반사도 모델자료를 활용한 식물플랑크톤 기능성 그룹의 광학적 구별

Abstract

Phytoplankton play a fundamental role in the biogeochemical cycling in marine systems, with different phytoplankton functional types (PFTs) having specific biogeochemical roles. In the last decades, there has been increasing recognition that we need to understand the PFTs as a tool to predict the response of marine ecosystems to anthropogenic change in the global environment. Recently, advances in remote sensing have enabled retrieving global information on the PFTs, which are useful in understanding biogeochemical and ecological process in the global scale. While many researches have conducted to distinguish one type of phytoplankton from another, there are currently few studies on classifying and quantifying multiple types of phytoplankton in natural conditions. In this study, we focused on the optical classification of five PFTs including diatoms, dinoflagellates, coccolithophores, cyanobacteria and cryptophytes. We tested the possibility of quantifying phytoplankton community composition under non-bloom conditions (0.04～10 mg m-3) based on the spectral response of simulated remote sensing reflectance (Rrs). In order to generate hyperspectral Rrs of mixed PFTs, we used Hydrolight software (version 5.1) and bio-optical properties of multiple types of phytoplankton. The phytoplankton absorption (aph) of mixed PFTs for model inputs were represented by change in percentage composition of phytoplankton size class inat we need to understand the PFTs as a tool to predict the response of marine ecosystems to anthropogenic change in the global environment. Recently, advances in remote sensing have enabled retrieving global information on the PFTs, which are useful in understanding biogeochemical and ecological process in the global scale. While many researches have conducted to distinguish one type of phytoplankton from another, there are currently few studies on classifying and quantifying multiple types of phytoplankton in natural conditions. In this study, we focused on the optical classification of five PFTs including diatoms, dinoflagellates, coccolithophores, cyanobacteria and cryptophytes. We tested the possibility of quantifying phytoplankton community composition under non-bloom conditions (0.04～10 mg m-3) based on the spectral response of simulated remote sensing reflectance (Rrs). In order to generate hyperspectral Rrs of mixed PFTs, we used Hydrolight software (version 5.1) and bio-optical properties of multiple types of phytoplankton. The phytoplankton absorption (aph) of mixed PFTs for model inputs were represented by change in percentage composition of phytoplankton size class in