• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.251-262
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• 2010

In order to provide quantitative control of the standard products of Geostationary Ocean Color Imager (GOCI), on-board radiometric correction, atmospheric correction, and bio-optical algorithm are obtained continuously by comprehensive and consistent calibration and validation procedures. The calibration/validation for radiometric, atmospheric, and bio-optical data of GOCI uses temperature, salinity, ocean optics, fluorescence, and turbidity data sets from buoy and platform systems, and periodic oceanic environmental data. For calibration and validation of GOCI, we compared radiometric data between in-situ measurement and HyperSAS data installed in the Ieodo ocean research station, and between HyperSAS and SeaWiFS radiance. HyperSAS data were slightly different in in-situ radiance and irradiance, but they did not have spectral shift in absorption bands. Although all radiance bands measured between HyperSAS and SeaWiFS had an average 25% error, the 11% absolute error was relatively lower when atmospheric correction bands were omitted. This error is related to the SeaWiFS standard atmospheric correction process. We have to consider and improve this error rate for calibration and validation of GOCI. A reference target site around Dokdo Island was used for studying calibration and validation of GOCI. In-situ ocean- and bio-optical data were collected during August and October, 2009. Reflectance spectra around Dokdo Island showed optical characteristic of Case-1 Water. Absorption spectra of chlorophyll, suspended matter, and dissolved organic matter also showed their spectral characteristics. MODIS Aqua-derived chlorophyll-a concentration was well correlated with in-situ fluorometer value, which installed in Dokdo buoy. As we strive to solv the problems of radiometric, atmospheric, and bio-optical correction, it is important to be able to progress and improve the future quality of calibration and validation of GOCI.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.125-134
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• 2017

Recent advance in the satellite-based remote sensing technology demands abilities to efficiently processthe massive satellite data. In thisstudy, we focused on developing GOCI Products Reprocessing System (GPRS) based on server virtualization and distributed processing in order to efficiently reprocess massive GOCI data. Experimental results revealed that GPRS allows raising the usage rates of memory and central processing unit (CPU) up to about 100% and 75%, respectively. This meansthat the proposed system enables us to save the hardware resources and increase the work process speed at the same time when we process massive satellite data.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.27.3-28
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• 2008

In this study, we report a new Monte Carlo ray tracing technique for estimating GOCI (Geostationary Ocean Color Instrument) radiative transfer characteristics and imaging performance simultaneously. First, a full scale GOCI optical model was constructed with measured characteristics at the component level and placed in the geostationary orbit. An optical model of approximated GOCI target area centered at the Korean penninsular was then built using the USGS coastal line data and representative land and sea surface reflectivity data. The light rays launched from a simulated sun model travel to the Earth surface, where they are reflected and scattered. Some of the light rays that are headed to the GOCI model in the orbit were selected and traced, as they have entered into the GOCI aperture. As they pass through each GOCI optical part, the ray path and intensity are adjusted according to the measured characteristics for reflection, transmission, refractive index and surface scattering. The ray-traced imaging and radiative transfer performance indicators confirm that the computer generated GOCI optical system with measured characteristics can be used for in-orbit operation simulation following the designed measurement sequence. The computational technique and its implications as a operation support tool are discussed.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.2
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• pp.199-210
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• 2013

Detailed 3 dimensional structure of Asian dust plume has been analyzed from the retrieved aerosol data from two different satellites which are the Korea's $1^{st}$ geostationary satellite, namely the Communication, Ocean, Meteorological Satellite (COMS) spacecraft launched in 2010, and the NASA's Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). COMS spacecraft provides the first time resolved aerial aerosol maps by the systematically well-calibrated multispectral measurements from the Geostationary Ocean Color Imager (GOCI) instrument. GOCI data are used here to evaluate intensity, spatial distribution, and long-range transport of Asian dust plume during 1~2 May 2011. We found that the strong Asian dust plume showing AOT of 2~5 was lofted to the altitude around 2~4 km above the Earth's surface and transported over Yellow Sea with a speed of about 25 km/hr. The CALIPSO extinction coefficient and particulate depolarization ratio (PDR) profiles confirmed that nonspherical dust particles were enriched in the dust plume. This study is a first example of quantitative integration of GOCI and CALIOP measurements for clarifying the overall structure of an Asian dust event.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.157-165
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• 2010

In June 2010, Geostationary Ocean Color Imager (GOCI), the world's first ocean color observation satellite will be launched. GOCI is planned for use in real-time monitoring of the ocean environment around Korean Peninsula by daily analysis of ocean environment measurements of chlorophyll concentration, dissolved organic matter, and suspended sediments taken eight times per day for seven years. GOCI primary data will support a fishery information service and red tide forecasting, and ocean climate change research. In this paper, the development background of GOCI, user requirements, GOCI architecture, and the GOCI on-orbit operational concept are explained.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1507-1515
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• 2021

If chlorophyll-a is estimated through ocean color remote sensing, it is able to understand the global distribution of phytoplankton and primary production. However, there are missing data in the ocean color observed from the satellites due to the clouds or weather conditions. In thisstudy, the missing data of the GOCI (Geostationary Ocean Color Imager) chlorophyll-a product wasreconstructed by using DINEOF (Data INterpolation Empirical Orthogonal Functions). DINEOF reconstructs the missing data based on spatio-temporal data, and the accuracy was cross-verified by removing a part of the GOCI chlorophyll-a image and comparing it with the reconstructed image. In the study area, the optimal EOF (Empirical Orthogonal Functions) mode for DINEOF wasin 10-13. The temporal and spatialreconstructed data reflected the increasing chlorophyll-a concentration in the afternoon, and the noise of outliers was filtered. Therefore, it is expected that DINEOF is useful to reconstruct the missing images, also it is considered that it is able to use as basic data for monitoring the ocean environment.

• Proceedings of the KSRS Conference
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• v.1
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• pp.82-85
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• 2006

The Geostationary Ocean Color Imager (GOCI) will be loaded in Communication, Ocean and Meteorological Satellite (COMS). To efficiently apply the GOCI data in the variety of fields, it is essential to develop the standard algorithm for estimating the concentration of ocean environmental components (, , and ). For developing the empirical algorithm, about 300 water samples and in situ measurements were collected from sea water around the Korean peninsula from 1998 to 2006. Two kinds of chlorophyll algorithms are developed by using statistical regression and fluorescence technique considering the bio-optical properties in Case-II waters. The single band algorithm for is derived by relationship between Rrs (555) and in situ concentration. The CDOM is estimated by absorption coefficient and ratio of Rrs(412)/Rrs(555). These standard algorithms will be programmed as a module of GOCI Data Processing System (GDPS) until 2008.

• Proceedings of KOSOMES biannual meeting
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• 2008.05a
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• pp.191-195
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• 2008

In Ansan (the headquarter of KORDI ; Korea Ocean Research & Development Institute), KOSC(Korea Ocean Satellite Center) is being prepared for acquisition, processing and distribution of sensor data via L-band from GOCI(Geostationary Ocean Color Imager) instrument which is loaded on COMS(Communication, Ocean and Meteorological Satellite); it will be launched in 2009. The basis equipment of KOSC(Electric power, Network, Security) has been constructed in 2007. KOSC is being constructed data processing and management system, GOCI L-band reception system, etc. The final object of KOSC is that maximize the application of GOCI.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.961-970
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• 2017

Despite of large demand for high spatial resolution products of aerosol properties from satellite remote sensing, it has been very difficult due to the weak signal by a single pixel and higher noise from clouds. In this study, aerosol retrieval algorithm with the high spatial resolution ($500m{\times}500m$) was developed using Geostationary Ocean Color Imager (GOCI) data during the Korea-US Air Quality (KORUS-AQ) period in May-June, 2016.Currently, conventional GOCI Yonsei aerosol retrieval(YAER) algorithm provides $6km{\times}6km$ spatial resolution product. The algorithm was tested for its best possible resolution of 500 m product based on GOCI YAER version 2 algorithm. With the new additional cloud masking, aerosol optical depth (AOD) is retrieved using the inversion method, aerosol model, and lookup table as in the GOCI YAER algorithm. In some cases, 500 m AOD shows consistent horizontal distribution and magnitude of AOD compared to the 6 km AOD. However, the 500 m AOD has more retrieved pixels than 6 km AOD because of its higher spatial resolution. As a result, the 500 m AOD exists around small clouds and shows finer features of AOD. To validate the accuracy of 500 m AOD, we used dataset from ground-based Aerosol Robotic Network (AERONET) sunphotometer over Korea. Even with the spatial resolution of 500 m, 500 m AOD shows the correlation coefficient of 0.76 against AERONET, and the ratio within Expected Error (EE) of 51.1%, which are comparable to the results of 6 km AOD.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1553-1563
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• 2023

The Geostationary Ocean Color Imager-II (GOCI-II) is a satellite designed for ocean color observation, covering the Northeast Asian region and the entire disk of the Earth. It commenced operations in 2020, succeeding its predecessor, GOCI, which had been active for the previous decade. In this study, we aimed to enhance the atmospheric correction algorithm, a critical step in producing satellite-based ocean color data, by performing cross-calibration on the GOCI-II near-infrared (NIR) band using the GOCI NIR band. To achieve this, we conducted a cross-calibration study on the top-of-atmosphere (TOA) radiance of the NIR band and derived a vicarious calibration gain for two NIR bands (745 and 865 nm). As a result of applying this gain, the offset of two sensors decreased and the ratio approached 1. It shows that consistency of two sensors was improved. Also, the Rayleigh-corrected reflectance at 745 nm and 865 nm increased by 5.62% and 9.52%, respectively. This alteration had implications for the ratio of Rayleigh-corrected reflectance at these wavelengths, potentially impacting the atmospheric correction results across all spectral bands, particularly during the aerosol reflectance correction process within the atmospheric correction algorithm. Due to the limited overlapping operational period of GOCI and GOCI-II satellites, we only used data from March 2021. Nevertheless, we anticipate further enhancements through ongoing cross-calibration research with other satellites in the future. Additionally, it is essential to apply the vicarious calibration gain derived for the NIR band in this study to perform vicarious calibration for the visible channels and assess its impact on the accuracy of the ocean color products.