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Remote Sensing of Ocean Color

  • Heidi M. Dierssen
  • Kaylan Randolph
Chapter

Abstract

The oceans cover over 70% of the earth’s surface and the life inhabiting the oceans play an important role in shaping the earth’s climate. Phytoplankton, the microscopic organisms in the surface ocean, are responsible for half of the photosynthesis on the planet. These organisms at the base of the food web take up light and carbon dioxide and fix carbon into biological structures releasing oxygen. Estimating the amount of microscopic phytoplankton and their associated primary productivity over the vast expanses of the ocean is extremely challenging from ships. However, as phytoplankton take up light for photosynthesis, they change the color of the surface ocean from blue to green. Such shifts in ocean color can be measured from sensors placed high above the sea on satellites or aircraft and is called “ocean color remote sensing.” In open ocean waters, the ocean color is predominantly driven by the phytoplankton concentration and ocean color remote sensing has been used to estimate the amount of chlorophyll a, the primary light-absorbing pigment in all phytoplankton. For the last few decades, satellite data have been used to estimate large-scale patterns of chlorophyll and to model primary productivity across the global ocean from daily to interannual timescales. Such global estimates of chlorophyll and primary productivity have been integrated into climate models and illustrate the important feedbacks between ocean life and global climate processes. In coastal and estuarine systems, ocean color is significantly influenced by other light-absorbing and light-scattering components besides phytoplankton. New approaches have been developed to evaluate the ocean color in relationship to colored dissolved organic matter, suspended sediments, and even to characterize the bathymetry and composition of the seafloor in optically shallow waters. Ocean color measurements are increasingly being used for environmental monitoring of harmful algal blooms, critical coastal habitats (e.g., seagrasses, kelps), eutrophication processes, oil spills, and a variety of hazards in the coastal zone.

Keywords

Atmospheric Correction Colored Dissolve Organic Matter Harmful Algal Bloom Ocean Color Photosynthetically Available Radiation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Glossary

Absorption, a(λ)

The fraction of a collimated beam of photons in a particular wavelength (λ), which is absorbed or scattered per unit distance within the medium (units 1/length or m−1). Photons which are absorbed by ocean water alter the spectral distribution of light that can be observed remotely.

Apparent optical properties (AOP)

Optical properties which depend primarily on the medium itself but have a small dependence on the ambient light field. Typically, AOPs are derived from measurements of the ambient light field, particularly upwelling and downwelling radiance and irradiance. Principal AOPs include irradiance reflectance, remote sensing reflectance, and the diffuse attenuation coefficients.

Backscattering, bb(λ)

Light of a particular wavelength (λ) that is scattered in a direction 90–180° away from its original path (i.e., backward hemisphere). Backscattered light is what is measured as ocean color in remote sensing, namely, downward propagating sunlight that has been redirected back toward the sea surface and out into the atmosphere. For natural waters, only a few percent of the light entering the ocean is backscattered out.

Colored or chromophoric dissolved organic material (CDOM)

CDOM is yellow-brown in color and absorbs primarily ultraviolet and blue light decreasing exponentially with increasing wavelength. Produced from the decay of plant material, it consists mainly of humic and fulvic acids and is operationally defined as substances that pass though a 0.2 μm filter.

Diffraction

Light which propagates or bends along the boundary of two different mediums with different indices of refraction.

Diffuse attenuation coefficient, K(λ)

A normalized depth derivative that describes the rate of change of light, plane incident irradiance, with depth. Sunlight underwater typically decreases exponentially with depth.

Index of refraction (real), n

The speed of light in a medium, c med , relative to the speed of light in a vacuum, c v expressed as \( n = {c_v}/{c_{{med}}} \). The real index of refraction determines the scattering of light at the boundary between two different mediums and within the medium from thermal and molecular fluctuations. The relative refractive index, n′, is the ratio of the speed of light within the medium, c m , to the speed of light within a particle, c p . As n′ deviates from 1, the scattering caused by the particle increases for a general size and shape particle (e.g., minerals and bubbles).

Inherent optical properties (IOP)

Optical properties which depend on the medium itself and are independent of the ambient light field. IOPs are defined from a parallel beam of light incident on a thin layer of the medium. Two fundamental IOPs are the absorption (a) and the volume scattering coefficient (β), which describe how light is either absorbed or directionally scattered by ocean water.

Irradiance (downward planar), Ed(λ)

The incremental amount of radiant energy per unit time (W) incident on the sensor area (m−2) from all solid angles contained in the upper hemisphere, expressed per unit wavelength of light (λ, nm−1). This is used to measure the amount of spectral energy from the sun reaching the sea surface.

Irradiance reflectance, R(λ)

The ratio of the upwelling irradiance, E u (λ), to the plane downwelling irradiance, E d (λ), in different wavelengths (λ).

Optical depth, ζ

A measure of how opaque a medium is to radiation. The optical depth is a function of the geometric depth and the vertical attenuation coefficient.

Optically shallow waters

An aquatic system where the spectral reflectance off the bottom contributes to radiance measured above the sea surface and is defined by the water clarity, bottom depth, and bottom composition.

Photosynthetically available radiation (PAR)

The integrated photon flux (photons per second per square meter) within the 400–700 nm wavelength range at the ocean surface. PAR is the total energy available to phytoplankton for photosynthesis and is reported in units of Q m−2 s−1, where Q is quanta, or in μE m−2 s−1, where E is Einsteins.

Radiance, L(λ)

The incremental amount of radiant energy per unit time (in Watts) incident on the sensor area (m−2) in a solid angle view (sr−1) per unit wavelength (λ) of light (nm−1). A satellite measures radiance.

Reflection

At the boundary of two different mediums with different indices of refraction, a certain amount of radiation is returned at an angle equal to the angle of incidence.

Refraction

The direction of light propagation changes, or is bent, at the boundary between two mediums with different indices of refraction. The refracted light bends toward the normal boundary when the index of refraction increases from one medium to another and away from the normal boundary when the index of refraction decreases from one medium to another.

Remote sensing reflectance, Rrs(λ)

A specialized ratio used for remote sensing purposes formulated as the ratio of the spectral water-leaving radiance, L w (λ), to the plane irradiance incident on the water, E d (λ). It represents the spectral distribution of sunlight penetrating the sea surface that is backscattered out again and potentially measured remotely. Theoretically, it is proportional to spectral backscattering b b (λ) and inversely proportional to absorption a(λ) of the surface water column.

Water-leaving radiance, Lw(λ)

The component of the radiance signal measured above the water consisting of photons that have penetrated the water column and been backscattered out through the air-sea interface. It does not include photons reflected off the sea surface, also called sun glint.

Bibliography

Primary Literature

  1. 1.
    Carr ME et al (2006) A comparison of global estimates of marine primary production from ocean color. Deep Sea Res Part II: Top Stud Oceanogr 53:741–770ADSCrossRefGoogle Scholar
  2. 2.
    Field CB, Behrenfeld MJ, Randerson JT, Falkowski P (1998) Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281:237–240ADSCrossRefGoogle Scholar
  3. 3.
    Smith RC, Baker KS (1978) Optical classification of natural waters. Limnol Oceanogr 23:260–267CrossRefGoogle Scholar
  4. 4.
    Martinez E, Antoine D, D’Ortenzio F, Gentili B (2009) Climate-driven basin-scale decadal oscillations of oceanic phytoplankton. Science 326:1253–1256ADSCrossRefGoogle Scholar
  5. 5.
    Siegel DA, Franz BA (2010) Century of phytoplankton change. Nature 466:569–570ADSCrossRefGoogle Scholar
  6. 6.
    Henson SA et al (2010) Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity. Biogeosciences 7:621–640ADSCrossRefGoogle Scholar
  7. 7.
    IOCCG (2008) Why ocean colour? the societal benefits of ocean-colour technology. In: Platt T, Hoepffner N, Stuart V, Brown C (eds) Reports of the International Ocean-Colour Coordinating Group, Dartmouth, CanadaGoogle Scholar
  8. 8.
    Morel A (1988) Optical modeling of the upper ocean in relation to its biogenous matter content (case I waters). J Geophys Res 93:10749–10768ADSCrossRefGoogle Scholar
  9. 9.
    Gordon HR, Morel AY (1983) Remote assessment of ocean color for interpretation of satellite visible imagery: a review. Springer, New YorkGoogle Scholar
  10. 10.
    O’Reilly JE, Maritorena S, Mitchell BG, Siegel DA (1998) Ocean color chlorophyll algorithms for SeaWiFS. J Geophys Res 103:24937–24953ADSCrossRefGoogle Scholar
  11. 11.
    McClain CR (2009) A decade of satellite ocean color observations*. Annu Rev Mar Sci 1:19–42MathSciNetADSCrossRefGoogle Scholar
  12. 12.
    Kirk JTO (1994) Light and photosynthesis in aquatic ecosystems. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  13. 13.
    Blough NV, Del Vecchio R (2002) Chromophoric DOM in the coastal environment. In: Hansell DA, Carlson CA (eds) Biogeochemistry of marine dissolved organic matter. Academic, San Diego, pp 509–546CrossRefGoogle Scholar
  14. 14.
    Twardowski MS, Boss E, Sullivan JM, Donaghay PL (2004) Modeling the spectral shape of absorption by chromophoric dissolved organic matter. Mar Chem 89:69–88CrossRefGoogle Scholar
  15. 15.
    Ciotti AM, Cullen JJ, Lewis MR (2002) Assessment of the relationships between dominant cell size in natural phytoplankton communities and the spectral shape of the absorption coefficient. Limnol Oceanogr 47:404–417CrossRefGoogle Scholar
  16. 16.
    Bricaud A, Claustre H, Ras J, Oubelkheir K (2004) Natural variability of phytoplanktonic absorption in oceanic waters: influence of the size structure of algal populations. J Geophys Res 109:C11010ADSCrossRefGoogle Scholar
  17. 17.
    Stramski D, Boss E, Bogucki D, Voss KJ (2004) The role of seawater constituents in light backscattering in the ocean. Prog Oceanogr 61:27–56ADSCrossRefGoogle Scholar
  18. 18.
    Mobley CD (1994) Light and water: radiative transfer in natural waters. Academic, San DiegoGoogle Scholar
  19. 19.
    Gordon HR et al (2009) Spectra of particulate backscattering in natural waters. Opt Express 17:16192–16208ADSCrossRefGoogle Scholar
  20. 20.
    Twardowski MS, Lewis M, Barnard A, Zaneveld JRV (2005) In-water instrumentation and platforms for ocean color remote sensing applications. In: Miller R, Del-Castillo C, McKeee D (eds) Remote sensing of coastal aquatic waters. Springer, DordrechtGoogle Scholar
  21. 21.
    Smith RC, Baker K (1978) The bio-optical state of ocean waters and remote sensing. Limnol Oceanogr 23:247–259CrossRefGoogle Scholar
  22. 22.
    Morel A, Gentilli B (1993) Diffuse reflectance of oceanic waters. II. Bidirectional aspects. Appl Opt 32:6864–6879ADSCrossRefGoogle Scholar
  23. 23.
    Lee ZP, Carder KL, Arnone RA (2002) Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep water. Appl Opt 41:5755–5772ADSCrossRefGoogle Scholar
  24. 24.
    Aurin DA (2010) Developing ocean color remote sensing algorithms for retrieving optical properties and biogeochemical parameters in the optically complex waters of Long Island Sound. Ph.D. Thesis. University of ConnecticutGoogle Scholar
  25. 25.
    Ryan JP et al (2005) Coastal ocean physics and red tides: an example from Monterey Bay, California. Oceanography 18:246–255CrossRefGoogle Scholar
  26. 26.
    Mouroulis P, Green RO, Wilson DW (2008) Optical design of a coastal ocean imaging spectrometer. Opt Express 16:9087–9096ADSCrossRefGoogle Scholar
  27. 27.
    Davis CO et al (2002) Ocean PHILLS hyperspectral imager: design, characterization, and calibration. Opt Express 10(4):210–221ADSCrossRefGoogle Scholar
  28. 28.
    McClain CR, Cleave ML, Feldman GC, Gregg WW (1998) Science quality SeaWiFS data for global biosphere research. Sea Technol 39:10–16Google Scholar
  29. 29.
    Gordon HR (1997) Atmospheric correction of ocean color imagery in the Earth Observing System era. J Geophys Res 102:17081–17106ADSCrossRefGoogle Scholar
  30. 30.
    Antoine D, Morel A (1999) A multiple scattering algorithm for atmospheric correction of remotely sensed ocean color (MERIS instrument): principle and implementation for atmospheres carrying various aerosols including absorbing ones. Int J Remote Sens 20:1875–1916CrossRefGoogle Scholar
  31. 31.
    Gao BC, Montes MJ, Ahmad Z, Davis CO (2000) Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space. Appl Opt 39:887–896ADSCrossRefGoogle Scholar
  32. 32.
    Wang M, Son SH, Shi W (2009) Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data. Remote Sens Environ 113:635–644CrossRefGoogle Scholar
  33. 33.
    Yan B et al (2002) Pitfalls in atmospheric correction of ocean color imagery: how should aerosol optical properties be computed? Appl Opt 41:412–423ADSCrossRefGoogle Scholar
  34. 34.
    Fukushima H, Toratani M (1997) Asian dust aerosol: optical effect on satellite ocean color signal and a scheme of its correction. J Geophys Res 102:17119–17130ADSCrossRefGoogle Scholar
  35. 35.
    Antoine D, Nobileau D (2006) Recent increase of Saharan dust transport over the Mediterranean Sea, as revealed from ocean color satellite (SeaWiFS) observations. J Geophys Res 111:D12214ADSCrossRefGoogle Scholar
  36. 36.
    Claustre H et al (2002) Is desert dust making oligotrophic waters greener? Geophys Res lett 29:107–1CrossRefGoogle Scholar
  37. 37.
    Paytan A et al (2009) Toxicity of atmospheric aerosols on marine phytoplankton. Proc Natl Acad Sci 106:4601ADSCrossRefGoogle Scholar
  38. 38.
    Garrison VH et al (2003) African and Asian dust: from desert soils to coral reefs. Bioscience 53:469–480CrossRefGoogle Scholar
  39. 39.
    Monahan EC, O’Muircheartaigh I (1981) Improved statement of the relationship between surface wind speed and oceanic whitecap coverage as required for the interpretation of satellite data. In: Gower JFR (ed) Oceanography from space. Plenum, New York, pp 751–755CrossRefGoogle Scholar
  40. 40.
    National Research Council Committee on Assessing Requirements for Sustained Ocean Color Research and Operations (2011) Assessing requirements for sustained ocean color research and operations. National Academies Press, Washington DCGoogle Scholar
  41. 41.
    Jerlov NG (1974) Optical aspects of oceanography. Academic, London, pp 77–94Google Scholar
  42. 42.
    Morel A, Prieur L (1977) Analysis of variations in ocean color. Limnol Oceanogr 22:709–721CrossRefGoogle Scholar
  43. 43.
    Mobley CD, Stramski D, Bissett WP, Boss E (2004) Optical modeling of ocean water: is the case 1 – case 2 classification still useful? Oceanography 17:60–67CrossRefGoogle Scholar
  44. 44.
    Morel A, Bricaud A (1981) Theoretical results concerning light absorption in a discrete medium and application to the specific absorption of phytoplankton. Deep-Sea Res 28:1357–1393CrossRefGoogle Scholar
  45. 45.
    Siegel DA, Maritorena S, Nelson NB, Behrenfeld MJ (2005) Independence and interdependencies among global ocean color properties: reassessing the bio-optical assumption. J Geophys Res 110:C07011ADSCrossRefGoogle Scholar
  46. 46.
    Swan CM, Siegel DA, Nelson NB, Carlson CA, Nasir E (2009) Biogeochemical and hydrographic controls on chromophoric dissolved organic matter distribution in the Pacific Ocean. Deep Sea Res Part I: Oceanogr Res Pap 56:2175–2192ADSCrossRefGoogle Scholar
  47. 47.
    Dierssen HM (2010) Benthic ecology from space: optics and net primary production in seagrass and benthic algae across the Great Bahama Bank. Mar Ecol Progress Ser 411:1–15CrossRefGoogle Scholar
  48. 48.
    Zaneveld JRV (1989) An asymptotic closure theory for irradiance in the sea and its inversion to obtain the inherent optical properties. Limnol Oceanogr 34:1442–1452CrossRefGoogle Scholar
  49. 49.
    Dierssen HM (2010) Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate. Proc Natl Acad Sci 107:17073ADSCrossRefGoogle Scholar
  50. 50.
    Moore TS, Campbell JW, Dowell MD (2009) A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product. Remote Sens Environ 113:2424–2430CrossRefGoogle Scholar
  51. 51.
    Schofield O et al (2004) Watercolors in the coastal zone: what can we see? Oceanography 17:25–31MathSciNetGoogle Scholar
  52. 52.
    Falkowski P et al (2000) The global carbon cycle: a test of our knowledge of earth as a system. Science 290:291ADSCrossRefGoogle Scholar
  53. 53.
    Behrenfeld MJ, Falkowski PG (1997) Consumers guide to phytoplankton primary productivity models. Limnol Oceanogr 42:1479–1491CrossRefGoogle Scholar
  54. 54.
    Campbell J et al (2002) Comparison of algorithms for estimating ocean primary production from surface chlorophyll, temperature, and irradiance. Glob Biogeochem Cycle 16:1035ADSCrossRefGoogle Scholar
  55. 55.
    Westberry T, Behrenfeld MJ, Siegel DA, Boss E (2008) Carbon-based primary productivity modeling with vertically resolved photoacclimation. Glob Biogeochem Cycle 22:GB2024ADSCrossRefGoogle Scholar
  56. 56.
    Mouw CB, Yoder JA (2005) Primary production calculations in the Mid-Atlantic Bight, including effects of phytoplankton community size structure. Limnol oceanogr 50(4):1232–1243CrossRefGoogle Scholar
  57. 57.
    IOCCG (2006) Remote sensing of inherent optical properties: fundamentals, tests of algorithms, and applications. In: Lee ZP (ed) Reports of the International Ocean-Colour Coordinating Group, DartmouthGoogle Scholar
  58. 58.
    Siegel DA, Maritorena S, Nelson NB, Hansell DA, Lorenzi-Kayser M (2002) Global distribution and dynamics of colored dissolved and detrital organic materials. J Geophys Res 107:3228CrossRefGoogle Scholar
  59. 59.
    U.S. National Aeronautics and Space Administration, Goddard Earth Sciences, Data and Information Services Center (2011) Giovanni. http://disc.sci.gsfc.nasa.gov/giovanni/
  60. 60.
    Behrenfeld MJ et al (2009) Satellite-detected fluorescence reveals global physiology of ocean phytoplankton. Biogeosciences 6:779–794ADSCrossRefGoogle Scholar
  61. 61.
    Dierssen HM, Kudela RM, Ryan JP, Zimmerman RC (2006) Red and black tides: quantitative analysis of water-leaving radiance and perceived color for phytoplankton, colored dissolved organic matter, and suspended sediments. Limnol oceanogr 51:2646–2659CrossRefGoogle Scholar
  62. 62.
    Brewin RJW et al (2011) An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing. Remote Sens Environ 115:325–339CrossRefGoogle Scholar
  63. 63.
    Balch WM, Kilpatrick KA, Trees CC (1996) The 1991 coccolithophore bloom in the central North Atlantic. 1. Optical properties and factors affecting their distribution. Limnol Oceanogr 41:1669–1683CrossRefGoogle Scholar
  64. 64.
    Tomlinson MC, Wynne TT, Stumpf RP (2009) An evaluation of remote sensing techniques for enhanced detection of the toxic dinoflagellate, Karenia brevis. Remote Sens Environ 113:598–609CrossRefGoogle Scholar
  65. 65.
    Simis SGH, Peters SWM, Gons HJ (2005) Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water. Limnol Oceanogr 50:237–245CrossRefGoogle Scholar
  66. 66.
    Chavez FP, Ryan J, Lluch-Cota SE, Niquen CM (2003) From anchovies to sardines and back: multidecadal change in the Pacific Ocean. Science 299:217–221ADSCrossRefGoogle Scholar
  67. 67.
    Platt T, Csar Fuentes-Yaco KTF (2003) Marine ecology: spring algal bloom and larval fish survival. Nature 423:398–399ADSCrossRefGoogle Scholar
  68. 68.
    Fuentes-Yaco C, Koeller PA, Sathyendranath S, Platt T (2007) Shrimp (Pandalus borealis) growth and timing of the spring phytoplankton bloom on the Newfoundland–Labrador Shelf. Fish Oceanogr 16:116–129CrossRefGoogle Scholar
  69. 69.
    Seibel BA, Dierssen HM (2003) Cascading trophic impacts of reduced biomass in the Ross Sea, Antarctica: just the tip of the iceberg? Biol Bull 205:93–97CrossRefGoogle Scholar
  70. 70.
    Rosa R, Dierssen HM, Gonzalez L, Seibel BA (2008) Large-scale diversity patterns of cephalopods in the Atlantic open ocean and deep sea. Ecology 89:3449–3461CrossRefGoogle Scholar
  71. 71.
    Dekker A et al (2005) Remote sensing of seagrass ecosystems: use of spaceborne and airborne sensors. In: Larkum AWD, Orth RJ, Duarte CM (eds) Seagrasses: biology, ecology, and conservation. Springer, Dordrecht, pp 347–359Google Scholar
  72. 72.
    Cavanaugh KC, Siegel DA, Kinlan BP, Reed DC (2010) Scaling giant kelp field measurements to regional scales using satellite observations. Mar Ecol Prog Ser 403:13–27CrossRefGoogle Scholar
  73. 73.
    Phinn S, Roelfsema C, Dekker A, Brando V, Anstee J (2008) Mapping seagrass species, cover and biomass in shallow waters: an assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia). Remote sens Environ 112:3413–3425CrossRefGoogle Scholar
  74. 74.
    Lesser MP, Mobley CD (2007) Bathymetry, water optical properties, and benthic classification of coral reefs using hyperspectral remote sensing imagery. Coral Reefs 26:819–829ADSCrossRefGoogle Scholar
  75. 75.
    Dierssen HM, Zimmerman RC, Drake LA, Burdige DJ (2009) Potential export of unattached benthic macroalgae to the deep sea through wind-driven Langmuir circulation. Geophys Res Lett 36:L04602CrossRefGoogle Scholar
  76. 76.
    Burdige DJ, Hu X, Zimmerman RC (2010) The widespread occurrence of coupled carbonate dissolution/reprecipitation in surface sediments on the Bahamas Bank. Am J Sci 310(6):492–521. doi:10.2475/06.2010.03Google Scholar
  77. 77.
    Goes JI, Thoppil PG, Gomes HR, Fasullo JT (2005) Warming of the Eurasian landmass is making the Arabian Sea more productive. Science 308:545–547ADSCrossRefGoogle Scholar
  78. 78.
    Beman JM, Arrigo KR, Matson PA (2005) Agricultural runoff fuels large phytoplankton blooms in vulnerable areas of the ocean. Nature 434:211–214ADSCrossRefGoogle Scholar
  79. 79.
    Dwivedi RM, Solanki HU, Nayak SR, Gulati D, Somvanshi VS (2005) Exploration of fishery resources through integration of ocean colour with sea surface temperature: Indian experience. IJMS 34:430–440Google Scholar
  80. 80.
    Chavez FP, Strutton PG, McPhaden MJ (1998) Biological-physical coupling in the Central Equatorial Pacific during the onset of the 1997–98 El Nino. Geophys Res Lett 25:3543–3546ADSCrossRefGoogle Scholar
  81. 81.
    Lewis MR, Platt TC (1987) Remote observation of ocean colour for prediction of upper ocean heating rates. Adv Space Res 7:127–130ADSCrossRefGoogle Scholar
  82. 82.
    Hill VJ (2008) Impacts of chromophoric dissolved organic material on surface ocean heating in the Chukchi Sea. J Geophys Res 113:C07024CrossRefGoogle Scholar
  83. 83.
    Gnanadesikan A, Anderson WG (2009) Ocean water clarity and the ocean general circulation in a coupled climate model. J Phys Oceanogr 39:314–332ADSCrossRefGoogle Scholar
  84. 84.
    Gnanadesikan A, Emanuel K, Vecchi GA, Anderson WG, Hallberg R (2010) How ocean color can steer Pacific tropical cyclones. Geophys Res Lett 37:L18802ADSCrossRefGoogle Scholar
  85. 85.
    Miller WL, Moran MA (1997) Interaction of photochemical and microbial processes in the degradation of refractory dissolved organic matter from a coastal marine environment. Limnol Oceanogr 42:1317–1324CrossRefGoogle Scholar
  86. 86.
    Ackleson SG, Balch WM, Holligan PM (1994) Response of water-leaving radiance to particulate calcite and chlorophyll a concentrations: a model for Gulf of Maine coccolithophore blooms. J Geophys Res 99:7483–7499ADSCrossRefGoogle Scholar
  87. 87.
    Gordon HR et al (2001) Retrieval of coccolithophore calcite concentration from SeaWiFS imagery. Geophys Res Lett 28:1587–1590ADSCrossRefGoogle Scholar
  88. 88.
    Balch W, Drapeau D, Bowler B, Booth E (2007) Prediction of pelagic calcification rates using satellite measurements. Deep Sea Res Part II: Top Stud Oceanogr 54:478–495ADSCrossRefGoogle Scholar
  89. 89.
    Balch WM, Fabry VJ (2008) Ocean acidification: documenting its impact on calcifying phytoplankton at basin scales. Mar Ecol Prog Ser 373:239–247CrossRefGoogle Scholar
  90. 90.
    Ryther JH (1969) Photosynthesis and fish production in the sea. Science 166:72–76ADSCrossRefGoogle Scholar
  91. 91.
    Wilson RW et al (2009) Contribution of fish to the marine inorganic carbon cycle. Science 323:359–362ADSCrossRefGoogle Scholar
  92. 92.
    Platt T, Sathyendranath S, Fuentes-Yaco C (2007) Biological oceanography and fisheries management: perspective after 10 years. ICES J Marine Sci 64:863CrossRefGoogle Scholar
  93. 93.
    Platt T, Sathyendranath S (2008) Ecological indicators for the pelagic zone of the ocean from remote sensing. Remote Sens Environ 112:3426–3436CrossRefGoogle Scholar
  94. 94.
    Stumpf RP et al (2003) Monitoring Karenia brevis blooms in the Gulf of Mexico using satellite ocean color imagery and other data. Harmful Algae 2:147–160CrossRefGoogle Scholar
  95. 95.
    Hu C et al (2003) MODIS detects oil spills in Lake Maracaibo, Venezuela. Eos AGU Trans 84:313–319ADSCrossRefGoogle Scholar
  96. 96.
    Fingas M, Brown C (2000) A review of the status of advanced technologies for the detection of oil in and with ice. Spill Sci Technol Bull 6:295–302CrossRefGoogle Scholar
  97. 97.
    Boland RC, Donohue MJ (2003) Marine debris accumulation in the nearshore marine habitat of the endangered Hawaiian monk seal, Monachus schauinslandi 1999–2001. Mar Pollut Bull 46:1385–1394CrossRefGoogle Scholar
  98. 98.
    Donohue MJ, Boland RC, Sramek CM, Antonelis GA (2001) Derelict fishing gear in the Northwestern Hawaiian Islands: diving surveys and debris removal in 1999 confirm threat to coral reef ecosystems. Mar Pollut Bull 42:1301–1312CrossRefGoogle Scholar
  99. 99.
    Randolph K et al (2008) Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin. Remote Sens Environ 112:4009–4019CrossRefGoogle Scholar
  100. 100.
    IOCCG (2007) Ocean-colour data merging In: Gregg W (ed) Reports of the International Ocean-Colour Coordinating Group, DartmouthGoogle Scholar
  101. 101.
    Roy S, Llewellyn C, Egeland ES, Johnsen G (2011) Phytoplankton pigments: updates on characterization, chemotaxonomy and applications in oceanography. Cambridge University Press. Cambridge Environmental Chemistry Series. Cambridge, UK. pp 845. ISBN: 978110700066-7Google Scholar

Books and Reviews

  1. Campbell J, Antoine D, Armstrong R, Arrigo K, Balch W, Barber R, Behrenfeld M, Bidigare R, Bishop J, Carr ME et al (2002) Comparison of algorithms for estimating ocean primary production from surface chlorophyll, temperature, and irradiance. Glob Biogeochem Cycle 16:1035ADSCrossRefGoogle Scholar
  2. Carr ME, Friedrichs MAM, Schmeltz M, Noguchi Aita M, Antoine D, Arrigo KR, Asanuma I, Aumont O, Barber R, Behrenfeld M et al (2006) A comparison of global estimates of marine primary production from ocean color. Deep Sea ResPart II: Top Stud Oceanogr 53:741–770ADSCrossRefGoogle Scholar
  3. GlobCOLOUR: An EO based service supporting global ocean carbon cycle research. European Space Agency. http://www.globcolour.info/
  4. IOCCG. Reports of the International Ocean-Colour Coordinating Group No. 1–10. Dartmouth. http://www.ioccg.org/reports_ioccg.html
  5. Jerlov NG, Nielsen ES (eds) (1974) Optical aspects of oceanography. Academic, LondonGoogle Scholar
  6. Miller R, Del-Castillo C, McKee BA (eds) (2005) Remote sensing of coastal aquatic waters. Springer, DordrechtGoogle Scholar
  7. Morel A (1991) Optics of marine particles and marine optics. In: Demers S (ed) Particle analysis in oceanography. Springer, Berlin, pp 141–188CrossRefGoogle Scholar
  8. Morel A, Bricaud A (1986) Inherent optical properties of algal cells including picoplankton: theoretical and experimental results. Can Bull Fish Aquat Sci 214:521–559Google Scholar
  9. National Aeronautics and Space Administration (NASA) Ocean optics protocols for satellite ocean color sensor validation, vol I–VI. http://oceancolor.gsfc.nasa.gov/DOCS/
  10. National Aeronautics and Space Administration (NASA) Ocean color web. http://oceancolor.gsfc.nasa.gov/
  11. Platt T, Nayak S (eds) (2005). Special issue on: ocean colour remote sensing. Indian J Marine Sci 34(4):341–355Google Scholar
  12. Siegel D (2004) Views of ocean processes from the sea-viewing wide field- of-view sensor mission: introduction to the first special issue. Deep Sea Res Part II Top Stud Oceanogr 51(1–3):1–3. http://dx.doi.org/10.1016/j.dsr2.2003.12.001Google Scholar
  13. The Oceanography Society (2004) Special issue: coastal ocean optics and dynamics. Oceanography 17(2):1–95Google Scholar
  14. Thomas A, Siegel D, Marra J (2004) Views of ocean processes from the sea-viewing wide field- of-view sensor (SeaWiFS) mission: introduction to the second special issue. Deep Sea Res Part II Top Stud Oceanogr 51(10–11):911–912. http://dx.doi.org/10.1016/j.dsr2.2004.06.003Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Marine SciencesUniversity of ConnecticutGrotonUSA

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