• 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.

• Proceedings of the KSRS Conference
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• v.1
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• pp.114-117
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• 2006

One of the most important tasks of ocean color observations is to determine the distribution of phytoplankton primary production. A variety of bio-optical algorithms have been developed estimate primary production from these parameters. In this communication, we investigated the possibility of using a novel universal approximator-support vector machines (SVMs)-as the nonlinear transfer function between oceanic primary production and the information that can be directly retrieved from satellite data. The VGPM (Vertically Generalized Production Model) dataset was used to evaluate the proposed approach. The PPARR2 (Primary Production Algorithm Round Robin 2) dataset was used to further compare the precision between the VGPM model and the SVM model. Using this SVM model to calculate the global ocean primary production, the result is 45.5 PgC $yr^{-1}$, which is a little higher than the VGPM result.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.187-190
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• 2007

The Geostationary Ocean Color Imager (GOCI) is under development to provide a monitoring of ocean-color around the Korean Peninsula from geostationary platforms. It is planned to be loaded on Communication, Ocean, and Meteorological Satellite (COMS) of Korea. The GOCI has been designed to provide multi-spectral data to detect, monitor, quantify, and predict short term changes of coastal ocean environment for marine science research and application purpose. The target area of GOCI observation covers sea area around the Korean Peninsula. Based on the nonlinear radiometric model, the GOCI calibration method has been derived. The nonlinear radiometric model for GOCI will be validated through ground test. The GOCI radiometric calibration is based on on-board calibration devices; solar diffuser, DAMD (Diffuser Aging Monitoring Device). In this paper, the GOCI radiometric error propagation is analyzed. The radiometric model error due to the dark current nonlinearity is analyzed as a systematic error. Also the offset correction error due to gain/offset instability is considered. The radiometric accuracy depends mainly on the ground characterization accuracies of solar diffuser and DAMD.

• Fisheries and Aquatic Sciences
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• v.3 no.1
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• pp.8-13
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• 2000

Monthly mean chlorophyll concentration in the East Sea was estimated from the ocean color observed by the Coastal Zone Color Scanner (CZCS) on Nimbus-7 satellite which had performed various remote sensing missions from 1979 to 1986. The areas of high chlorophyll concentration were found in the sea between Siberia coast and Sakhalin Island, in the Donghan Bay and in the Ulleung Basin. In the southwestern East Sea, especially in the area near Ulleung Island, the yearly maximum chlorophyll concentration occurred in December. The chlorophyll concentration in Ulleung Basin in December was about two times higher than during spring bloom in April. The early winter bloom occurred in the warm side of the front that was formed between warm water from the East China Sea and nutrition rich cold water from the northern East Sea.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1295-1305
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• 2021

Satellite-derived ocean color products are required to effectively monitor clear open ocean and coastal water regions for various research fields. For this purpose, accurate correction of atmospheric effect is essential. Currently, the Geostationary Ocean Color Imager (GOCI)-II ground segment uses the reanalysis of meteorological fields such as European Centre for Medium-Range Weather Forecasts (ECMWF) or National Centers for Environmental Prediction (NCEP) to correct gas absorption by water vapor and ozone. In this process, uncertainties may occur due to the low spatiotemporal resolution of the meteorological data. In this study, we develop water vapor absorption correction model for the GK-2 combined GOCI-II atmospheric correction using Advanced Meteorological Imager (AMI) total precipitable water (TPW) information through radiative transfer model simulations. Also, we investigate the impact of the developed model on GOCI products. Overall, the errors with and without water vapor absorption correction in the top-of-atmosphere (TOA) reflectance at 620 nm and 680 nm are only 1.3% and 0.27%, indicating that there is no significant effect by the water vapor absorption model. However, the GK-2A combined water vapor absorption model has the large impacts at the 709 nm channel, as revealing error of 6 to 15% depending on the solar zenith angle and the TPW. We also found more significant impacts of the GK-2 combined water vapor absorption model on Rayleigh-corrected reflectance at all GOCI-II spectral bands. The errors generated from the TOA reflectance is greatly amplified, showing a large error of 1.46~4.98, 7.53~19.53, 0.25~0.64, 14.74~40.5, 8.2~18.56, 5.7~11.9% for from 620 nm to 865 nm, repectively, depending on the SZA. This study emphasizes the water vapor correction model can affect the accuracy and stability of ocean color products, and implies that the accuracy of GOCI-II ocean color products can be improved through fusion with GK-2A/AMI.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.137-147
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• 2014

We proposed and verified the methods to maintain data qualities as well as to reduce data volume for the Geostationary Ocean Color Imager (GOCI), the world's first ocean color sensor operated in geostationary orbit. For the GOCI level-2 data, 92.9% of data volume could be saved by only the data compression. For the GOCI level-1 data, however, just 20.7% of data volume could be saved by the data compression therefore another approach was required. First, we found the optimized number of bits per a pixel for the GOCI level-1 data from an idea that the quantization bit for the GOCI (i.e. 12 bit) was less than the number of bits per a pixel for the GOCI level-1 data (i.e. 32 bit). Experiments were conducted using the $R^2$ and the Modulation Transfer Function (MTF). It was quantitatively revealed that the data qualities were maintained although the number of bits per a pixel was reduced to 14. Also, we performed network simulations using the Network Simulator 2 (Ns2). The result showed that 57.7% of the end-toend delay for a GOCI level-1 data was saved when the number of bits per a pixel was reduced to 14 and 92.5% of the end-to-end delay for a GOCI level-2 data was saved when 92.9% of the data size was reduced due to the compression.

• Proceedings of the KSRS Conference
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• v.1
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• pp.445-445
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• 2006

• Bulletin of the Korean Space Science Society
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• 2009.10a
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• pp.49.3-49.3
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• 2009

Geostationary Ocean Colour Imager (GOCI) is the first ocean color instrument that will be operating in a geostationary orbit from 2010. GOCI will provide the crucial information of ocean environment around the Korean peninsula in high spatial and temporal resolutions at eight visible bands. We report an on-going development of imaging and radiometric performance prediction model for GOCI with realistic data for reflectance, transmittance, absorption, wave-front error and scattering properties for its optical elements. For performance simulation, Monte Carlo based ray tracing technique was used along the optical path starting from the Sun to the final detector plane for a fixed solar zenith angle. This was then followed by simulation of red-tide evolution detection and their radiance estimation, following the in-orbit operational sequence. The simulation results proves the GOCI flight model is capable of detecting both image and radiance originated from the key ocean phenomena including red tide. The model details and computational process are discussed with implications to other earth observation instruments.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.241-246
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• 2004

The ocean signal that reaches the detector of an imaging system after multiple interactions with the atmospheric molecules and aerosols was retrieved from the total signal recorded at the top of the atmosphere (TOA). A simple method referred to as 'Path Extraction' applied to the Landsat-TM ocean imagery of turbid coastal water was compared with the conventional dark-pixel subtraction technique. The shape of the path-extracted water-leaving radiance spectrum resembled the radiance spectrum measured in-situ. The path-extraction was also extended to the SeaWiFS ocean color imagery and compared with the standard SeaWiFS atmospheric correction algorithm, which relays on the assumption of zero water leaving radiance at the two NIR wavebands (765 and 865nm). The path-extracted water-leaving radiance was good agreement with the measured radiance spectrum. In contrast, the standard SeaWiFS atmospheric correction algorithm led to essential underestimation of the water-leaving radiance in the blue-green part of the spectrum. The reason is that the assumption of zero water-leaving radiance at 755 and 865nm fails due to backscattering by suspended mineral particles. Therefore, the near infrared channels 765 and 865nm used fur deriving the aerosol information are no longer valid for turbid coastal waters. The path-extraction is identified as a simple and efficient method of extracting the path radiance largely introduced due to light interaction through the complex atmosphere carried several aerosol and gaseous components and at the air-sea interface.interface.

• Journal of Environmental Science International
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• v.17 no.1
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• pp.85-95
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• 2008

Temporal and spatial variabilities of chlorophyll a (Chl-a) in the northern East China Sea (ECS) are described, using both 8-day composite images of the SeaWiFS (Sea-viewing Wide Field-of-view Sensor) and in-situ data investigated in August and September during 2000-2005. Ocean color imagery showed that Chl-a concentrations on the continental shelf within the 50 m depth in the ECS were above 10 times higher than those of the Kuroshio area throughout the year. Higher concentrations (above $5mg/m^3$) of yearly mean Chl-a were observed along the western part of the shelf near the coast of China. The standard deviation also showed the characteristics of the spatial variability near $122-124^{\circ}E$, where the western region of the East China Sea was grater than that of the eastern region. Particularly the significant concentration of Chl-a, up to $9mg/m^3$, was found at the western part of $125^{\circ}E$ in the in-situ data of 2002. The higher Chl-a concentrations of in-situ data were consistent with low salinity waters of below 30 psu. It means that there were the close relationship between the horizontal distribution of Chl-a and low salinity water.