Abstract
Abstract. Chlorophyll a (Chl) concentrations derived from satellite measurements have been used in oceanographic research, for example to interpret eco-responses to environmental changes on global and regional scales. However, it is unclear how existing Chl products compare with each other in terms of accuracy and consistency in revealing temporal and spatial patterns, especially in the optically complex marginal seas. In this study, we examined three MODIS (Moderate Resolution Imaging Spectroradiometer) Chl data products that have been made available to the community by the US National Aeronautics and Space Administration (NASA) using community-accepted algorithms and default parameterization. These included the products derived from the OC3M (ocean chlorophyll three-band algorithm for MODIS), GSM (Garver–Siegel–Maritorena model) and GIOP (generalized inherent optical properties) algorithms. We compared their temporal variations and spatial distributions in the northern South China Sea. We found that the three products appeared to capture general features such as unique winter peaks at the Southeast Asian Time-series Study station (SEATS, 18° N, 116° E) and the Pearl River plume associated blooms in summer. Their absolute magnitudes, however, may be questionable in the coastal zones. Additional error statistics using field measured Chl as the truth demonstrated that the three MODIS Chl products may contain high degree of uncertainties in the study region. Root mean square error (RMSE) of the products from OC3M and GSM (on a log scale) was about 0.4 and average percentage error (ε) was ~ 115% (Chl between 0.05–10.41 mg m−3, n = 114). GIOP with default parameterization led to higher errors (ε = 329%). An attempt to tune the algorithms based on a local coastal-water bio-optical data set led to reduced errors for Chl retrievals, indicating the importance of local tuning of globally-optimized algorithms. Overall, this study points to the need of continuous improvements for algorithm development and parameterization for the coastal zones of the study region, where quantitative interpretation of the current Chl products requires extra caution.
Highlights
IntroductionChlorophyll a (Chl) a is the primary phytoplankton pigment for photosynthesis, whose concentration (hereafter abbreviated as Chl, mg m−3) has been commonly used as a phytoplankton biomass index by oceanographers
Chlorophyll a (Chl) a is the primary phytoplankton pigment for photosynthesis, whose concentration has been commonly used as a phytoplankton biomass index by oceanographers
All three Chl products showed similar seasonality and spatial distri-25 butions: (1) Chl is lower in spring and fall than in summer and winter; (2) Chl is lower in the offshore South China Sea (SCS) (< 0.1 –26 ∼ 0.1 mg m−3) than in nearshore waters (∼ 1 – > 1 mg m−3);27 and (3) there is a large patch of elevated Chl in and to the28 west of the Luzon Strait in winter
Summary
Chl a is the primary phytoplankton pigment for photosynthesis, whose concentration (hereafter abbreviated as Chl, mg m−3) has been commonly used as a phytoplankton biomass index by oceanographers. Over the past three decades, an unprecedented view of the spatiotemporal pattern of Chl in the global ocean has been enabled by ocean color satellites such as CZCS (Coastal Zone Color Scanner), SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) and MODIS (Moderate Resolution Imaging Spectroradiometer) (McClain, 2009). The difference lies solely in the assumptions and parameterizations when modeling IOPs. For example, GSM uses fixed constants for the chlorophyllspecific phytoplankton pigment absorption coefficients (ap∗h) while the default GIOP uses ap∗h as a function of Chl (Bricaud et al, 1995). The inputs to the OC3M are Rrs at 443, 488, Empirical Regression
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