• 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.25 no.1
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• pp.1-10
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• 2009

We studied on the relationship between oceanic variation in the offshore and abnormal sea surface temperature rise in the coastal area of the Yellow Sea using a variety of satellite and in-situ data during winter 2004. In results of the satellite data, the average value of sea surface temperature in the Yellow Sea for 2003 was $10^{\circ}C$, and the average value of sea surface temperature for 2004 was $13^{\circ}C$. It was higher than those of the last year about $3^{\circ}C$. In results of the in-situ data, the average value of surface layer temperature in the Yellow Sea for 2003 was $9.85^{\circ}C$, and the average value of surface layer temperature for 2004 was $12.17^{\circ}C$. In the same satellite data, it was higher than those of the last year about $3^{\circ}C$. In results of the T-S diagram, we divided definitely into water mass of the Yellow Sea and the East China Sea in 2003. But we didn't divide definitely into water mass of the Yellow Sea and the East China Sea in 2004. The average values of air temperature and wind speed for 2003 were $5.23^{\circ}C$ and 4.81 m/s, respectively. And, the average values of air temperature and wind speed for 2004 were $5.61^{\circ}C$ and 4.52 m/s, respectively. So, These were similar. But the wind directions for 2003 were superior northwestern wind, and the wind directions for 2004 were various northern wind. The wind directions were different from each other. Therefore, the abnormal sea surface temperature rise in the coastal area of the Yellow Sea during winter 2004 were better related to oceanic variation in the offshore than influences of atmosphere. In the future, We will do in-depth study for these.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.3
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• pp.291-297
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• 2008

The launch of the COMS by using Ariane-5 launch vehicle is introduced. First, the COMS is introduced briefly, and then, the Ariane-5 launch vehicle is introduced including detail description of the improvement of Vulcain-1 engine of Ariane-5G to Vulcain-2 engine of Ariane-5ECA for 20% increase of thrust. Then, the launch process of the COMS is introduced. The COMS will be launched from the Guiana Space Center in Kourou, French Guiana. After the final check at PPF the COMS is transferred to HPF in the same building for fueling, and it is integrated to the launch vehicle adaptor at HPF, too. Then, this assembly is transferred to Final Assembly Building. After the satellites to be launched together are integrated to the launch vehicle on the launch table in the Final Assembly Building, the launch table loaded with the launch vehicle is moved to the launch pad for launch. The events during the launch vehicle flight is also introduced.

• 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.38 no.6_2
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• pp.1653-1661
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• 2022

In this study, the classification of cold water and normal water based on Geo-Kompsat 2A images was performed. Daily mean surface temperature products provided by the National Meteorological Satellite Center (NMSC) were used, and convolution neural network (CNN) deep learning technique was applied as a classification algorithm. From 2019 to 2022, the cold water occurrence data provided by the National Institute of Fisheries Science (NIFS) were used as the cold water class. As a result of learning, the probability of detection was 82.5% and the false alarm ratio was 54.4%. Through misclassification analysis, it was confirmed that cloud area should be considered and accurate learning data should be considered in the future.

• 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.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.587-592
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• 2004

현재 원격탐사 자료는 각 분야 혹은 기관별로 개별적으로 자료 관리, 정보 생산이 이루어지고 있어 활용성이 저하되고 있으며 목적에 따라 활용하는 자료가 다양한 상황에서 활용분야별 특성을 고려한 기술 개발 및 기반구축의 필요성이 대두되고 있다. 이러한 배경에서 공공기술연구회 소속 4개 기관 (항우연, 지질연, 해양연, 건기연)은 2002년 4월 지상분야, 건설분야, 해양분야 등 전문분야별로 원격탐사센터를 설립하여 공공활용 서비스를 위한 국가원격탐사센터로서의 역할을 수행할 수 있도록 협조체계를 구축하였다. 한국건설기술연구원의 건설원격탐사센터에서는 건설 분야와 관련된 지구관측위성 자료의 분석ㆍ활용 및 체계적인 영상 Data Base를 구축하고 이를 Web을 통해 서비스 할 수 있는 체계를 구축하였다. 현재 건설원격탐사센터 홈페이지(http：//krsc.kict.re.kr)는 기 구축된 수자원/하천분야 관련 공간자료를 Web을 통해 서비스하고 있으며 공간자료의 분석을 통해 획득한 연구 성과들을 지속적으로 갱신하고 있다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.11a
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• pp.41-42
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• 2019

해양공간의 효율적인 활용과 해상사고 예방을 위하여 해상교통 현황 파악이 필요하다. 이를 위해서는 해상에서 운항하는 선박에 대한 면밀한 모니터링이 선행 되어야한다. 때문에 본 연구에서는 선박자동식별장치(Automatic Identification System, AIS)와 선박패스(V-Pass)를 활용하는 기존 모니터링 방법에서 나아가, 위성 자료를 활용한 연안 선박감시 방법을 해상교통관제(Vessel Traffic Service, VTS) 센터에서 활용하기 위한 방안을 제안한다. 위성 자료는 광범위한 영역에 대하여 다양한 정보를 획득할 수 있는 장점을 지니므로, 부산항 연안에서 수집한 AIS 데이터와 함께 딥 러닝 기반 선박 탐지 및 분류 모델에 활용함으로써, 보다 개선된 모니터링을 기대할 수 있다. 이를 활용하여 미식별 선박들의 출현 위치를 분석하고 나아가 선박의 종류를 예측함으로써, 상세한 해상교통 현황 파악 및 예측을 기대할 수 있다. 향후에는 선박의 종류 뿐 아니라 각 선박의 해상활동을 분석함으로써, 보다 체계적이고 실용적인 해양공간활용 계획수립에 도움이 될 수 있도록 개선할 계획이다.

• 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.39 no.6_2
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• pp.1615-1633
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• 2023

Sargassum horneri is one of the floating algae in the sea, which breeds in large quantities in the Yellow Sea and East China Sea and then flows into the coast of Republic of Korea, causing various problems such as destroying the environment and damaging fish farms. In order to effectively prevent damage and preserve the coastal environment, the development of Sargassum horneri detection algorithms using satellite-based remote sensing technology has been actively developed. However, incorrect detection information causes an increase in the moving distance of ships collecting Sargassum horneri and confusion in the response of related local governments or institutions,so it is very important to minimize false detections when producing Sargassum horneri spatial information. This study applied technology to automatically remove false detection results using the GOCI-II-based Sargassum horneri detection algorithm of the National Ocean Satellite Center (NOSC) of the Korea Hydrographic and Oceanography Agency (KHOA). Based on the results of analyzing the causes of major false detection results, it includes a process of removing linear and sporadic false detections and green algae that occurs in large quantities along the coast of China in spring and summer by considering them as false detections. The technology to automatically remove false detection was applied to the dates when Sargassum horneri occurred from February 24 to June 25, 2022. Visual assessment results were generated using mid-resolution satellite images, qualitative and quantitative evaluations were performed. Linear false detection results were completely removed, and most of the sporadic and green algae false detection results that affected the distribution were removed. Even after the automatic false detection removal process, it was possible to confirm the distribution area of Sargassum horneri compared to the visual assessment results, and the accuracy and precision calculated using the binary classification model averaged 97.73% and 95.4%, respectively. Recall value was very low at 29.03%, which is presumed to be due to the effect of Sargassum horneri movement due to the observation time discrepancy between GOCI-II and mid-resolution satellite images, differences in spatial resolution, location deviation by orthocorrection, and cloud masking. The results of this study's removal of false detections of Sargassum horneri can determine the spatial distribution status in near real-time, but there are limitations in accurately estimating biomass. Therefore, continuous research on upgrading the Sargassum horneri monitoring system must be conducted to use it as data for establishing future Sargassum horneri response plans.