• 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.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.88-91
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• 2008

The COMS IMPS (Communication Ocean and Meteorological Satellite IMage Pre-processing Subsystem) is developed for image pre-processing of COMS. For a test of the COMS IMPS, 7 support software are developed in KARI GS using simulated MI/GOCI WB (Wide-Band) data; COMS Fill Adder, MI (Meteorological Imager) CADU generator, GOCI (Geostationary Ocean Colour Imager) CADU generator, COMS CADU combiner, MI SD (Sensor Data) analyzer, GOCI SD analyzer, and COMS DM (Decomposition Module) test harness. This paper explains functions of developed support software and the COMS IMPS test using those software.

• Korean Journal of Remote Sensing
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• v.28 no.3
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• pp.277-285
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• 2012

The GOCI imagery can be an effective alternative to monitor short-term changes over terrestrial environments. This study aimed to assess the radiometric characteristics of the GOCI multispectral imagery for land applications. As an initial approach, we compared GOCI at-sensor radiance with MODIS data obtained simultaneously. Dynamic range of GOCI radiance was larger than MODIS over land area. Further, the at-sensor radiance over various land surface targets were tested by vicarious calibration. Surface reflectance were directly measured in field using a portable spectrometer and indirectly derived from the atmospherically corrected MODIS product over relatively homogeneous sites of desert, tidal flat, bare soil, and fallow crop fields. The GOCI radiance values were then simulated by radiative transfer model (6S). In overall, simulated radiance were very similar to the actual radiance extracted from GOCI data. Normalized difference vegetation index (NDVI) calculated from the GOCI bands 5 and 8 shows very close relationship with MODIS NDVI. In this study, the GOCI imagery has shown appropriate radiometric quality to be used for various land applications. Further works are needed to derive surface reflectance over land area after atmospheric correction.

• 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.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1577-1589
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• 2023

Recently, the necessity of predicting changes for monitoring ocean is widely recognized. In this study, we performed a time series prediction of remote-sensing reflectance (Rrs), which can indicate changes in the ocean, using Geostationary Ocean Color Imager (GOCI) data. Using GOCI-I data, we trained a multi-scale Convolutional Long-Short-Term-Memory (ConvLSTM) which is proposed in this study. Validation was conducted using GOCI-II data acquired at different periods from GOCI-I. We compared model performance with the existing ConvLSTM models. The results showed that the proposed model, which considers both spatial and temporal features, outperformed other models in predicting temporal trends of Rrs. We checked the temporal trends of Rrs learned by the model through long-term prediction results. Consequently, we anticipate that it would be available in periodic change detection.

• Proceedings of the KSRS Conference
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• v.1
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• pp.90-93
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• 2006

The first Geostationary Ocean Color Imager (GOCI) onboard its Communication Ocean and Meteorological Satellite (COMS) is scheduled for launch in 2008. GOCI includes the eight visible-to-near-infrared (NIR) bands, 0.5km pixel resolution, and a coverage region of 2500 ${\times}$ 2500km centered at 36N and 130E. GOCI has had the scope of its objectives broadened to understand the role of the oceans and ocean productivity in the climate system, biogeochemical variables, geological and biological response to physical dynamics and to detect and monitor toxic algal blooms of notable extension through observations of ocean color. The special feature with GOCI is that like MODIS, MERIS and GLI, it will include the band triplets 660-680-745 for the measurements of sun-induced chlorophyll-a fluorescence signal from the ocean. The GOCI will provide SeaWiFS quality observations with frequencies of image acquisition 8 times during daytime and 2 times during nighttime. With all the above features, GOCI is considered to be a remote sensing tool with great potential to contribute to better understanding of coastal oceanic ecosystem dynamics and processes by addressing environmental features in a multidisciplinary way. To achieve the objectives of the GOCI mission, we develop the GOCI Data Processing System (GDPS) which integrates all necessary basic and advanced techniques to process the GOCI data and deliver the desired biological and geophysical products to its user community. Several useful ocean parameters estimated by in-water and other optical algorithms included in the GDPS will be used for monitoring the ocean environment of Korea and neighbouring countries and input into the models for climate change prediction.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.275-292
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• 2014

The objective of this study is to determine Tasseled Cap Transformation (TCT) coefficients for the Geostationary Ocean Color Imager (GOCI). TCT is traditional method of analyzing the characteristics of the land area from multi spectral sensor data. TCT coefficients for a new sensor must be estimated individually because of different sensor characteristics of each sensor. Although the primary objective of the GOCI is for ocean color study, one half of the scene covers land area with typical land observing channels in Visible-Near InfraRed (VNIR). The GOCI has a unique capability to acquire eight scenes per day. This advantage of high temporal resolution can be utilized for detecting daily variation of land surface. The GOCI TCT offers a great potential for application in near-real time analysis and interpretation of land cover characteristics. TCT generally represents information of "Brightness", "Greenness" and "Wetness". However, in the case of the GOCI is not able to provide "Wetness" due to lack of ShortWave InfraRed (SWIR) band. To maximize the utilization of high temporal resolution, "Wetness" should be provided. In order to obtain "Wetness", the linear regression method was used to align the GOCI Principal Component Analysis (PCA) space with the MODIS TCT space. The GOCI TCT coefficients obtained by this method have different values according to observation time due to the characteristics of geostationary earth orbit. To examine these differences, the correlation between the GOCI TCT and the MODIS TCT were compared. As a result, while the GOCI TCT coefficients of "Brightness" and "Greenness" were selected at 4h, the GOCI TCT coefficient of "Wetness" was selected at 2h. To assess the adequacy of the resulting GOCI TCT coefficients, the GOCI TCT data were compared to the MODIS TCT image and several land parameters. The land cover classification of the GOCI TCT image was expressed more precisely than the MODIS TCT image. The distribution of land cover classification of the GOCI TCT space showed meaningful results. Also, "Brightness", "Greenness", and "Wetness" of the GOCI TCT data showed a relatively high correlation with Albedo ($R^2$ = 0.75), Normalized Difference Vegetation Index (NDVI) ($R^2$ = 0.97), and Normalized Difference Moisture Index (NDMI) ($R^2$ = 0.77), respectively. These results indicate the suitability of the GOCI TCT coefficients.

• 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.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1529-1539
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• 2023

The data from Geostationary Ocean Color Imager-II (GOCI-II), which observes the color of the sea to monitor marine environments, undergoes various correction processes in the ground station system, producing data from Raw to Level-2 (L2). Quality issues arising at each processing stage accumulate step by step, leading to an amplification of errors in the satellite data. To address this, improvements were made to the GOCI-II ground station system to measure potential optical quality and geolocation accuracy errors in the Level-1A/B (L1A/B) data. A newly established Radiometric and Geometric Performance Assessment Module (RGPAM) now measures five optical quality factors and four geolocation accuracy factors in near real-time. Testing with GOCI-II data has shown that RGPAM's functions, including data processing, display and download of measurement results, work well. The performance metrics obtained through RGPAM are expected to serve as foundational data for real-time radiometric correction model enhancements, assessment of L1 data quality consistency, and the development of reprocessing strategies to address identified issues related to the GOCI-II detector's sensitivity degradation.

• Spatial Information Research
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• v.19 no.1
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• pp.29-40
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• 2011

GOCI the world first Ocean Color Imager in Geostationary Orbit, which could obtain total 8 images of the same region a day, however, its spatial resolution(500m) is not enough to use for the accurate land application, Super Resolution(SR), reconstructing the high resolution(HR) image from multiple low resolution(LR) images introduced by computer vision field. could be applied to the time-series remotely sensed images such as GOCI data, and the higher resolution image could be reconstructed from multiple images by the SR, and also the cloud masked area of images could be recovered. As the precedent study for developing the efficient SR method for GOCI images, on this research, it reproduced the simulated data under the acquisition process of the remote sensed data, and then the simulated images arc applied to the proposed algorithm. From the proposed algorithm result of the simulated data, it turned out that low resolution(LR) images could be registered in sub-pixel accuracy, and the reconstructed HR image including RMSE, PSNR, SSIM Index value compared with original HR image were 0.5763, 52.9183 db, 0.9486, could be obtained.