• Proceedings of the KSRS Conference
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• v.1
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• pp.75-78
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• 2006

The Korea Ocean Satellite Center (KOSC) is under development to establish in line with the launch of the first Korean multi-function geostationary satellite COMS (Communication, Ocean and Meteorological Satellite) scheduled in 2008. KOSC aims to receive, process and distribute Geostationary Ocean Color Sensor (GOCI) data on board COMS in near-real time. In this report, current status of KOSC development is presented in the following categories; site selection for KOSC, antenna design, GOCI data receiving and processing system, data distribution, future works.

• 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.

• 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.

• 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.

• 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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• 2002.10a
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• pp.649-654
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• 2002

In summer season of 1998, a huge flood occurred around the Yangtze River in the eastern China. The low salinity water less than 28 psu from the river was detected around the southeastern part of the Jeju Island which is located in the southern part of the Korean peninsula. We studied how to detect low salinity water from the Yangtze River, which gives terrible damages to the Korean fisheries. We got the relationships between low surface salinity, turbid water from the Yangtze River and digital ocean color using remote sensing of SeaWiFS satellite in the northern East China Sea in summer seanson of 1998, 1999, 2000 and 2001. The charts of salinity in the northern East China Sea were made by the regenerating of the satellite ocean color data with the formula from the relationships between low salinity, in situ turbid water (transparency) and satellite ocean color.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.153-162
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• 2004

In the summer of 1998-2001, a huge flood occurred in the Yangtze River in the eastern China. Low salinity water less than 28 psu from the river was detected around the southwestern part of the Jeju Island, which is located in the southern part of the Korean Peninsula. We studied how to detect low salinity water from the Yangtze River, that cause a terrible damage to the Korean fisheries. We established a relationships between low salinity at surface, turbid water from the Yangtze River and digital ocean color remotely sensed data of SeaWiFS sensor in the northern East China Sea, in the summer of 1998, 1999, 2000 and 2001. The salinity charts of the northern East China Sea were created by regeneration of the satellite ocean color data using the empirical formula from the relationships between in situ low salinity, in situ measured turbid water with transparency and SeaWiFS ocean color data (normalized water leaving radiance of 490 nm/555 nm).

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1039-1041
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• 2003

Ocean color products derived from remote sensing satellite data are useful for monitoring the sea water quality such as the concentrations of chlorophyll, sediments and dissolved organic matter. Currently, ocean color products derived from MODIS data can be requested from NASA over the internet. However, due to the bandwidth limitation of most users in this region, and the time delay in data delivery, the products cannot be use for near-real time monitoring of sea water chlorophyll. CRISP operates a MODIS data receiving station for environmental monitoring purposes. MODIS data have been routinely received and processed to level 1B. We have adapted the higher level processing algorithms from the Institutional Algorithms provided by NASA to run in a standalone environment. The implemented algorithms include the MODIS ocean color algorithms. Seasonal chlorophyll concentration composite can be compiled for the region. By comparing the near-real time chlorophyll product with the seasonal composite, anomaly in chlorophyll concentration can be detected.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.24 no.2
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• pp.282-297
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• 2019

Detection of mesoscale oceanic eddies using satellite data can utilize various ocean parameters such as sea surface temperature (SST), chlorophyll-a pigment concentration in phytoplankton, and sea level altimetry measurements. Observation methods vary for each satellite dataset, as it is obtained using different temporal and spatial resolution, and optimized data processing. Different detection results can be derived for the same oceanic eddies; therefore, fundamental research on eddy detection using satellite data is required. In this study, we used ocean color satellite data, sea level altimetry data, and infrared SST data to detect mesoscale eddies in the East Sea and compared results from different detection methods. The sea surface current field derived from the consecutive ocean color chlorophyll-a concentration images using the maximum cross correlation coefficient and the geostrophic current field obtained from the sea level altimetry data were used to detect the mesoscale eddies in the East Sea. In order to compare the eddy detection from satellite data, the results were divided into three cases as follows: 1) the eddy was detected in both the ocean color and altimeter images simultaneously; 2) the eddy was detected from ocean color and SST images, but no eddy was detected in the altimeter data; 3) the eddy was not detected in ocean color image, while the altimeter data detected the eddy. Through these three cases, we described the difficulties with satellite altimetry data and the limitations of ocean color and infrared SST data for eddy detection. It was also emphasized that study on eddy detection and related research required an in-depth understanding of the mesoscale oceanic phenomenon and the principles of satellite observation.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1591-1604
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• 2023

In ocean color remote sensing, atmospheric correction is a vital process for ensuring the accuracy and reliability of ocean color products. Furthermore, in recent years, the remote sensing community has intensified its requirements for understanding errors in satellite data. Accordingly, research is currently addressing errors in remote sensing reflectance (R_rs) resulting from inaccuracies in meteorological variables (total ozone, pressure, wind field, and total precipitable water) used as auxiliary data for atmospheric correction. However, there has been no investigation into the error in R_rs caused by the variability of the water vapor profile, despite it being a recognized error source. In this study, we used the Second Simulation of a Satellite Signal Vector version 2.1 simulation to compute errors in water vapor transmittance arising from variations in the water vapor profile within the GOCI-II observation area. Subsequently, we conducted an analysis of the associated errors in ocean color products. The observed water vapor profile not only exhibited a complex shape but also showed significant variations near the surface, leading to differences of up to 0.007 compared to the US standard 62 water vapor profile used in the GOCI-II atmospheric correction. The resulting variation in water vapor transmittance led to a difference in aerosol reflectance estimation, consequently introducing errors in R_rs across all GOCI-II bands. However, the error of R_rs in the 412-555 nm due to the difference in the water vapor profile band was found to be below 2%, which is lower than the required accuracy. Also, similar errors were shown in other ocean color products such as chlorophyll-a concentration, colored dissolved organic matter, and total suspended matter concentration. The results of this study indicate that the variability in water vapor profiles has minimal impact on the accuracy of atmospheric correction and ocean color products. Therefore, improving the accuracy of the input data related to the water vapor column concentration is even more critical for enhancing the accuracy of ocean color products in terms of water vapor absorption correction.