• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1523-1528
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• 2023

Since the successful launch of Geostationary Ocean Color Imager-II (GOCI-II) in February 2020, various studies for improving the accuracies of the product have been underway through full-scale Cal/Val (calibration and validation) activities. This special issue examines the algorithm for GOCI-II data quality management at present, two years after the start of studies on Cal/Val and algorithm improvement of GOCI-II data, and introduces accuracy improvement and application progress along with the related research results. We expect that highly accurate data will be provided and utilized through continuous Cal/Val activities for GOCI-II data.

• Proceedings of the KSRS Conference
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• v.1
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• pp.86-89
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• 2006

Ocean color remote sensing community currently uses the different solar irradiance spectra covering the visible and near-infrared in the calibration/validation and deriving products of ocean color instruments. These spectra derived from single and / or multiple measurements sets or models have significant discrepancies, primarily due to variation of the solar activity and uncertainties in the measurements from various instruments and their different calibration standards. Thus, it is prudent to examine model-to-model differences and select a standard reference spectrum that can be adopted in the future calibration and validation processes, particularly of the first Geostationary Ocean Color Imager (GOCI) onboard its Communication Ocean and Meterological Satellite (COMS) planned to be launched in 2008. From an exhaustive survey that reveals a variety of solar spectra in the literature, only eight spectra are considered here seeing as reference in many remote sensing applications. Several criteria are designed to define the reference spectrum: i.e., minimum spectral range of 350-1200nm, based completely or mostly on direct measurements, possible update of data and less errors. A careful analysis of these spectra reveals that the Thuillier 2004 spectrum seems to be very identical compared to other spectra, primarily because it represents very high spectral resolution and the current state of the art in solar irradiance spectra of exceptionally low uncertainty ${\sim}0.1%.$ This study also suggests use of the Gueymard 2004 spectrum as an alternative for applications of multispectral/multipurpose satellite sensors covering the terrestrial regions of interest, where it provides spectral converge beyond 2400nm of the Thuillier 2004 spectrum. Since the solar-activity induced spectral variation is about less than 0.1% and a large portion of this variability occurs particularly in the ultraviolet portion of the electromagnetic spectrum that is the region of less interest for the ocean color community, we disregard considering this variability in the analysis of solar irradiance spectra, although determine the solar constant 1366.1 $Wm^{-2}$ to be proposed for an improved approximation of the extraterrestrial solar spectrum in the visible and NIR region.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.159-161
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• 2007

Geostationary Ocean Color Imager (GOCI), the world-first ocean remote sensing instrument on geostationary Communication, Ocean, Meteorological Satellite (COMS), will be able to take a picture of a large region several times a day (almost with every one hour interval). We, KORDI, are in charge for developing the GOCI data processing system (GDPS) which is the basic software for processing the data from GOCI. The GDPS will be based on windows operating system to produce the GOCI level 2 data products (useful for oceanographic environmental analysis) automatically in real-time mode. Also, the GDPS will be a user-interactive program by well-organized graphical user interfaces for data processing and visualization. Its products will be the chlorophyll concentration, amount of total suspended sediments (TSS), colored dissolved organic matters (CDOM) and red tide from water leaving radiance or remote sensing reflectance. In addition, the GDPS will be able to produce daily products such as water current vector, primary productivity, water quality categorization, vegetation index, using individual observation data composed from several subscenes provided by GOCI for each slit within the target area. The resulting GOCI level 2 data will be disseminated through LRIT using satellite dissemination system and through online request and download systems. This software is carefully designed and implemented, and will be tested by sub-contractual company until the end of this year. It will need to be updated in effect with respect to new/improved algorithms and the calibration/validation activities.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.673-679
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• 2016

This study propose a new method to detect Cochlodinium polykrikoides red tide occurring in South Sea of Korea using Water-leaving Radiance data and Absorption Coefficients data of Geostationary Ocean Color Imager (GOCI). C. polykrikoides were analyzed and the irradiance and light emission characteristics of the wavelength range from 412 nm to 555 nm were confirmed. The detection technique proposed in this study detects the red tide occurring in the optically complex South Sea. Based on these results, it can be used for future red tide prevention.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.57-81
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• 2019

Satellite images can be integrated into a crop model to strengthen the advantages of each technique for crop monitoring and to compensate for weaknesses of each other, which can be systematically applied for monitoring inaccessible croplands. The objective of this study was to outline the productivity of paddy rice based on simulation of the yield of all paddy fields in North Korea, using a grid crop model combined with optical satellite imagery. The grid GRAMI-rice model was used to simulate paddy rice yields for inaccessible North Korea based on the bidirectional reflectance distribution function-adjusted vegetation indices (VIs) and the solar insolation. VIs and solar insolation for the model simulation were obtained from the Geostationary Ocean Color Imager (GOCI) and the Meteorological Imager (MI) sensors of the Communication Ocean and Meteorological Satellite (COMS). Reanalysis data of air temperature were achieved from the Korea Local Analysis and Prediction System (KLAPS). Study results showed that the yields of paddy rice were reproduced with a statistically significant range of accuracy. The regional characteristics of crops for all of the sites in North Korea were successfully defined into four clusters through a spatial analysis using the K-means clustering approach. The current study has demonstrated the potential effectiveness of characterization of crop productivity based on incorporation of a crop model with satellite images, which is a proven consistent technique for monitoring of crop productivity in inaccessible regions.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.505-508
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• 2004

Korea Aerospace Research Institute (KARI) has a plan to launch COMS for consistent monitoring of the Korean Peninsula. Korea Geostationary Ocean Color Imager (GOCI) is one of the main payloads of COMS which will provide a monitoring of ocean-colour around the Korean Peninsula from geostationary platforms. Ocean color observation from geostationary platform is required to achieve the proper spatial and temporal resolution for coastal observation mission. In this paper the characteristics of GOCI and LEO sensors are discussed. GOCI will provide the measurement data of 6 visible channels and 2 near-infrared channels (400nm ~ 900nm). The integration time and aperture diameter required to achieve the SNR specification of KGOCI are analyzed.

• Proceedings of the KSRS Conference
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• v.1
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• pp.60-63
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• 2006

Geostationary Ocean Color Imager (GOCI) is under development to provide a monitoring of oceancolor around the Korean Peninsula from geostationary platforms. It is planned to be loaded on Communication, Ocean, and Meteorological Satellite (COMS) of Korea. In this paper radiometric calibration concept of the GOCI is introduced. The GOCI radiometric response is modeled as a nonlinear system in order to reflect a nonlinear characteristic of detector. In this paper estimation approaches for radiometric parameters of GOCI model are discussed. For the GOCI, the offset signal depends on each spectral channel because dark current offset signal is a function of integration time which is different from channel to channel. The offset parameter can be estimated by using offset signal measurements for two integration time setting is described.

• 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.189-207
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• 2010

Several ocean color algorithms have been developed for GOCI (Geostationary Ocean Color Imager) using in-situ bio-optical data sets. These data sets collected around the Korean Peninsula between 1998 and 2009 include chlorophyll-a concentration (Chl-a), suspended sediment concentration (SS), absorption coefficient of dissolved organic matter ($a_{dom}$), and remote sensing reflectance ($R_{rs}$) obtained from 1348 points. The GOCI Chl-a algorithm was developed using a 4-band remote sensing reflectance ratio that account for the influence of suspended sediment and dissolved organic matter. The GOCI Chl-a algorithm reproduced in-situ chlorophyll concentration better than the other algorithms. In the SeaWiFS images, this algorithm reduced an average error of 46 % in chlorophyll concentration retrieved by standard chlorophyll algorithms of SeaWiFS. For the GOCI SS algorithm, a single band was used (Ahn et al., 2001) instead of a band ratio that is commonly used in chlorophyll algorithms. The GOCI $a_{dom}$ algorithm was derived from the relationship between remote sensing reflectance band ratio ($R_{rs}(412)/R_{rs}(555)$) and $a_{dom}(\lambda)$). The GOCI Chl-a fluorescence and GOCI red tide algorithms were developed by Ahn and Shanmugam (2007) and Ahn and Shanmugam (2006), respectively. If the launch of GOCI in June 2010 is successful, then the developed algorithms will be analyzed in the GOCI CAL/VAL processes, and improved by incorporating more data sets of the ocean optical properties data that will be obtained from waters around the Korean Peninsula.

• 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.