Journal of Oceanography

, 67:627 | Cite as

Empirical ocean-color algorithms to retrieve chlorophyll-a, total suspended matter, and colored dissolved organic matter absorption coefficient in the Yellow and East China Seas

  • Eko Siswanto
  • Junwu Tang
  • Hisashi Yamaguchi
  • Yu-Hwan Ahn
  • Joji Ishizaka
  • Sinjae Yoo
  • Sang-Woo Kim
  • Yoko Kiyomoto
  • Keiko Yamada
  • Connie Chiang
  • Hiroshi Kawamura
Original Article

Abstract

A bio-optical dataset collected during the 1998–2007 period in the Yellow and East China Seas (YECS) was used to provide alternative empirical ocean-color algorithms in the retrieval of chlorophyll-a (Chl-a), total suspended matter (TSM), and colored dissolved organic matter (CDOM) absorption coefficients at 440 nm (ag440). Assuming that remote-sensing reflectance (Rrs) could be retrieved accurately, empirical algorithms for TChl (regionally tuned Tassan’s Chl-a algorithm) in case-1 waters (TChl2i in case-2 waters), TTSM (regionally tuned Tassan’s TSM algorithm), and Tag440 or Cag440 (regionally tuned Tassan’s or Carder’s ag440 algorithm) were able to retrieve Chl-a, TSM, and ag440 with uncertainties as high as 35, 46, and 35%, respectively. Applying the standard SeaWiFS Rrs, TChl was not viable in the eastern part of the YECS, which was associated with an inaccurate SeaWiFS Rrs retrieval because of improper atmospheric correction. TChl behaved better than other algorithms in the turbid case-2 waters, although overestimation was still observed. To retrieve more reliable Chl-a estimates with standard SeaWiFS Rrs in turbid water (a proxy for case-2 waters), we modified TChl for data with SeaWiFS normalized water-leaving radiance at 555 nm (nLw555) > 2 mW cm−2 μm−1 sr−1 (TChl2s). Finally, with standard SeaWiFS Rrs, we recommend switching algorithms from TChl2s (for case-2 waters) to MOCChl (SeaWiFS-modified NASA OC4v4 standard algorithm for case-1 waters) for retrieving Chl-a, which resulted in uncertainties as high as 49%. To retrieve TSM and ag440 using SeaWiFS Rrs, we recommend empirical algorithms for TTSM (pre-SeaWiFS-modified form) and MTag440 or MCag440 (SeaWiFS Rrs-modified forms of Tag440 or Cag440). These could retrieve with uncertainties as high as 82 and 52%, respectively.

Keywords

Chlorophyll-a Suspended sediment CDOM Remote-sensing reflectance 

Abbreviation

Bio-optic and radiometric variables

Chl-a

Chlorophyll-a (mg m−3)

TSM

Total suspended matter (mg l−1)

CDOM

Colored dissolved organic matter

ag440

CDOM absorption coefficient (m−1) at 440 nm

Rrs

Remote-sensing reflectance (sr−1)

nLw

Normalized water-leaving radiance (mW cm−2 μm−1 sr−1)

Ocean-color algorithms (based on in situ Rrs or accurate Rrs retrieval)

TChl-O

Original Tassan’s (1994) Chl-a algorithm

TChl

Regionally tuned Tassan’s (1994) Chl-a algorithm

OC4v4

NASA Chl-a standard algorithm

OCChl

Regionally tuned OC4v4

TChl2i

TChl optimized only for in situ nLw555 > 2 mW cm−2 μm−1 sr−1

TChl_TChl2i

Switching Chl-a algorithms: TChl for nLw555 ≤ 2 mW cm−2 μm−1 sr−1, TChl2i for nLw555 > 2 mW cm−2 μm−1 sr−1

TTSM-O

Original Tassan’s (1994) TSM algorithm

TTSM

Regionally tuned Tassan’s (1994) TSM algorithm

CSeaTSM

TSM algorithm implemented in SeaDAS (SeaWiFS Data Analysis System)

CTSM

Regionally tuned CSeaTSM

Tag440-O

Original Tassan’s (1994) ag440 algorithm

Tag440

Regionally tuned Tassan’s (1994) ag440 algorithm

Cag440

Regionally tuned Carder et al.’s (2003) ag440 algorithm

Ocean-color algorithms (based on standard SeaWiFS Rrs)

MTChl

Modified TChl based on standard SeaWiFS Rrs

MOCChl

Modified OCChl based on standard SeaWiFS Rrs

TChl2 s

Same as TChl2i except with standard SeaWiFS nLw555

MOCChl_TChl2s

Switched Chl-a algorithms: MOCChl for SeaWiFS nLw555 ≤ 2 mW cm−2 μm−1 sr−1, TChl2s for SeaWiFS nLw555 > 2 mW cm−2 μm−1 sr−1

MTTSM

Modified TTSM based on standard SeaWiFS Rrs

MTag440

Modified Tag440 based on standard SeaWiFS Rrs

MCag440

Modified Cag440 based on standard SeaWiFS Rrs

Notes

Acknowledgments

This work was supported by the United Nations Development Programme (UNDP)/Global Environment Facility (GEF) Yellow Sea Large Marine Ecosystem (YSLME) Project. We would like to thank the SeaWiFS Project (code 970.2) and the Goddard Earth Sciences Data and Information Services Center/Distributed Active Archive Center (code 902) at the Goddard Space Flight Center, Greenbelt, MD, 20771, for the production and distribution of the SeaWiFS data, respectively.

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Copyright information

© The Oceanographic Society of Japan and Springer 2011

Authors and Affiliations

  • Eko Siswanto
    • 1
  • Junwu Tang
    • 3
  • Hisashi Yamaguchi
    • 10
  • Yu-Hwan Ahn
    • 4
  • Joji Ishizaka
    • 2
  • Sinjae Yoo
    • 4
  • Sang-Woo Kim
    • 5
  • Yoko Kiyomoto
    • 6
  • Keiko Yamada
    • 7
  • Connie Chiang
    • 8
  • Hiroshi Kawamura
    • 9
  1. 1.Institute of Geospatial Science and Technology (INSTeG)Universiti Teknologi MalaysiaJohor BahruMalaysia
  2. 2.Hydrospheric Atmospheric Research CenterNagoya UniversityNagoyaJapan
  3. 3.National Ocean Technology CenterTianjinChina
  4. 4.Korean Ocean Research and Development InstituteSeoulKorea
  5. 5.National Fisheries Research and Development InstituteBusanKorea
  6. 6.Seikai National Fisheries Research InstituteNagasakiJapan
  7. 7.Department of Global Environment College of EnvironmentKeimyung UniversityDaeguKorea
  8. 8.Yellow Sea Large Marine Ecosystem ProjectSeoulKorea
  9. 9.Graduated School of ScienceTohoku UniversitySendaiJapan
  10. 10.Earth Observation Research Center, Japan Aerospace Exploration AgencyTsukubaJapan

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