GOCI IOP 분석을 위한 semi-analytical model 비교 연구

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 waters on the Northeast Asia having optically complicated seawater types. In this study the Quasi-Analytical Algorithm, Carders semi-analytical model, and GSM01 (Garver/Siegel/Maritorena) model were processed to IOP retrieval for sea areas around the Korean peninsula. To verify these models, we used two kind of simulated dataset made developed by a Hydrolight simulation (IOCCG dataset) and a forward Rrs model. And we compared the result with the in-situ a and bb data.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 waters on the Northeast Asia having optically complicated seawater types. In this study the Quasi-Analytical