Retrieving vertical phytoplankton functional types in the South China Sea and adjacent Taiwan Strait based on phytoplankton absorption spectra and machine learning

Abstract

Phytoplankton functional types (PFTs) found in natural aquatic environments play different roles in the biogeochemical cycles of different elements. However, commonly used methods for identifying PFTs have inherent limitations. In this study, based on a large dataset (1747 samples) collected from 2004 to 2019 in the South China Sea and adjacent Taiwan Strait, which had concurrent measurements of the spectral absorption coefficient of phytoplankton and chlorophyll a concentration of nine PFTs (PFTs_Chla), along with depth and time information, a reliable support vector regression (SVR) model was developed to retrieve these nine PFTs_Chla in the water column. These PFTs included diatoms, dinoflagellates, haptophytes_8, haptophytes_6, chlorophytes, cryptophytes, Prochlorococcus, Synechococcus, and prasinophytes. The independent validation results indicated that the SVR model outperformed the traditional PFTs_Chla retrieval algorithms, with an average mean bias of −14.2%, an average mean absolute unbiased relative difference of 60.3%, and an average coefficient of determination of 0.56. The predicted PFTs_Chla values and their error distributions in the water column were subsequently analyzed. Finally, the SVR model was found to be applicable to most PFTs_Chla retrieval in the East China Sea.