• Korean Journal of Remote Sensing
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• v.29 no.3
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• pp.337-349
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• 2013

Communication Ocean Meteorological Satellite (COMS) for the hybrid mission of meteorological observation, ocean monitoring, and telecommunication service was launched onto Geostationary Earth Orbit on June 27, 2010 and it is currently under normal operation service on $128.2^{\circ}$ East of the geostationary orbit since April 2011. In order to perform the three missions, the COMS has 3 separate payloads, the meteorological imager (MI), the Geostationary Ocean Color Imager (GOCI), and the Ka-band antenna. The MI and GOCI perform the Earth observation mission of meteorological observation and ocean monitoring, respectively. For this Earth observation mission the COMS requires daily mission commands from the satellite control ground station and daily mission is affected by the satellite control activities. For this reason daily mission planning is required. The Earth observation mission operation of COMS is described in aspects of mission operation characteristics and mission planning for the normal operation services of meteorological observation and ocean monitoring. And the first one-year normal operation results after the In-Orbit-Test (IOT) are investigated through statistical approach to provide the achieved COMS normal operation status for the Earth observation mission.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.215-228
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• 2019

HDF5 data format is used to effectively store and distribute large volume of Geostationary Ocean Color Imager(GOCI) satellite data. The Korea Ocean Satellite Center has developed and provided a GOCI Data Processing System(GDPS) for general users who are not familiar with HDF5 format. Nevertheless, it is not easy to merge and process Hierarchical Data Format version5(HDF5) data that requires an understanding of satellite data characteristics, needs to learn how to use GDPS, and stores location and attribute information separately. Therefore, the open source R and rhdf5, data.table, and matrixStats packages were used to develop algorithm that could easily utilize satellite data in HDF5 format without the need for the process of using GDPS.

• 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 $(R^2)$.

• Journal of Satellite, Information and Communications
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• v.8 no.1
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• pp.89-100
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• 2013

Communication Ocean Meteorological Satellite (COMS) for the hybrid mission of meteorological observation, ocean monitoring, and telecommunication service was launched onto Geostationary Earth Orbit on June 27, 2010 and it is currently under normal operation service after the In-Orbit Test (IOT) phase. The COMS is located on $128.2^{\circ}$ East of the geostationary orbit. In order to perform the three missions, the COMS has 3 separate payloads, the meteorological imager (MI), the Geostationary Ocean Color Imager (GOCI), and the Ka-band antenna. Each payload is dedicated to one of the three missions, respectively. The MI and GOCI perform the Earth observation mission of meteorological observation and ocean monitoring, respectively. During the IOT phase the functionalities and the performances of the COMS satellite and ground station have been checked through the Earth observation mission operation for the observation of the meteorological phenomenon over several areas of the Earth and the monitoring of marine environments around the Korean peninsula. The operation characteristics of meteorological mission and ocean mission are described and the mission planning for the COMS is discussed. The mission operation results during the COMS IOT are analyzed through statistical approach for the study of both the mission operation capability of COMS verified during the IOT and the satellite image reception capacity achieved during the IOT.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.861-870
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• 2021

In this study, fused Aerosol Optical Depth (AOD) data were produced using AOD products from the Geostationary Ocean Color Imager (GOCI) onboard Communication, Oceanography and Meteorology Satellite (COMS)satellite and the Advanced Himawari Imager (AHI) onboard Himawari-8. Since the spatial resolution and the coordinate system between the satellite sensors are different, a preprocessing was first preceded. After that, using the level 1.5 AOD dataset of AErosol RObotic NETwork (AERONET), which is ground-based observation, correlations and trends between each satellite AOD and AERONET AOD were utilized to produce more accurate satellite AOD data than the originalsatellite AODs. The fused AOD were found to be more accurate than the originalsatellite AODs. Root Mean Square Error (RMSE) and mean bias of the fused AODs were calculated to be 0.13 and 0.05, respectively. We also compared errors of the fused AODs against those of the original GOCI AOD (RMSE: 0.15, mean bias: 0.11) and the original AHI AOD (RMSE: 0.15, mean bias: 0.05). It was confirmed that the fused AODs have betterspatial coverage than the original AODsin areas where there are no observations due to the presence of cloud from a single satellite.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1477-1482
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• 2023

This study examines marine heat wave (MHW) in the Northeast Asia region from 2012 to 2021, utilizing geostationary satellite Communication, Ocean, and Meteorological Satellite (COMS)/Meteorological Imager sensor (MI) and GEO-KOMPSAT-2A (GK-2A)/Advanced Meteorological Imager sensor (AMI) Sea Surface Temperature (SST) data. Our analysis has identified an increasing trend in the frequency and intensity of MHW events, especially post-2018, with the year 2020 marked by significantly prolonged and intense events. The statistical validation using Optimal Interpolation (OI) SST data and satellite SST data through T-test assessment confirmed a significant rise in sea surface temperatures, suggesting that these changes are a direct consequence of climate change, rather than random variations. The findings revealed in this study serve the necessity for ongoing monitoring and more granular analysis to inform long-term responses to climate change. As the region is characterized by complex topography and diverse climatic conditions, the insights provided by this research are critical for understanding the localized impacts of global climate dynamics.

• Aerospace Engineering and Technology
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• v.6 no.2
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• pp.66-72
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• 2007

In this study, we would like to report the analysis of COMS(Communication, Ocean & Meterological Satellite) MI2U(Meteo-Imager Interface Unit) ORB (On Board Reconfiguration) verification test. MI2U is one of equipment integrated on COMS and in charge of TM/TC function and Power Supply function of MI(Meteo-Imager). COMS, an geo-stationary satellite, is a multi-functional satellite accommodation two observation payloads and one communication payload.

• Aerospace Engineering and Technology
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• v.10 no.1
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• pp.156-166
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• 2011

COMS (Communication Ocean and Meteorological Satellite), the Korea's first geostationary Earth observation satellite, started to operate 24 hours to observe Land/Ocean/Atmosphere with the MI (Meteorological Imager) and GOCI (Geostationary Ocean Color Imager). After the successful completion of the IOT (In-Orbit Test), the satellite is in normal operation from April of 2011. This paper describes an algorithm for scan mirror emissivity compensation of the COMS MI and its software implementation.

• Journal of Satellite, Information and Communications
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• v.7 no.2
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• pp.87-96
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• 2012

In this research, we describe recent technologies and system requirements of the passive microwave radiometer used in Earth observation satellites. And we classify types of microwave radiometer system for Earth observation satellites according to observation targets and ways to scan and discuss a design method. Also, requirements of passive radiometer for Earth observation missions in the latest practical examples used and developed are analyzed in this research.

• Journal of Astronomy and Space Sciences
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• v.23 no.1
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• pp.29-38
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• 2006

This paper presents an Image Motion Compensation (IMC) algorithm for the Korea's Communication, Ocean, and Meteorological Satellite (COMS)-1. An IMC algorithm is a priority component of image registration in Image Navigation and Registration (INR) system to locate and register radiometric image data. Due to various perturbations, a satellite has orbit and attitude errors with respect to a reference motion. These errors cause depointing of the imager aiming direction, and in consequence cause image distortions. To correct the depointing of the imager aiming direction, a compensation algorithm is designed by adapting different equations from those used for the GOES satellites. The capability of the algorithm is compared with that of existing algorithm applied to the GOES's INR system. The algorithm developed in this paper improves pointing accuracy by 40%, and efficiently compensates the depointings of the imager aiming direction.