Picophytoplankton are essential components of phytoplankton in the oligotrophic South China Sea (SCS). Understanding the variation in the picophytoplankton community structure will provide important information about primary production and the biogeochemical cycling of carbon in the SCS. Based on a field dataset from the SCS, we developed empirical algorithms using the absorption coefficient of phytoplankton at 443 nm [aph(443)] as the input to estimate the cell abundances of picophytoplankton, including Prochlorococcus (Pro), Synechococcus (Syn), and autotrophic picoeukaryotes (PE), in the SCS. Evaluation of algorithm performances demonstrated good agreement with field measurements. The root mean square errors and the mean absolute errors (MAEs) between the algorithm derivations and measurements were 0.44, 0.35, and 0.29, and 2.67, 2.02, and 1.88 for the cell abundances of Pro, Syn, and PE, respectively. The match-up comparisons showed that the satellite-derived cell abundances of picophytoplankton (e.g., MAEs ranged from 1.73 to 2.76) also agreed with the field data. We also analyzed the influences of both temperature and nutrient concentration on algorithm performance. The influence of temperature on the algorithms was not significant because the data were mainly collected in the summer, and the analysis should be repeated in the future with data from other seasons. The algorithm for estimating cell abundance of Syn was sensitive to variations in nutrient levels and herbivory pressure in coastal waters where nano- or microphytoplankton dominated. The input of our algorithms, aph(443), was easily obtained from field measurements and remote sensing. These algorithms provide a relatively easy way to estimate the cell abundances of picophytoplankton and provide data for studying the picophytoplankton community structure in the SCS.
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