북서태평양에서의 식물성 플랑크톤 생물 계절학

Abstract

Annual and interannual variability in phytoplankton bloom has important influence for marine ecosystem such as ocean carbon cycle result from photosynthesis, food web and so on. Many study investigated global phytoplankton phenology using ocean remote sensing satellite which allow large spatial coverage and high temporal resolution observation. However missing data may affect the accuracy of analysis of phenology such as initiation, termination, duration and peak timing of phytoplankton bloom. To reduce the missing data, we used the Geostationary Ocean Color Imager (GOCI) data. GOCI, the first geostationary ocean color observation satellite, observes the Northeastern Pacific Ocean 8 times a day from 00 UTC to 07 UTC. Since GOCI supply high spatial and temporal resolution image, to investigate the phytoplankton phenology can be detailed with much more accuracy in GOCI coverage. In this study, phonological properties were analyzed using GOCI chlorophyll-a dataset over the study area. First of all the spatially and temporally interpolation were conducted to reduce the missing data. And annual median plus 5% chlorophyll concentration threshold applied to define the bloom. After then we compared annual difference of phenology phenomena and compared with SeaWiFS era.cean remote sensing satellite which allow large spatial coverage and high temporal resolution observation. However missing data may affect the accuracy of analysis of phenology such as initiation, termination, duration and peak timing of phytoplankton bloom. To reduce the missing data, we used the Geostationary Ocean Color Imager (GOCI) data. GOCI, the first geostationary ocean color observation satellite, observes the Northeastern Pacific Ocean 8 times a day from 00 UTC to 07 UTC. Since GOCI supply high spatial and temporal resolution image, to investigate the phytoplankton phenology can be detailed with much more accuracy in GOCI coverage. In this study, phonological properties were analyzed using GOCI chlorophyll-a dataset over the study area. First of all the spatially and temporally interpolation were conducted to reduce the missing data. And annual median plus 5% chlorophyll concentration threshold applied to define the bloom. After then we compared annual difference of phenology phenomena and compared with SeaWiFS era.