GOCI 위성자료를 이용한 고유광특성 (IOP) 분석

Abstract

Inherent Optical Properties (IOPs) are important factor to characterize marine optical environments as well as to process remote-sensing applications. An attenuation c(λ), absorption a(λ), scattering b(λ), and backscattering bb(λ) coefficients are IOPs, and the two key IOPs relevant to the remote sensing reflectance (Rrs) are a and bb. Each coefficient can divide by seawater constituents like as phytoplankton, detritus (suspended particles), glebstoff (dissolved organic matter), etc. IOPs are also connected to the Apparent Optical Properties (AOPs) by the equation of radiative transfer. So, several semi-analytical bio-optical models were developed to retrieve a and bb from satellite Rrs data. The GOCI (Geostationary Ocean Color Imager) consistently acquires 8 images on everyday for monitoring a sea area around the Northeast Asia having optically complicated sea water types. In this study we tried to retrieve a and bb data using empirically developed GOCI IOP algorithms. And we compared the result with the in-situ a and bb data matching with a GOCI capture time and with results from other semi-analytical bio-optical model (QAA and Carder model). The absorption values from GOCI algorithm are higher than other two model results and the back-scattering values from GOCI algorithm are lower than the others for all bands especially in turbid water.oefficients are IOPs, and the two key IOPs relevant to the remote sensing reflectance (Rrs) are a and bb. Each coefficient can divide by seawater constituents like as phytoplankton, detritus (suspended particles), glebstoff (dissolved organic matter), etc. IOPs are also connected to the Apparent Optical Properties (AOPs) by the equation of radiative transfer. So, several semi-analytical bio-optical models were developed to retrieve a and bb from satellite Rrs data. The GOCI (Geostationary Ocean Color Imager) consistently acquires 8 images on everyday for monitoring a sea area around the