• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.74-79
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• 2017

The Geostationary Ocean Color Imager (GOCI), the world's first ocean color sensor operated in a geostationary orbit, can be utilized to mitigate damages by monitoring marine disasters in real time such as red tides, green algae, sargassum, cold pools, typhoons, and so on. In this paper, we described a methodology and procedure for processing GOCI data in order to maximize its utilization potential. The GOCI data processing procedure is divided into data reception, data processing, and data distribution. The kinds of GOCI data are classified as raw, level 1, and level 2. "Raw" refers to an unstructured data type immediately generated after reception by satellite communications. Level 1 is defined as a radiance data type of two dimensions, generated after radiometric and geometric corrections for raw data. Level 2 indicates an ocean color data type from level-1 data using ocean color algorithms.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.231.1-231.1
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• 2012

한국해양과학기술원 해양위성센터에서 주관운영을 수행하고 있는 천리안 위성의 해양탑재체인 천리안 해양관측위성(이하 GOCI)은 정지궤도위성용 해색센서로서, 태양을 광원으로 지구상의 해수 표면 부근에서 반사되어 대기를 통과한 가시광 및 근적외 대역을 8개 밴드로 분광하여 관측하는 센서이다. 해색센서의 경우, 일반적으로 센서에 입사되는 광신호의 약 90%가 대기에 의한 신호이며, 약 10%에 해당되는 신호만 원래 관측목적인 해수에 의한 신호이기 때문에, 5% 이내의 높은 복사보정 정확도가 요구된다. 이러한 높은 복사보정 정확도를 만족시키기 위해서는, 지상에서의 현장관측을 통한 위성자료 검보정 뿐만 아니라, 발사 후 위성 궤도상에서 센서의 복사보정을 수행하는 궤도상 복사보정이 체계적으로 수행되어야 한다. GOCI는 태양을 기준광원으로 하는 태양광 복사보정을 채택하여, 센서의 셔터부에 태양광 복사보정을 위한 2개의 태양광확산기(Solar Diffuser)를 장비하고 있다. 본 발표에서는 궤도상 시험 후 약 16개월에 걸친 궤도상 복사보정 운영결과와 관련하여, 발사 후 일별, 월별, 계절별 등 각 기간별 센서의 이득변화를 관찰하였으며, 그 결과 1년을 기준으로 약 3% 범위로 주기적인 이득 변화가 있음을 확인하였다. 지상시험결과와의 비교에 의해, 태양광확산기에 대한 태양입사각이 이러한 주기적인 이득 변화의 주 원인임을 궤도상 복사보정 운영결과를 통해 밝히고자 한다.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.1
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• pp.36-46
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• 2013

The Geostationary Ocean Color Imager (GOCI), the first geostationary ocean color observation instrument launched in 2010 on board the Communication, Ocean, and Meteorological Satellite (COMS), has been generating the operational level 1 data. This study describes a methodology for creating the GOCI true color image and data processing software, namely the GOCI RGB maker. The algorithm uses a generic atmospheric correction and reprojection technique to produce the color composite image. Especially, the program is designed for educational purpose in a way that the region of interest and image size can be determined by the user. By distributing software to public, it would maximize the understanding and utilizing the GOCI data. Moreover, images produced from the geostationary observations are expected to be an excellent tool for monitoring environmental changes.

• 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.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.129-136
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• 2014

This study deals with red tide detection by using the remote sensing imagery from the Geostationary Ocean Color Imager (GOCI), the world's first geostationary orbit satellite, around the southern coast of Korea where the most severe red tide occurred recently. The red tide zone was determined by the available data selection from the GOCI imagery during the period of red tide occurrence and also the severe red tide zone was detected through the spatial analysis by temporal change out of the red tide zone. This study results showed that the coast in the vicinity of the Hansan and Yokji in Tongyeong-si was classified into the severe red tide zone, and that the red tide was likely to spread from the coast of Hansan and Yokji to the one of Sanyang-eub. In addition, the comparative analysis between the area of red tide occurrence, the prevention activities of Gyeongsangnam-do provincial government and the amount of the damage cost over time showed close correlation among them. It is still early to conclude that the study is showing the severe red tide zone and the spread path exactly due to various factors for red tide occurrence and activities. In order to improve the reliability of the results, the more data analysis is required.

• 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.

• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.255-266
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• 2012

The enormous disaster (Friday nightmare) occurred at 14:46 (JST) (05:46 UTC) on 11 March 2011, officially named "the 2011 Tohoku Earthquake and Tsunami". To monitor the variations of the marine environment after the earthquake, we used chlorophyll and Rrs(555) of GOCI and MODIS ocean color satellite data during March ~ May 2011. Before the earthquake, chlorophyll and Rrs(555) were relatively low around the Sendai areas. After the earthquake;their concentration and intensity were suddenly increased along the coast and the water column was disturbed by the tsunami wave. The severe distortions influenced by the tsunami occurred at less than 30 m water depth and the variations in offshore were difficult to discern the effect of the tsunami. The disturbance by the tsunami was still remained in the terrestrial environment after one month. However the ocean environment returned to the former condition in almost two month later.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1565-1576
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• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1245-1257
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• 2021

GOCI-II, succeeding the mission of GOCI, was launched in February 2020 and has been in regular operation since October 2020. Korea Institute of Ocean Science and Technology (KIOST) processes and produces in real time Level-1B and 26 Level-2 outputs, which then are provided by Korea Hydrographic and Oceanographic Agency (KHOA). We introduced current status of regular GOCI-II operation and showed future improvement. Basic GOCI-II products including chlorophyll-a, total suspended materials, and colored dissolved organic matter concentration, are induced by OC4 and YOC algorithms, which were described in detail. For the full disk (FD), imaging schedule was established considering solar zenith angle and sun glint during the in-orbital test, but improved by further considering satellite zenith angle. The number of slots satisfying the condition 'Best Ocean' significantly increased from 15 to 78. GOCI-II calibration requirements were presented based on that by European Space Agency (ESA) and candidate fixed locations for calibrating local observation area were. The quality management of FD uses research ships and overseas bases of KIOST, but it is necessary to establish an international calibration/validation network. These results are expected to enhance the understanding of users for output processing and help establish detailed plans for future quality management tasks.

• 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.