GOCI 위성자료를 위한 식물플랑크톤 사이별 분석 알고리즘 개발

Abstract

The phytoplankton size class (PSC) is one of key factor for the ecological and biogeochemical processes in the ocean environment. In recent years, remote sensed data has been used to determine the global distribution of PSC and phytoplankton functional type (PFT). The sea area of Northeast Asia has various characteristics from clear water (e.g. East Sea) to extremely turbid water (e.g. estuary of the Yangtze River). Therefore, the ecological and biogeochemical processes in this area were also very intricate. In this study, we determined the PSC distribution in the sea area of Northeast Asia using the Geostationary Ocean Color Imager (GOCI) data. GOCI is the world’s first geostationary ocean color sensor and now consistently observing the Northeast Asia. For the retrieval of PSC, we developed abundance-based PSC algorithm for GOCI. Totally 260 in-situ pigment samples used for algorithm development, these data was acquired from 7 different cruises performed by different institutes. To get the consistency of pigment dataset, we carried out the quality control analysis. A quality control filter was applied to all pigment data using relationship of accessory pigments and total chlorophyll-a concentration. Consequently, 15 samples and 1 entire cruise were eliminated as a result of QC. Detection of nano-plankton and micro-plankton shows similar patterns compared with previous algorithms, but pico-plankton induced slightly n functional type (PFT). The sea area of Northeast Asia has various characteristics from clear water (e.g. East Sea) to extremely turbid water (e.g. estuary of the Yangtze River). Therefore, the ecological and biogeochemical processes in this area were also very intricate. In this study, we determined the PSC distribution in the sea area of Northeast Asia using the Geostationary Ocean Color Imager (GOCI) data. GOCI is the world’s first geostationary ocean color sensor and now consistently observing the Northeast Asia. For the retrieval of PSC, we developed abundance-based PSC algorithm for GOCI. Totally 260 in-situ pigment samples used for algorithm development, these data was acquired from 7 different cruises performed by different institutes. To get the consistency of pigment dataset, we carried out the quality control analysis. A quality control filter was applied to all pigment data using relationship of accessory pigments and total chlorophyll-a concentration. Consequently, 15 samples and 1 entire cruise were eliminated as a result of QC. Detection of nano-plankton and micro-plankton shows similar patterns compared with previous algorithms, but pico-plankton induced slightly