• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.163-166
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• 2009

2009년에 발사 예정인 통신해양기상위성은 우리나라 최초의 정지궤도 위성이며 해양기상관측위성이다. 해색관측에 있어서 정지제도 상에서 한반도와 주변해역을 관측하는 것은 시-공간해상력에서 향상된 해색위성자료를 제공해 줄 것이다. 이러한 정지궤도 해색위성 자료의 해양지리정보 활용은 적용의 범위와 GOCI 자료가 제공하는 정보의 해석적 내용에 있어서 기존의 극궤도 위성자료를 활용하는 것과는 다른 차원의 자료 구축 능력을 제시할 수 있다. 본 연구에서는 정지궤도 해색위성에 탑재된 GOCI로부터 획득되는 영상정보를 통해 해양지리정보에 적용 가능한 분야를 해석하고 이를 적용하는 방안에 대해 제시하였다. 해양지리정보의 다양한 구축 자료와 개발된 해양공간정보시스템은 향후 해양위성자료의 실시간 분석결과를 반영하여 자료의 갱신과 추출 정보의 신속한 서비스를 구현할 수 있을 것으로 판단된다. 이러한 정보서비스의 효과는 지구온난화에 따른 기후변화와 기상이변 등의 해양기상재해에 보다 신속하게 대처하는 재해정보시스템의 구현에 기여할 것으로 판단된다.

• 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.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.231.1-231.1
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• 2012

한국해양과학기술원 해양위성센터에서 주관운영을 수행하고 있는 천리안 위성의 해양탑재체인 천리안 해양관측위성(이하 GOCI)은 정지궤도위성용 해색센서로서, 태양을 광원으로 지구상의 해수 표면 부근에서 반사되어 대기를 통과한 가시광 및 근적외 대역을 8개 밴드로 분광하여 관측하는 센서이다. 해색센서의 경우, 일반적으로 센서에 입사되는 광신호의 약 90%가 대기에 의한 신호이며, 약 10%에 해당되는 신호만 원래 관측목적인 해수에 의한 신호이기 때문에, 5% 이내의 높은 복사보정 정확도가 요구된다. 이러한 높은 복사보정 정확도를 만족시키기 위해서는, 지상에서의 현장관측을 통한 위성자료 검보정 뿐만 아니라, 발사 후 위성 궤도상에서 센서의 복사보정을 수행하는 궤도상 복사보정이 체계적으로 수행되어야 한다. GOCI는 태양을 기준광원으로 하는 태양광 복사보정을 채택하여, 센서의 셔터부에 태양광 복사보정을 위한 2개의 태양광확산기(Solar Diffuser)를 장비하고 있다. 본 발표에서는 궤도상 시험 후 약 16개월에 걸친 궤도상 복사보정 운영결과와 관련하여, 발사 후 일별, 월별, 계절별 등 각 기간별 센서의 이득변화를 관찰하였으며, 그 결과 1년을 기준으로 약 3% 범위로 주기적인 이득 변화가 있음을 확인하였다. 지상시험결과와의 비교에 의해, 태양광확산기에 대한 태양입사각이 이러한 주기적인 이득 변화의 주 원인임을 궤도상 복사보정 운영결과를 통해 밝히고자 한다.

• Proceedings of the KSEEG Conference
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• 2007.04a
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• pp.199-199
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• 2007

• 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.

• 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.26 no.2
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• pp.239-249
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• 2010

The Geostationary Ocean Color Imager (GOCI) data-processing system (GDPS), which is a software system for satellite data processing and analysis of the first geostationary ocean color observation satellite, has been developed concurrently with the development of th satellite. The GDPS has functions to generate level 2 and 3 oceanographic analytical data, from level 1B data that comprise the total radiance information, by programming a specialized atmospheric algorithm and oceanic analytical algorithms to the software module. The GDPS will be a multiversion system not only as a standard Korea Ocean Satellite Center(KOSC) operational system, but also as a basic GOCI data-processing system for researchers and other users. Additionally, the GDPS will be used to make the GOCI images available for distribution by satellite network, to calculate the lookup table for radiometric calibration coefficients, to divide/mosaic several region images, to analyze time-series satellite data. the developed GDPS system has satisfied the user requirement to complete data production within 30 minutes. This system is expected to be able to be an excellent tool for monitoring both long-term and short-term changes of ocean environmental characteristics.

• Journal of Satellite, Information and Communications
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• v.1 no.2
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• pp.1-7
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• 2006

GOCi (Geostationary Ocean Color Imager) is one of the COMS payloads that KARI is currently developing and scheduled to be in operation from around 2008. Its primary objective is to monitor the Korean coastal water environmental condition. We report the current progress in development of the integrated optical model as one of the key analysis tools for the GOCI in-orbit performance verification. The model includes the Sun as the emitting light source. The curved Earth surface section of 2500 km x 2500 km includingthe Korean peninsular os defined as a Lambertian scattering surface consisted of land and sea surface. From its geostationary orbit, the GOCI optical system observes the reflected light from the surfaces with varying reflectance representing the changes in its environmental conditions. The optical ray tracing technique was used to demonstrate the GOCI in-orbit performances such as red tide detection. The computational concept, simulation results and its implications to the on-going development of GOCI are presented.

• 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.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1255-1272
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• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.