• Proceedings of the KSRS Conference
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• v.1
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• pp.257-259
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• 2006

The Communication Ocean Meteorological Satellite (COMS) is a geostationary satellite being developed by Korea Aerospace Research Institute. Geostationary Ocean Color Imager (GOCI) is one of the payloads embarked on the COMS satellite. It acquires ocean images around Korea in 8 visible spectral bands with a spatial resolution of about 500 m. The acquired data are used to provide forecasting and now casting of the ocean state. The GOCI operations are controlled by the satellite embedded software, i.e. on-board software. This paper introduces the GOCI payload of the COMS satellite and describes the control software for the GOCI.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.108-111
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• 2005

The Communication Ocean, Meteorological Satellite (COMS) as the one of the national space program has been developed by Korea Aerospace Research Institute (KARl). The Geostationary Ocean Color Imager (GOCI) is one of the main payloads ofCOMS which will provide consistent monitoring of ocean-colour around the Korean Peninsula from geostationary platforms. The ocean color observation from geostationary platform is required to remedy the coverage constraints imposed by polar orbiting platforms. In this paper the main characteristics of GOCI are described and compared with the current ocean color sensors. The GOCI will provide the measurement data of 6 visible channels and 2 nearinfrared channels (40Onm - 900nm). The high radiometric sensitivity is essential of ocean color sensor because of the weak water leaving radiance.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.74-79
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• 2017

The Geostationary Ocean Color Imager (GOCI), the world's first ocean color sensor operated in a geostationary orbit, can be utilized to mitigate damages by monitoring marine disasters in real time such as red tides, green algae, sargassum, cold pools, typhoons, and so on. In this paper, we described a methodology and procedure for processing GOCI data in order to maximize its utilization potential. The GOCI data processing procedure is divided into data reception, data processing, and data distribution. The kinds of GOCI data are classified as raw, level 1, and level 2. "Raw" refers to an unstructured data type immediately generated after reception by satellite communications. Level 1 is defined as a radiance data type of two dimensions, generated after radiometric and geometric corrections for raw data. Level 2 indicates an ocean color data type from level-1 data using ocean color algorithms.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.171-176
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• 2017

Recently, several information-technology research projects such as those for high-performance computing, the cloud service, and the DevOps methodology have been advanced to develop the efficiency of satellite data-processing systems. In March 2019, the Geostationary Ocean Color Imager II (GOCI-II) will be launched for its predictive capability regarding marine disasters and the management of the fishery environment; moreover, the GOCI-II Ground Segment (G2GS) system for data acquisition/processing/storing/distribution is being designed at the Korea Ocean Satellite Center (KOSC). The G2GS is composed of the following six functional subsystems: data-acquisition subsystem (DAS), data-correction subsystem (DCS), precision-correction subsystem (PCS), ocean data-processing subsystem (ODPS), data-management subsystem (DMS), and operation and quality management subsystem (OQMS). The G2GS will enable the real-time support of the GOCI-II ocean-color data for government-related organizations and public users.

• Journal of Astronomy and Space Sciences
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• v.30 no.4
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• pp.321-326
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• 2013

The modulation transfer function (MTF) is a widely used indicator in assessments of remote-sensing image quality. This MTF method is also used to restore information to a standard value to compensate for image degradation caused by atmospheric or satellite jitter effects. In this study, we evaluated MTF values as an image quality indicator for the Geostationary Ocean Color Imager (GOCI). GOCI was launched in 2010 to monitor the ocean and coastal areas of the Korean peninsula. We evaluated in-orbit MTF value based on the GOCI image having a 500-m spatial resolution in the first time. The pulse method was selected to estimate a point spread function (PSF) with an optimal natural target such as a Seamangeum Seawall. Finally, image restoration was performed with a Wiener filter (WF) to calculate the PSF value required for the optimal regularization parameter. After application of the WF to the target image, MTF value is improved 35.06%, and the compensated image shows more sharpness comparing with the original image.

• Journal of Astronomy and Space Sciences
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• v.29 no.2
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• pp.199-208
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• 2012

The world's first geostationary ocean color imager (GOCI) is a three-mirror anastigmat optical system 140 mm in diameter. Designed for 500 m ground sampling distance, this paper deals with on-orbit modulation transfer function (MTF)measurement and analysis for GOCI. First, the knife-edge and point source methods were applied to the 8th band (865 nm) image measured April 5th, 2011. The target details used are the coastlines of the Korean peninsula and of Japan, and an island 400 meters in diameter. The resulting MTFs are 0.35 and 0.34 for the Korean East Coastline and Japanese West Coastline edge targets, respectively, and 0.38 for the island target. The daily and seasonal MTF variations at the Nyquist frequency were also checked, and the result is $0.32{\pm}0.04$ on average. From these results, we confirm that the GOCI on-orbit MTF performance satisfies the design requirements of 0.32 for 865 nm wavelength.

• Journal of Information Technology Services
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• v.11 no.sup
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• pp.177-189
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• 2012

We developed the ocean disaster detection system(OD2S) which copes with the occurrences of ocean disasters (e. g. the red and green tide, the oil spill, the typhoon, and the sea ice) by converging and integrating the ocean color remote sensing using the satellite and the information technology exploiting the mass data processing and the pattern recognitions. This system which is based on the cosine similarity detects the ocean disasters in real time. The existing ocean color sensors which are operated in the polar orbit platforms cannot conduct the real time observation of ocean environments because they support the low temporal resolutions of one observation a day. However, geostationary ocean color imager(GOCI), the first geostationary ocean color sensor in the world, produces the ocean color images(e. g. the chlorophyll, the colored dissolved organic matter(CDOM), and the total suspended solid(TSS)), with high temporal resolutions of hourly intervals up to eight observations a day. The evaluation demonstrated that the OD2S can detect the excessive concentration of chlorophyll, CDOM, and TSS. Based on these results, it is expected that OD2S detects the ocean disasters in real time.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.175-188
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• 2010

A new correction method is required for the Geostationary Ocean Color Imager (GOCI), which is the world's first ocean color observing sensor in geostationary orbit. In this paper we introduce a new method of atmospheric and the Bidirectional Reflectance Distribution Function(BRDF) correction for GOCI. The Spectral Shape Matching Method(SSMM) and the Sun Glint Correction Algorithm(SGCA) were developed for atmospheric correction, and BRDF correction was improved using Inherent Optical Property(IOP) data. Each method was applied to the Sea-Viewing Wide Field-of-view Sensor(SeaWiFS) images obtained in the Korean sea area. More accurate estimates of chlorophyll concentrations could be possible in the turbid coastal waters as well as areas severely affected by aerosols.

• 한국전산유체공학회:학술대회논문집
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• 2009.04a
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• pp.135-141
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• 2009

A preliminary thermal analysis is performed for the optical payload system of a geostationary satellite. The optical payload considered in this paper is GOCI(Geostationary Ocean Color Imager) of COMS of Korea. The radiative and conductive thermal models are employed in order to predict thermal responses of the GOCI on the geostationary orbit. According to the results of this analysis are as follows: 1) the GOCI instrument thermal control is satisfactory to provide the temperatures for the GOCI performances, 2) the thermal control is defined and interfaces are validated, and 3) the entrance baffle temperature is found slightly out its specification, therefore further detailed analyses should be continued on this element.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.162-165
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• 2007

Chlorophyll concentration is an important factor for physical oceanography as well as biological oceanography. For these necessity many oceanographic researchers have been investigated it for a long time. But investigation using vessel is very inefficient, on the other hands, ocean color remote sensing is a powerful means to get fine-scale (spatial and temporal scale) measurements of chlorophyll concentration. Geostationary Ocean Color Imager (GOCI), for ocean color sensor, loaded on COMS (Communication, Ocean and Meteorological Satellite), will be launched on late 2008 in Korea. According to the necessity of algorithm for GOCI, we developed chlorophyll algorithm for GOCI in this study. There are two types of chlorophyll algorithms. One is an empirical algorithm using band ratio, and the other one is a fluorescence-based algorithms. To develop GOCI chlorophyll algorithm empirically we used bands centered at 412 nm, 443 nm and 555 nm for the DOM absorption, chlorophyll maximum absorption and for absorption of suspended solid material respectively. For the fluorescence-based algorithm we analyzed in-situ remote sensing reflectance $(R_{rs})$ data using baseline method. Fluorescence Line Height $({\Delta}Flu)$ calculated from $R_{rs}$ at bands centered on 681 nm and 688 nm, and ${\Delta}Flu_{(area)}$ are used for development of algorithm. As a result ${\Delta}Flu_{(area)}$ method leads the best fitting for squared correlation coefficient $(R^2)$.