• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1281-1293
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• 2021

The recent launch of the GOCI-II enables South Korea to have the world's first capability in deriving the ocean color data at geostationary satellite orbit for about 20 years. It is necessary to develop a consistent long-term ocean color time-series spanning GOCI to GOCI-II mission and improve the accuracy through validation using in situ data. To assess the GOCI-II's early mission performance, the objective of this study is to compare the GOCI-II Chlorophyll-a concentration (Chl-a), Colored Dissolved Organic Matter (CDOM), and remote sensing reflectances (R_rs) through comparison with the GOCI data. Overall, the distribution of GOCI-II Chl-a corresponds with that of the GOCI over the Yellow Sea, Korea Strait, and the Ulleung Basin. In particular, a smaller RMSE value (0.07) between GOCI and GOCI-II over the summer Ulleung Basin confirms the GOCI-II data's reliability. However, despite the excellent correlation, the GOCI-II tends to overestimate Chl-a than the GOCI over the Yellow Sea and Korea Strait. The similar over-estimation bias of the GOCI-II is also notable in CDOM. Whereas no significant bias or error is found for R_rs at 490 nm and 550 nm (RMSE~0), the underestimation of R_rs at 443 nm contributes to the overestimation of GOCI-II Chl-a and CDOM over the Yellow Sea and the Korea Strait. Also, we show over-estimation of GOCI-II R_rs at 660 nm relative to GOCI to cause a possible bias in Total suspended sediment. In conclusion, this study confirms the initial reliability of the GOCI-II ocean color products, and upcoming update of GOCI-II radiometric calibration will lessen the inconsistency between GOCI and GOCI-II ocean color products.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1317-1328
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• 2021

This study analyzes the early satellite mission marine fog detection results from Geostationary Ocean Color Imager-II (GOCI-II). We investigate optical characteristics of the GOCI-II spectral bands for marine fog between October 2020 and March 2021 during the overlapping mission period of Geostationary Ocean Color Imager (GOCI) and GOCI-II. For Rayleigh-corrected reflection (R_rc) at 412 nm band available for the input of the GOCI-II marine fog algorithm, the inter-comparison between GOCI and GOCI-II data showed a small Root Mean Square Error (RMSE) value (0.01) with a high correlation coefficient (0.988). Another input variable, Normalized Localization Standard (NLSD), also shows a reasonable correlation (0.798) between the GOCI and GOCI-II data with a small RMSE value (0.007). We also found distinctive optical characteristics between marine fog and clouds by the GOCI-II observations, showing the narrower distribution of all bands' R_rc values centered at high values for cloud compared to marine fog. The GOCI-II marine fog detection distribution for actual cases is similar to the GOCI but more detailed due to the improved spatial resolution from 500 m to 250 m. The validation with the automated synoptic observing system (ASOS) visibility data confirms the initial reliability of the GOCI-II marine fog detection. Also, it is expected to improve the performance of the GOCI-II marine fog detection algorithm by adding sufficient samples to verify stable performance, improving the post-processing process by replacing real-time available cloud input data and reducing false alarm by adding aerosol information.

• 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.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1697-1707
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• 2021

On 19 February 2020, the 2nd Geostationary Ocean Color Imager (GOCI-II), a maritime sensor of GEO-KOMPSAT-2B, was launched. The GOCI-II instrument expands the scope of aerosol retrieval research with its improved performance compared to the former instrument (GOCI). In particular, the newly included UV band at 380 nm plays a significant role in improving the sensitivity of GOCI-II observations to the absorbing aerosols. In this study, we calculated the aerosol index and detected absorbing aerosols from January to June 2021 using GOCI-II 380 and 412 nm channels. Compared to the TROPOMI aerosol index, the GOCI-II aerosol index showed a positive bias, but the dust pixels still could be clearly distinguished from the cloud and clear pixels. The high GOCI-II aerosol index coincided with ground-based observations indicating dust aerosols were detected. We found that 70.5% of dust and 80% of moderately-absorbing fine aerosols detected from the ground had GOCI-II aerosol indices larger than the 75th percentile through the whole study period.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1229-1234
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• 2021

GOCI-II, which succeeded the mission of GOCI, was successfully launched in February 2020 and is in operation. GOCI-II is expected to be highly useful in a wide range of fields, including detailed changes in the coastal seawater environment using improved spatial and spectral resolution, increased number of observation and full disk observation mode. This special issue introduces the assessment of the current GOCI-II data quality and the studies on the accuracy improvement and applications at this time of one year after launch and data disclosure. We expect that this issue can be an opportunity for GOCI-II data to be actively utilized not only in the ocean but also in various fields of land and atmosphere.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.277-285
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• 2010

Geostationary Ocean Color Imager(GOCI-I), the world's first space-borne ocean color observation geostationary satellite, will be launched on June 2010. Development of GOCI-I took about 6 years, and its expected lifetime is about 7 years. The mission and user requirements of GOCI-II are required to be defined at this moment. Because baseline of the main mission of GOCI-II must be defined during the development time and early operational period of GOCI-I. The main difference between these missions is the global-monitoring capability of GOCI-II, which will meet the necessity of the monitoring and research on climate change in the long-term. The user requirements of GOCI-II will have higher spatial resolution, $250m{\times}250m$, and 12 spectral bands to fulfill GOCI-I's user request, which could not be implemented on GOCI-I for technical reasons. A dedicated panchromatic band will be added for the nighttime observation to obtain fishery information. GOCI-II will have a new capability, supporting user-definable observation requests such as clear sky area without clouds and special-event areas, etc. This will enable higher applicability of GOCI-II products. GOCI-II will perform observations 8 times daily, the same as GOCI-I's. Additionally, daily global observation once or twice daily is planned for GOCI-II. In this paper, we present an improved development and organization structure to solve the problems that have emerged so far. The hardware design of the GOCI-II will proceed in conjunction with domestic or foreign space agencies.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.171-176
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• 2017

Recently, several information-technology research projects such as those for high-performance computing, the cloud service, and the DevOps methodology have been advanced to develop the efficiency of satellite data-processing systems. In March 2019, the Geostationary Ocean Color Imager II (GOCI-II) will be launched for its predictive capability regarding marine disasters and the management of the fishery environment; moreover, the GOCI-II Ground Segment (G2GS) system for data acquisition/processing/storing/distribution is being designed at the Korea Ocean Satellite Center (KOSC). The G2GS is composed of the following six functional subsystems: data-acquisition subsystem (DAS), data-correction subsystem (DCS), precision-correction subsystem (PCS), ocean data-processing subsystem (ODPS), data-management subsystem (DMS), and operation and quality management subsystem (OQMS). The G2GS will enable the real-time support of the GOCI-II ocean-color data for government-related organizations and public users.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1235-1243
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• 2021

Geostationary Ocean Color Imager 2 (GOCI-II) on Geo-KOMPSAT-2 (GK2B)satellite was developed as a mission successor of GOCI on COMS which had been operated for around 10 years since launch in 2010 to observe and monitor ocean color around Korean peninsula. GOCI-II on GK2B was successfully launched in February of 2020 to continue for detection, monitoring, quantification, and prediction of short/long term changes of coastal ocean environment for marine science research and application purpose. GOCI-II had already finished IAC and IOT including early in-orbit calibration and had been handed over to NOSC (National Ocean Satellite Center) in KHOA (Korea Hydrographic and Oceanographic Agency). Radiometric calibration was periodically conducted using on-board solar calibration system in GOCI-II. The final calibrated gain and offset were applied and validated during IOT. And three video parameter sets for one day and 12 video parameter sets for a year was selected and transferred to NOSC for normal operation. Star measurement-based INR (Image Navigation and Registration) navigation filtering and landmark measurement-based image geometric correction were applied to meet the all INR requirements. The GOCI2 INR software was validated through INR IOT. In this paper, status and results of IOT, radiometric calibration and INR of GOCI-II are analysed and described.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1259-1268
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• 2021

The 2nd Geostationary Ocean Color Imager (GOCI-II) is the successor to the Geostationary Ocean Color Imager (GOCI), which employs one near-ultraviolet wavelength (380 nm) and eight visible wavelengths(412, 443, 490, 510, 555, 620, 660, 680 nm) and three near-infrared wavelengths(709, 745, 865 nm) to observe the marine environment in Northeast Asia, including the Korean Peninsula. However, the multispectral radiance image observed at satellite altitude includes both the water-leaving radiance and the atmospheric path radiance. Therefore, the atmospheric correction process to estimate the water-leaving radiance without the path radiance is essential for analyzing the ocean environment. This manuscript describes the GOCI-II standard atmospheric correction algorithm and its initial phase validation. The GOCI-II atmospheric correction method is theoretically based on the previous GOCI atmospheric correction, then partially improved for turbid water with the GOCI-II's two additional bands, i.e., 620 and 709 nm. The match-up showed an acceptable result, with the mean absolute percentage errors are fall within 5% in blue bands. It is supposed that part of the deviation over case-II waters arose from a lack of near-infrared vicarious calibration. We expect the GOCI-II atmospheric correction algorithm to be improved and updated regularly to the GOCI-II data processing system through continuous calibration and validation activities.

• 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.