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

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 (ag(440)). Assuming that remote-sensing reflectance (Rrs) could be retrieved accurately, empirical algorithms for T(Chl) (regionally tuned Tassan's Chl-a algorithm) in case-1 waters (T(Chl)2i in case-2 waters), T(TSM) (regionally tuned Tassan's TSM algorithm), and T(ag440) or C(ag440) (regionally tuned Tassan's or Carder's ag(440) algorithm) were able to retrieve Chl-a, TSM, and ag(440) with uncertainties as high as 35, 46, and 35%, respectively. Applying the standard SeaWiFS Rrs, T(Chl) was not viable in the eastern part of the YECS, which was associated with an inaccurate SeaWiFS Rrs retrieval because of improper atmospheric correction. T(Chl) 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 T(Chl) for data with SeaWiFS normalized water-leaving radiance at 555 nm (nLw(555)) > 2 mW cm(-2) mu m(-1) sr(-1) (T(Chl)2s). Finally, with standard SeaWiFS Rrs, we recommend switching algorithms from T(Chl)2s (for case-2 waters) to MOC(Chl) (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 ag(440) using SeaWiFS Rrs, we recommend empirical algorithms for T(TSM) (pre-SeaWiFS-modified form) and MT(ag440) or MC(ag440) (SeaWiFS Rrs-modified forms of T(ag440) or C(ag440)). These could retrieve with uncertainties as high as 82 and 52%, respectively.