradiance at <10 the sensitivity and data quality of hyper- nm continuous spectral resolution from the spectral sensors are rapidly increasing and UV to IR wavelengths. These instruments costs are coming down. Thus the shift from were relatively slow with sample scan rates multispectral to hyperspectral systems will on the order of minutes to maximize signal continue. The availability of hyperspectral to noise. Just a decade ago, computational sensors opens a new door for optical ocean- limitations also made processing and stor- ography and related fields that make use of age of large amounts of hyperspectral data optical remote sensing of the oceans. Here, difficult. However, within the last five years, we discuss a few of the scientific advantages high sample rate (less than seconds) in situ to using high spectral resolution sensors and and remote sensing hyperspectral sensors describe valuable hyperspectral applications have been developed and utilized for various in the marine environment. 1 See special issues: “Hydrologic Optics” in Limnology and Oceanography, 34(8), 1989; “Ocean Color From Space: A Coastal Zone Color Scanner Retrospective” in Journal of Geophysical Research, 99(C4), 7291-7270, 1994; and “Ocean Optics” in Journal of Geophysical Research, 100(C7), 13,133-13,372, 1995). Oceanography June 2004 17 SPECTR AL TECHNIQUE S [high concentrations of colored dissolved Traditionally, multispectral remote sensors organic matter (CDOM) and particles] have been utilized for characterizing open- cannot be ignored, (3) the influence of the ocean waters. Some results have shown that ocean bottom (bottom reflectance and sedi- a few, wide, carefully selected bands may ment resuspension) is important, and (4) be all that is needed to monitor these water high temporal and spatial variability collude bodies whose optical signatures are domi- to create an optically diverse environment. nated by chlorophyll a and co-varying opti- Not only do these influences complicate the cally significant constituents. However, when characterization of the water and bottom these open ocean algorithms (O’Reilly et al., types, but also make the atmospheric correc- 1998) are applied to coastal areas, the results tion of these scenes difficult. Traditional blue are less useful, if not altogether inapplicable water atmospheric corrections (e.g., “black (Hu et al., 2000; Lee and Carder, 2002). The pixel” assumptions; Siegel et al., 2000) are coastal ocean is an optically complex envi- no longer valid. These correction methods ronment. For example: (1) phytoplankton assume that any remote sensing signal at the populations are generally more abundant IR wavelengths is due to the atmosphere, and less diverse, (2) terrestrial influences but this assumption does not hold in high sediment or optically shallow coastal waters. Grace Chang ([email protected]) is Thus, the successful removal of the atmo- Assistant Researcher, Ocean Physics Laboratory, spheric interference in the water-leaving University of California, Santa Barbara, Goleta, radiance signal within the coastal environ- CA. Kevin Mahoney is Postdoctoral Fellow, ment requires a priori knowledge of a host Monterey Bay Aquarium Research Institute, of atmospheric constituents (e.g., water col- Moss Landing, CA. Amanda Briggs-Whitmire umn vapor, aerosol type and density, ozone is Graduate Student, College of Oceanic and concentration). Without a priori knowledge, Atmospheric Sciences, Oregon State University, these constituents must be derived from the Corvallis, OR. David D.R. Kohler is Senior Sci- spectral data stream itself, decreasing the entist, Florida Environmental Research Institute, degrees of freedom with which to resolve Tampa, FL. Curtis D. Mobley is Vice President the water leaving radiance signal. Addition- and Senior Scientist, Sequoia Scientific, Inc., Bel- ally, the increased development along the levue, WA. Marlon Lewis is Professor, Depart- world’s coastal boundaries adds a degree of ment of Oceanography, Dalhousie University, complexity in the determination of concen- Halifax, Nova Scotia, Canada. Mark A. Moline is tration and interactions between the ma- Associate Professor, Biological Sciences Depart- rine and terrestrial aerosols, such that the ment, California Polytechnic State University, San atmospheric parameterization may change Luis Obispo, CA. Emmanuel Boss is Assistant dramatically within a single scene. Hyper- Professor, School of Marine Sciences, University spectral information provides optical ocean- of Maine, Orono, ME. Minsu Kim is Postdoctoral ographers the potential to accurately correct Associate, School of Civil & Environmental Engi- remote sensing images and classify complex neering, Cornell University, Ithaca, NY. William oceanic environments, finer-scale features Philpot is Associate Professor, School of Civil & (e.g., bottom type and characteristics and Environmental Engineering, Cornell University, phytoplankton blooms), and depth-depen- Ithaca, NY. Tommy D. Dickey is Professor, Ocean dent IOPs. Physics Laboratory, University of California, Santa Barbara, Goleta, CA. 18 Oceanography June 2004 With higher spectral resolution data (i.e., more wavelengths) come more degrees of freedom for optical models and empirical Schofield et al., In press). These decomposi- et al., 1992) whereas other microorgan- algorithms. Many ocean color algorithms tion analyses are techniques that separate isms, such as bacteria and flagellates, show in use today involve empirical relationships pigment peaks and shoulders from troughs wavelength independent backscatter (Morel between the property of interest (i.e., chloro- in phytoplankton absorption curves of and Ahn, 1990, 1991). These studies and the phyll a concentration, IOPs, etc.) and wave- mixed assemblages. The similarity index is results of numerous modeling efforts (see band ratios of remote sensing reflectance or typically used to correlate measured absorp- Stramski et al., 2001 and references therein) water-leaving radiance (O’Reilly et al., 1998). tion with known phytoplankton absorption demonstrate that backscatter is not spectral- Most of these algorithms are derived by re- curves for identification purposes by taking ly flat (as it is oftentimes modeled) or easily gressions of radiance at select (or available) into account the differences in shapes be- predicted for all particles. Therefore, back- wavebands or waveband ratios versus the property of interest. Naturally, the regression results are maximized at the highest number of statistically independent wavelengths available. Also, the spectral resolution of derived IOPs is limited by the number of wavebands of the ocean color remote sens- Hyperspectral information provides optical oceanographers the potential to accurately correct remote sensing images and classify complex oceanic environments, finer-scale features... and depth-dependent [inherent optical properties]. ing data used in the regression. tween two spectra based on the peaks and scatter has the potential to provide a means tion are useful for determining the relative troughs of each spectrum. These identifica- to identify phytoplankton by group or spe- concentrations and variability of the differ- tion techniques usually cannot be applied cies and to determine particle characteristics. ent constituents in the water column: water to multispectral data because the required This provides incentive for the development itself, phytoplankton, CDOM, and inorgan- features (i.e., peaks and troughs) are not well of in situ hyperspectral backscatter sensors ics (Schofield et al., In press and references resolved. and algorithms. Multispectral measurements of absorp- therein). Absorption peaks of chlorophyll a, While the absorption properties of nu- non-pigmented troughs, and the exponen- merous planktonic species and other water tial slopes of CDOM and inorganic material column constituents have been studied ex- E XA MPLE S OF HYPER SPECTR AL ANALYSE S are well distinguished in absorption spec- tensively, the same cannot be said for their Hyperspectral data used in combination tra collected by most multispectral sensors. backscattering properties. Backscattering with spectral techniques such as derivative However, in order to identify phytoplankton properties must be known in order to ac- analysis, spectral angle mapping, spectral by taxonomic group or species, quantifi- curately interpret ocean color measure- deconvolution, and similarity indices can aid cation of the absorption by accessory or ments because the reflectance of the upper in the characterization of marine ecosystems marker pigments beyond chlorophyll a is ocean is directly related to the ratio of the including the detection and identification of oftentimes necessary. Some accessory pig- backscattering coefficient to the absorption harmful algal blooms, an increasing prob- ments are unique to individual phytoplank- coefficient. Hyperspectral backscattering lem in the world’s coastal oceans (Millie et ton taxa and usually cannot be discerned in measurements can be used to distinguish al., 1997; Lohrenz et al., 1999). For example, absorption spectra with a limited number of phytoplankton populations from co-varying Figure 1 shows phytoplankton absorption wavelengths or wavebands (accessory pig- seawater constituents because the spectral spectra for a red tide species, Karenis bre- ment peaks are generally narrow), but can dependence of backscattering by algal cells vis, measured with a multispectral sensor, be discriminated in hyperspectral data. This is different from that of other particles (Bri- a hyperspectral sensor, and modeled us- discrimination can be accomplished with caud et al., 1983; Stramski et al., 2001). Also, ing Mie theory (following Mahoney, 2001). various methods such as spectral unmixing hyperspectral backscatter measurements in K. brevis can be identified by its accessory and deconvolution, Gaussian decomposi- the laboratory have revealed that some phy- pigment, Gyroxanthin–diester, which has tion, and derivative analysis (usually taken toplankton species may show complex, high- unique absorption peaks at 444 and 469 nm to the fourth derivative) together with simi- ly distinct backscattering spectra between (Örnólfsdóttir et al., 2003). As seen in Figure larity index analysis (e.g., Millie et al., 1997; species (cultured) (Bricaud et al., 1983; Ahn 1, the multispectral spectrum lacks detailed Oceanography June 2004 19 absorption information, i.e., pigment peaks radiative transfer model (Mobley, 1994). by distinguishing accessory pigments, due Water 1 is 6.5 m and has low chlorophyll a to a limited number of wavebands. Hyper- and CDOM concentrations with a bottom spectral data allow for the detection of spe- type of a mixture of soft coral and Sargas- cies-discriminating accessory pigments and sum, while Water 2 is 13 m deep, “pure wa- are more adequate for comparing measured ter” with a flat green sponge bottom type. By spectra to a reference spectrum for similar- inspection of the hyperspectral spectra, the ity index analysis (Figure 1). Wood et al. difference between the two curves is obvious (2002) have also used these techniques and in the 500-600 nm range. However, spectra presented evidence that distinctive hyper- for the two water types produced using only spectral signatures are associated with Syn- the SeaWiFS wavebands appear almost iden- echococcus blooms in upwelling and nutrient tical (note: the SeaWiFS spectra were derived enrichment systems in the Gulf of Califor- by applying the SeaWiFS spectral response nia. Cannizzaro et al. (2002) show that it is function to the hyperspectral signatures). A possible to utilize multispectral techniques second example, Figure 3, shows 122 remote (SeaWiFS) to detect K. brevis. However, their sensing reflectance spectra generated by Hy- method works only for waters under certain drolight for various combinations of nine optical conditions (low concentrations of different sets of IOPs, 32 different bottom CDOM and suspended sediments relative to reflectances, and 22 depths between 5.5 and chlorophyll a or low backscattering relative 50 m. These spectra are clearly unique. How- to absorption) as different ocean color prod- ever, every spectrum has nearly the same ucts (particulate backscattering and its rela- remote sensing reflectance wavelength ratio: tionship to chlorophyll a) are used as proxies Rrs(490)/Rrs(555) = 1.71 ± 0.01. This ratio, for K. brevis abundance. if used in the SeaWiFS Ocean Chlorophyll 2 In the past, multispectral techniques have 20 Oceanography June 2004 (OC2) band-ratio algorithm (O’Reilly et al., been used for the derivation of water depth 1998, as revised on http://seawifs.gsfc.nasa. and bottom bathymetry (e.g., Philpot, 1989; gov/SEAWIFS/RECAL/Repro3/OC4_repro- Maritorena et al., 1994), and more recently cess.html), gives a chlorophyll concentra- for characterization of bottom type (see tion of 0.59 ± 0.01 mg Chl m-3. Thus these “Light in Shallow Waters” in Limnology and simulated water bodies, which have IOPs Oceanography, 48(2), 2003). These analy- corresponding to chlorophyll concentrations ses generally involve empirical algorithms, between 0.0 (pure water) and 0.2 mg Chl where reflectance waveband ratios are re- m-3, are all viewed as the same by the OC2 gressed against water depth. Wavelength lim- algorithm. The OC2 algorithm fails here itations and commonly employed assump- because of bottom effects in optically clear tions that the water optical properties are waters simulated by Hydrolight. vertically uniform and constant over the area While much of the interest in hyper- being mapped can lead to inaccurate retriev- spectral approaches relates to the visible als of bottom depth and characteristics un- wavebands, several oceanic constituents of der certain conditions. These retrievals can interest have distinct spectral signatures be improved with hyperspectral data (Lee in the UVA/UVB (e.g., Ogura and Hanya, and Carder, 2002 and references therein). 1966). Chief among these is nitrate, a ma- For example, Figure 2 shows hyperspectral jor plant nutrient that limits the primary remote sensing reflectance spectra for two production of organic matter in many re- water types generated by the Hydrolight gions of the world’s oceans. The net vertical 0.3 Figure 1. Phytoplankton taxonomic group or species identification is now achievable with the development of hyperspectral instruments; generally narrow accessory 0.25 pigment absorption wavelength peaks that are unique to specific species can be discerned. Shown here are three different methods used to measure phytoplankton absorption spectra for a red tide species, Karenis brevis, on the west Florida shelf. a (m −1 ) 0.2 0.15 Closed circles symbolize absorption measured with a multispectral sensor (ac-9). Open circles signify data modeled using Mie theory (following Mahoney, 2001), and plus Gyroxanthin−diester signs represent data measured with a hyperspectral sensor (HiStar). It is apparent in this figure that the multispectral spectrum lacks the distinguishing accessory pigment peaks due to a limited number of wavebands. Hyperspectral data, however, allow for 0.1 the detection of species-discriminating accessory pigments and are more adequate for comparing measured spectra to a reference spectrum and thus phytoplankton species identification. K. brevis can be identified by its accessory pigment, Gyroxanthin–dies- 0.05 0 −0.05 350 ter, which has unique absorption peaks at 444 and 469 nm (Örnólfsdóttir et al., 2003). ac9 Mi e HiStar 400 450 (Multispectral data were provided by Oscar Schofield and John Kerfoot, Rutgers University and hyperspectral data were provided by Steven Lohrenz, University of Southern Mississippi.) 500 550 600 650 700 750 Wavelength (nm) Figure 2. Bottom effects in shallow coastal waters may lead to inaccurate remote sensing retrievals of bottom depth if limited spectral bands are utilized for analysis. This figure shows modeled hyperspectral (solid lines) and multispectral (SeaWiFS wavebands; circles) spectra for two water types, generated by the Hydrolight radiative transfer model (Mobley, 1994). Water 1 (blue) is 6.5 m deep and has low chlorophyll-a and CDOM concentrations with a bottom type of a mixture of soft coral and Sargassum, while Water 2 (green) is 13 m deep, “pure water” with a flat green sponge bottom type. By inspection of the hyperspectral spectra, the difference between the two curves is obvious in the 500-600 nm range. However, spectra for the two water types produced using only the SeaWiFS wavebands appear almost identical. (SeaWiFS spectra in this figure were derived by applying the SeaWiFS spectral response function to the hyperspectral signatures). Figure 3. Chlorophyll concentration algorithms designed for multispectral instrumentation may not be useful for shallow, optically clear waters. Shown here are one hundred twenty two Hydrolight-generated remote sensing reflectance (Rrs) spectra for Bahamian waters using various combinations of nine different sets of IOPs, 32 different bottom reflectances, and 22 depths between 5.5 and 50 m. These spectra are clearly unique. However, every spectrum has nearly the same remote sensing reflectance wavelength ratio: Rrs(490)/Rrs(555) = 1.71 ± 0.01 (490 and 555 nm are indicated by the vertical black dashed lines). If this ratio were applied to the commonly used SeaWiFS band-ratio algorithm (OC2; O’Reilly et al., 1998), it would give a chlorophyll concentration of 0.59 ± 0.01 mg Chl m-3. In other words, the same chlorophyll concentration would be determined for all 122 spectra despite the fact that these simulated water bodies have IOPs corresponding to chlorophyll concentrations between 0.0 (pure water) and 0.2 mg Chl m-3. The OC2 algorithm fails here because of bottom effects in optically clear waters. Oceanography June 2004 21 ABOVE AND PRECEDING THREE SPREADS: Three bands (RGB= 666, 547, 439 nm) from a March 23, 1996 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) image taken over the Florida Keys from an ER-2 aircraft at 20 km above ground. The top of the image is near the eastern end of the Keys; the bottom of the image is near the western end. The rough heading is 260 degrees (clockwise from north) top to bottom (i.e., just south of west). AVIRIS is an optical sensor that delivers calibrated images of upwelling spectral radiance in 224 contiguous spectral channels (bands) with wavelengths from 400 to 2500 nanometers. Note that this image is not atmospherically corrected. It is pi*radiance/[mean_solar_irradiance_at_the_top_of_the_atmosphere * cos(solar_zenith_angle)]. Original data courtesy of NASA/JPL. Caption courtesy of Marcos Montes of Naval Research Laboratory, Washington D.C. 22 Oceanography June 2004 transport of nitrate, for example, constrains accessory pigment absorption wavelength the export flux of organic matter from the peaks that are unique to specific species can surface ocean in a steady-state sense. Nitrate be discerned. High spectral resolution back- dissolved in seawater exhibits a broad ab- scattering spectra are unique to some phy- sorption maximum centered at ~210 nm; it toplankton species and can aid in the char- competes with the absorption of bromide, acterization of oceanic particles. One other a conservative component of sea-salt, and exciting aspect of hyperspectral technology to a lesser extent, the carbonate ion (Figure is the development of optically based chemi- 4). In anaerobic areas, sulphide also absorbs cal sensors. These sensors allow for long- in a band around 220 nm and various dis- term monitoring of ecologically important solved organic compounds of oceanographic nutrients and potentially harmful pollutants and practical purposes (e.g., TNT) exhibit at unprecedented time and space scales. absorption maxima in the UV. Past attempts Hyperspectral instrumentation is becom- to estimate the concentration of nitrate and ing increasingly important to oceanographic other compounds with multispectral instru- research as coastal and open ocean observ- ments have been met with equivocal suc- ing systems are rapidly developing into key cess. The introduction of a field-deployable elements for scientific research, monitoring, hyperspectral UV absorption spectrometer, decision-making, science education, and i.e., the In Situ Ultraviolet Spectrometer outreach. Some concerns of these observa- (ISUS), coupled with advanced spectro- tories are that autonomous sampling plat- scopic deconvolution techniques, has made forms can be limited by weight and volume routine spectral measurements of nutrients and data bandwidth capabilities. The incor- possible (Johnson and Coletti, 2002; Figure poration of hyperspectral sensors to autono- 4). Oceanographers are now able to resolve mous sampling platforms of an observing nitrate concentrations in the ocean at tem- system can expand the amount of informa- poral and spatial scales consistent with mea- tion gained from one instrument without surements of temperature and salinity and compromising platform payload. High to an accuracy and precision more than ac- spectral resolution sensors provide a greater ceptable for oceanographic biogeochemical number of wavelengths for various analysis investigations, as a direct result of a hyper- techniques, particularly in optically complex spectral approach to the problem. coastal environments. In addition, emerging cabled observatories offer exceptional power SUMMARY AND CONCLUSIONS and data bandwidth for hyperspectral sen- Hyperspectral technology provides a means sors. for optical oceanographers to classify and Optical oceanographers have been posing quantify complex oceanic environments (in hyperspectrally-related questions since the situ and remotely): bottom depth and type, popularity of ocean exploration expanded particle characteristics, depth-dependent in the 1950s. However, technological and IOPs, and specific chemical compounds. computing constraints limited us to the Hyperspectral data enable, for the first time, use of multispectral or even single wave- a real attempt at environmental spectros- length sensors in our field studies. Now that copy. In situ and remote phytoplankton computing power has become more than taxonomic group or species identification adequate to handle large quantities of data is now achievable with the development of and technology has allowed miniaturiza- hyperspectral instruments; generally narrow tion of in situ and remotely sensed optical Figure 4. Nitrate is a major plant nutrient that limits the primary production of organic matter in many regions of the world’s oceans. Nitrate dissolved in seawater exhibits a broad absorption maximum centered at ~210 nm. The introduction of a field-deployable hyperspectral UV absorption spectrometer, known as In Situ Ultraviolet Spectrometer (ISUS), coupled with advanced spectroscopic deconvolution techniques, has made routine spectral measurements of nutrients possible at unprecedented time and space scales. The specific molar absorption of bromide (black, dotted line) and nitrate (black, solid line) are shown with the absorption spectrum of whole water (red line; 1 nm resolution) measured with the ISUS (MBARI/Satlantic Inc.) deployed on a Conductivity-Temperature Depth profiler (CTD) at 150 m depth in the western Equatorial Pacific. Most of the variance in absorption is explained by bromide; advanced deconvolution techniques are required to extract the concentration of nitrate (here 14.9 M) based on its absorption. sensors, we have been able to utilize hyperspectral instruments in the field to answer a host of scientific questions that were never before possible. 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