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Ocean Science Journal

, Volume 53, Issue 3, pp 475–485 | Cite as

Improved Chlorophyll-a Algorithm for the Satellite Ocean Color Data in the Northern Bering Sea and Southern Chukchi Sea

  • Sang Heon Lee
  • Jongseong Ryu
  • Jung-woo Park
  • Dabin Lee
  • Jae-Il Kwon
  • Jingping Zhao
  • SeungHyun SonEmail author
Article

Abstract

The Bering and Chukchi seas are an important conduit to the Arctic Ocean and are reported to be one of the most productive regions in the world’s oceans in terms of high primary productivity that sustains large numbers of fishes, marine mammals, and sea birds as well as benthic animals. Climate-induced changes in primary production and production at higher trophic levels also have been observed in the northern Bering and Chukchi seas. Satellite ocean color observations could enable the monitoring of relatively long term patterns in chlorophyll-a (Chl-a) concentrations that would serve as an indicator of phytoplankton biomass. The performance of existing global and regional Chl-a algorithms for satellite ocean color data was investigated in the northeastern Bering Sea and southern Chukchi Sea using in situ optical measurements from the Healy 2007 cruise. The model-derived Chl-a data using the previous Chl-a algorithms present striking uncertainties regarding Chl-a concentrations – for example, overestimation in lower Chl-a concentrations or systematic overestimation in the northeastern Bering Sea and southern Chukchi Sea. Accordingly, a simple two band ratio (Rrs(443)/Rrs(555)) algorithm of Chl-a for the satellite ocean color data was devised for the northeastern Bering Sea and southern Chukchi Sea. The MODIS-derived Chl-a data from July 2002 to December 2014 were produced using the new Chl-a algorithm to investigate the seasonal and interannual variations of Chl-a in the northern Bering Sea and the southern Chukchi Sea. The seasonal distribution of Chl-a shows that the highest (spring bloom) Chl-a concentrations are in May and the lowest are in July in the overall area. Chl-a concentrations relatively decreased in June, particularly in the open ocean waters of the Bering Sea. The Chl-a concentrations start to increase again in August and become quite high in September. In October, Chl-a concentrations decreased in the western area of the Study area and the Alaskan coastal waters. Strong interannual variations are shown in Chl-a concentrations in all areas. There is a slightly increasing trend in Chl-a concentrations in the northern Bering Strait (SECS). This increasing trend may be related to recent increases in the extent and duration of open waters due to the early break up of sea ice and the late formation of sea ice in the Chukchi Sea.

Keywords

chlorophyll-a ocean color remote sensing Bering sea Chukchi sea 

References

  1. Bailey S, Franz B, Werdell PJ (2010) Estimation of near-infrared waterleaving reflectance for satellite ocean color data processing. Opt Express 18:7521–7527CrossRefGoogle Scholar
  2. Bluhm BA Gradinger R (2008) Regional variability in food availability for Arctic marine mammals. Ecol Appl 18:S77–S96Google Scholar
  3. Coachman LK, Aagaard K, Tripp RB (1975) Bering strait: the regional physical oceanography. University of Washington Press, Seattle, 172 pGoogle Scholar
  4. Cota GF, Wang J, Comiso JC (2004) Transformation of global satellite chlorophyll retrievals with a regionally tuned algorithm. Remote Sens Environ 90:373–377CrossRefGoogle Scholar
  5. Frey KE, Maslanik JA, Clement Kinney J, Maslowski W (2014) Recent variability in sea ice cover, age, and thickness in the Pacific Arctic region. In: Grebmeier JM, Maslowski W (eds) The Pacific Arctic region: ecosystem status and trends in a rapidly changing environment. Springer, Dordrecht, pp 31–64Google Scholar
  6. Frey KE, Moore GWK, Cooper LW, Grebmeier JM (2015) Divergent patterns of recent sea ice cover across the Bering, Chukchi, and Beaufort seas of the Pacific Arctic Region. Prog Oceanogr 136:32–49CrossRefGoogle Scholar
  7. Garver SA, Siegel DA (1997) Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation. 1. Time series from the Sargasso Sea. J Geophys Res 102:18607–18625Google Scholar
  8. Grebmeier JM, McRoy CP (1989) Pelagic-benthic coupling on the shelf of the northern Bering and Chukchi Seas. III Benthic food supply and carbon cycling. Mar Ecol-Prog Ser 53:79–91Google Scholar
  9. Grebmeier JM, Cooper LW, Feder HM, Sirenko BI (2006a) Ecosystem dynamics of the Pacific-influenced Northern Bering and Chukchi seas in the Amerasian Arctic. Prog Oceanogr 71:331–361CrossRefGoogle Scholar
  10. Grebmeier JM, Overland JE, Moore SE, Farley EV, Carmack EC, Cooper LW, Frey KE, Helle JH, McLaughlin FA, McNutt SL (2006b) A major ecosystem shift in the northern Bering Sea. Science 311:1461–1464CrossRefGoogle Scholar
  11. Grebmeier JM, Moore SE, Overland JE, Frey KE, Gradinger R (2010) Biological response to recent Pacific Arctic sea ice retreats. Eos T Am Geophys Un 91:161–168CrossRefGoogle Scholar
  12. Grebmeier JM (2012) Shifting patterns of life in the Pacific Arctic and Sub-Arctic seas. Ann Rev Mar Sci 4:63–78CrossRefGoogle Scholar
  13. Grebmeier JM, Bluhm BA, Cooper LW, Danielson SL, Arrigo KR, Blanchard AL, Clarke JT, Day RH, Frey KE, Gradinger RR, Kędra M, Konar B, Kuletz KJ, Lee SH, Lovvorn JR, Norcross BL, Okkonen SR (2015) Ecosystem characteristics and processes facilitating persistent microbenthic biomass hotspots and associated benthivory in the Pacific Arctic. Prog Oceanogr 136:92–114. doi:10.1016/j.pocean.2015.05.006CrossRefGoogle Scholar
  14. Hirawake T, Shinmyo K, Fujiwara A, Saitoh S (2012) Satellite remote sensing of primary productivity in the Bering and Chukchi Seas using an absorption-based approach. ICES J Mar Sci 69:1194–1204CrossRefGoogle Scholar
  15. Lee SH, Whitledge TE, Kang SH (2007) Recent carbon and nitrogen uptake rates of phytoplankton in Bering Strait and the Chukchi Sea. Cont Shelf Res 27:2231–2249CrossRefGoogle Scholar
  16. Lee SH, Joo HM, Yun MS, Whitledge TE (2012) Recent phytoplankton productivity of the northern Bering Sea during early summer in 2007. Polar Biol 35:83–98CrossRefGoogle Scholar
  17. Maritorena S, Siegel DA, Peterson A (2002) Optimization of a semi-analytical ocean color model for global scale applications. Appl Optics 41(15):2705–2714CrossRefGoogle Scholar
  18. Matsuoka A, Huot Y, Shimada K, Saitoh S, Babin M (2007) Biooptical characteristics of the western Arctic Ocean: implications for ocean color algorithms. Can J Remote Sens 33(6):503–518CrossRefGoogle Scholar
  19. Matsuoka A, Hill V, Huot Y, Babin M, Bricaud A (2011) Seasonal variability in the light absorption properties of western Arctic waters: parameterization of the individual components of absorption for ocean color applications. J Geophys Res 116:C02007CrossRefGoogle Scholar
  20. Mitchell BG (1992) Predictive bio-optical relationships for polar oceans and marginal ice zones. J Marine Syst 3:91–105CrossRefGoogle Scholar
  21. Morel A, Gentili B (1991) Diffuse reflectance of oceanic waters: its dependence of sun angle as influenced by the molecular scattering contribution. Appl Optics 30:4427–4438CrossRefGoogle Scholar
  22. O’Reilly JE, Maritorena S, Mitchell BG, Siegel DA, Carder KL, Garver SA, Kahru M, McClain CR (1998) Ocean color chlorophyll algorithms for SeaWiFS. J Geophs Res 103:24937–24953CrossRefGoogle Scholar
  23. Overland JE, Stabeno PJ (2004) Is the climate of the Bering Sea warming and affecting the ecosystem? Eos T Am Geophys Un 85:309–316CrossRefGoogle Scholar
  24. Sambrotto RN, Goering JJ, McRoy CP (1984) Large yearly production of phytoplankton in the western Bering Strait. Science 225:1147–1150CrossRefGoogle Scholar
  25. Serreze MC, Holland MM, Stroeve J (2007) Perspectives on the Arctic’s shrinking sea-ice cover. Science 315:1533–1536CrossRefGoogle Scholar
  26. Springer AM, Murphy EC, Roseneau DG, McRoy CP, Cooper BA (1987) The paradox of pelagic food webs in the northern Bering Sea: I. Seabird food habits. Cont Shelf Res 7:895–911CrossRefGoogle Scholar
  27. Springer AM, McRoy CP (1993) The paradox of pelagic food webs in the northern Bering Sea-III. Patterns of primary production. Cont Shelf Res 13:575–599Google Scholar
  28. Stumpf R, Arnone R, Gould R, Martinolich P, Ransibrahmanakul V (2003) A partially coupled ocean-atmosphere model for retrieval of water leaving radiance from SeaWiFS in coastal waters. In: Hooker SB, Firestone ER (eds) Algorithm updates for the fourth SeaWiFS data processing. NASA Goddard Space Flight Center, Greenbelt, pp 51–59Google Scholar
  29. Wang J, Cota GF (2003) Remote-sensing reflectance in the Beaufort and Chukchi seas: observations and models. Appl Optics 2(15):2754–2765CrossRefGoogle Scholar
  30. Wang J, Cota GF, Comiso JC (2005) Phytoplankton in the Beaufort and Chukchi seas: distribution, dynamics, and environmental forcing. Deep-Sea Res Pt II 52:3355–3368CrossRefGoogle Scholar

Copyright information

© Korea Institute of Ocean Science & Technology (KIOST) and the Korean Society of Oceanography (KSO) and Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Sang Heon Lee
    • 1
  • Jongseong Ryu
    • 2
  • Jung-woo Park
    • 3
  • Dabin Lee
    • 1
  • Jae-Il Kwon
    • 4
  • Jingping Zhao
    • 5
  • SeungHyun Son
    • 6
    Email author
  1. 1.Department of Oceanography, College of Natural SciencesPusan National UniversityBusanKorea
  2. 2.Department of Marine BiotechnologyAnyang UniversityIncheonKorea
  3. 3.Graduate School of Fisheries SciencesHokkaido UniversityHakodate, HokkaidoJapan
  4. 4.Operational Oceanography Research CenterKIOSTBusanKorea
  5. 5.Department of Physical and Environmental OceanographyOcean University of ChinaQingdaoChina
  6. 6.CIRAColorado State UniversityFort CollinsUSA

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