• 한국지구과학회:학술대회논문집
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• 2010.04a
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• pp.136-136
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• 2010

해색원격탐사에서 해수 type을 광학적 성질에 따라 Case-I water와 Case-II water로 구분한다. Case-I water는 기본적으로 조류 세포 및 그 쇄설물 그리고 미량의 용존 유기물을 포함하며, 맑은 해수인 대양이 이에 속한다. Case-II water는 부유 무기입자, 육상 기원입자 및 높은 용존 유기물 농도, 인간이 만든 유입물을 포함한 탁한 해수이며, 연안 해역의 해수가 이에 속한다(Gordon, 1983). Case-II water에 속하는 연안 해역의 부유사 농도는 해양 연안 환경 변화를 모니터링 할 수 있는 중요한 지표 중 하나이다. 부유사가 연안 환경, 연안 양식장, 어장 환경에 어떤 영향을 주는지 파악할 수 있다. 채수를 통하여 부유사 농도를 추정하는 것은 시간적 경제적 제약이 따른다. 하지만 위성자료와 실측 자료간의 상관관계가 잘 나타나는 계수를 유도하면, 실측없이 위성영상만 있으면 연속적인 부유사 농도 분포를 볼 수 있을 것으로 기대된다. 이런 목적으로 Landsat ETM+ 자료를 이용하여 광양만의 표층 부유사 농도를 추정하였다. 위성 통과시간에 맞춰 2009년 9월 22일과 11월 25일 2차례에 걸쳐 부유사 농도와 반사도를 광양만 16개 정점에서 측정하였다. 부유사 농도는 표층의 해수를 채수하여 GF/F 필터를 이용해 측정되었고, 반사도는 Spectroradiometer를 사용하여 350nm부터 1050nm까지 1nm 간격으로 측정되었다. Landsat ETM+ 자료와 실측 반사도는 원격반사도 $R_{rs}$로 변환되었고, 두 가지 $R_{rs}$를 실측 부유사 농도와 비교하여 부유사 농도 산출 계수를 유도하였다. 경험적으로 구한 계수를 사용하여 위성영상을 부유사 농도로 계산하였으며, 이는 같은 날의 실측 부유사 농도와 비슷한 공간분포를 나타내었다.

• Journal of the Korean earth science society
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• v.42 no.3
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• pp.247-263
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• 2021

Climate change has been accelerating in coastal waters recently; therefore, the importance of coastal environmental monitoring is also increasing. Chlorophyll-a concentration, an important marine variable, in the surface layer of the global ocean has been retrieved for decades through various ocean color satellites and utilized in various research fields. However, the commonly used chlorophyll-a concentration algorithm is only suitable for application in clear water and cannot be applied to turbid waters because significant errors are caused by differences in their distinct components and optical properties. In addition, designing a standard algorithm for coastal waters is difficult because of differences in various optical characteristics depending on the coastal area. To overcome this problem, various algorithms have been developed and used considering the components and the variations in the optical properties of coastal waters with high turbidity. Chlorophyll-a concentration retrieval algorithms can be categorized into empirical algorithms, semi-analytic algorithms, and machine learning algorithms. These algorithms mainly use the blue-green band ratio based on the reflective spectrum of sea water as the basic form. In constrast, algorithms developed for turbid water utilizes the green-red band ratio, the red-near-infrared band ratio, and the inherent optical properties to compensate for the effect of dissolved organisms and suspended sediments in coastal area. Reliable retrieval of satellite chlorophyll-a concentration from turbid waters is essential for monitoring the coastal environment and understanding changes in the marine ecosystem. Therefore, this study summarizes the pre-existing algorithms that have been utilized for monitoring turbid Case 2 water and presents the problems associated with the mornitoring and study of seas around the Korean Peninsula. We also summarize the prospective for future ocean color satellites, which can yield more accurate and diverse results regarding the ecological environment with the development of multi-spectral and hyperspectral sensors.

• Journal of the Korean earth science society
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• v.43 no.5
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• pp.591-603
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• 2022

The color and optical properties of seawater are determined by the interaction between dissolved organic and inorganic substances and plankton contained in it. The Ieodo - Ocean Research Institute (I-ORS), located in the East China Sea, is affected by the low salinity of the Yangtze River in the west and the Tsushima Warm Current in the south. Thus, it is a suitable site for analyzing the fluctuations in circulation and optical properties around the Korean Peninsula. In this study, seawater surrounding the I-ORS was classified according to its optical characteristics using the satellite remote reflectance observed with Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua and National Aeronautics and Space Administration (NASA) bio-Optical Marine Algorithm Dataset (NOMAD) from January 2016 to December 2020. Additionally, the variation characteristics of optical water types (OWTs) from different seasons were presented. A total of 59,532 satellite match-up data (d ≤ 10 km) collected from seawater surrounding the I-ORS were classified into 23 types using the spectral angle mapper. The OWTs appearing in relatively clear waters surrounding the I-ORS were observed to be greater than 50% of the total. The maximum OWTs frequency in summer and winter was opposite according to season. In particular, the OWTs corresponding to optically clear seawater were primarily present in the summer. However, the same OWTs were lower than overall 1% rate in winter. Considering the OWTs fluctuations in the East China Sea, the I-ORS is inferred to be located in the transition zone of seawater. This study contributes in understanding the optical characteristics of seawater and improving the accuracy of satellite ocean color variables.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1125-1133
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• 2021

This study attempted to understand seawater characteristics like chlorophyll concentration (CHL), total suspended matter concentration (TSM), absorption characteristics, and remote sensing reflectance around Weno Island, Micronesia, located in the South Pacific Ocean near the equator. 50 in-situ measurement from May to June 2013 were analyzed and compared with data from around Korean peninsula. CHL around Weno Island was 0.11-0.49 mg/m³ (average 0.26 mg/m³), and TSM was 0.03-0.31 g/m³, (average 0.16 g/m³), showing typical clear water characteristics. Absorption coefficient of total suspended matters at 443 nm showed over 0.5 times less than that from East Sea and the slope of absorption coefficient spectrum of dissolved organic matter showed much larger than that of Korean peninsula, indicating the concentration of organic matter is very low and dissolved organic matter of marine origin is considered to be the main component in the study area. As a result of comparing the remote sensing reflectance spectrum with that of coastal waters around the Korean peninsula, coastal waters around Weno Island showed typical CASE-1 water properties. It was possible to understand the marine optical characteristics of coral reef habitats in tropical waters, and it can be used to develop seawater algorithms specialized in the study area.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.97-97
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• 2017

천일염의 생산공정은 바닷물(나트륨 농도: 2~3%)을 저수지로 유입시킨 바닷물을 농축하는 1차 증발지(난치)와 2차 증발지(누태)로 보내 일반적으로 2주 정도 증발시켜 나트륨 농도 12~15% 소금물을 만든다. 온도가 낮은 겨울철에는 결정지를 거치거나 2차 증발지의 농축시켜 창고(해주)에서 보관 하는 동안 나트륨 농도 25~27%정도로 상승되어 소금 결정을 이루고 채염하여 창고로 운송한다. 보관창고에서 천일염에서 쓴맛을 내는 간수를 제거한 뒤 선별, 건조 및 포장하는 유통하는 단계로 이뤄진다. 천일염의 생산과정 중 해주는 함수를 저장하여 고농도의 함수를 저장하여 불용분 및 이물질을 자연 침전시켜 맑은 함수를 다음 공정으로 이송시켜 천일염 품질향상에 중요한 공정 중에 하나이다. 그러나 일반적인 염전의 해주는 지붕의 복사열로 인하여 저장중인 해수의 대류로 인하여 침전물이 상승되고 이물질이 부유함으로 인하여 품질저하의 원인이 되고 있다. 또한 해주 구조 및 해주 품질에 대한 자료는 전무한 실정이다. 따라서, 본 연구의 목적은 해주의 깊이에 따른 해수의 대류 방지 깊이 및 품질 변화를 측정하여 고품질 천일염 생산공정에 적용하는데 목적이 있다. 해주분석을 위한 염전은 결정지 채염공정을 공유하는 2개지역 3개 염전을 대상으로 하였으며, 무안, 운남 및 만품지역의 3개소를 선정하여 생산방식이 비교적 동일한 염전을 선정하여 조사 및 분석하였다. 해주의 깊이는 1.0, 3.0, 3.5m인 해주의 시료를 채취하여 Calcium, Magnesium, Sulphate, Chloride 의 함량 분석은 Arena 20 XT 자동흡광분석기 (Automated photometric analyzer)을 이용하여 분석을 하였다. 각 염전에서 채취한 해수의 평균 탁도 값은 해주의 깊이가 깊을수록 탁도 값이 낮은 것으로 나타났다. 해수의 색상 "b" 값은 해주 깊이가 깊을수록 낮은 결과를 보였다. Mg 함량은 전반적으로 15,000~40,000 mg/L 범위의 값을 나타냈으며, Sulphate 함량은 약 30,000~65,000 mg/L 범위의 값을 나타났으며, 깊이가 깊을수록 Mg, Sulphate 함량이 낮아지는 결과를 보였으나 Chloride 함량은 뚜렷한 경향을 보이지 않았다. 실험 결과 해주 깊이에 따른 함수의 품질 차이가 발생하는 것을 확인 할 수 있었으며, 해주의 바닥 깊이가 깊을수록 함수의 불용분 침전, 분리에 효율적인 것으로 판단되었다.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.473-490
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• 2015

There are diverse sea areas within the coverage of GOCI which is observed around the Korea at one-hour intervals. It includes not only very clear ocean of East Sea, but also extremely turbid waters of the Yangtze River estuary. In this study, we analyzed the different optical characteristics of various sea areas using absorption coefficients of phytoplankton, Suspended Particulate Matter(SPM), Dissolved Organic Matter(DOM). Totally 959 sets of bio-optical and marine environmental data were obtained from 2009 to 2014 around the sea area of Korea. The East Sea, South Sea, East China Sea and offshore part of Yellow Sea showed similar pattern having high levels of contribution of phytoplankton and DOM. On the other hands, the coastal part of Mokpo and Gyeonggi Bay showed opposite pattern having high levels of contribution of SPM and DOM. As a result of the algorithm performance for chlorophyll-a(Chl-a) and SPM, Chl-a is mostly overestimated and SPM is mainly tended to be underestimated. Large amount of errors are induced by the SPM rather than the chl-a and DOM. These errors are primarily founded in the coastal waters having relatively high levels of $a_{SPM}$ contribution of more than 60%.

• Korean Journal of Remote Sensing
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• v.18 no.4
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• pp.187-199
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• 2002

Correlation between chlorophyll a in the East China Sea and spectral bands (412, 443, 490, (510), 555, (676, 765)nm) of Ocean Scanning Multi-Spectral Imager (OSMI) including the profile multi-spectral radiometer (PRR-800) was studied. The values of remote sensing reflectance (Rrs) at the bands corresponding to the field chlorophyll $\alpha$ in the East China Sea were much higher than those in clear waters off California, USA. In case of the particle absorptions related to the chlorophyll a concentration at the spectral bands (440, 670nm) were much higher in the East China Sea than the ones in the clean waters off California. The normalized water leaving radiances (nLw) at 412, 443, 490, 555 nm of OSMI and the field chlorophyll a in the East China Sea were correlated each other. According to the results, the relationship between field chlorophyll $\alpha$ and nLw 410 nm in OSMI bands was the lowest, whereas that between field chlorophyll a and nLw 555 nm in the bands was the highest. Reciprocal action between the field chlorophyll a and the band ratio of the OSMI bands (nLw410/nLw555, nLw443/nLw555, nLw490/nLw555) was also studied. Relationship between the chlorophyll $\alpha$ and the band ratio (nLw490/nLw555) was highest in the OSMI bands. Relationship between the chlorophyll $\alpha$ and the ratio (nLw490/nLw555) was higher than one in the nLw410/nLw555. The difference in the estimated chlorophyll $\alpha$ (mg/m$^3$) between OSMI and SeaWiFS (Sea Viewing Wide Field-of-View Sensor) at the special observing stations in the northern eastern sea of Jeju Island in February 25, 2002 was about less than 0.3 mg/m$^3$ within 3 hours. It is suggested that OC2 (ocean color chlorophyll 2 algorithm) be used to get much better estimation of chlorophyll $\alpha$ from OSMI than the ones from the updated algorithms as OC4.

• Korean Journal of Remote Sensing
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• v.16 no.3
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• pp.243-260
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• 2000

Ocean remote sensing reflectance of just above water level was modeled using inherent optical properties of seawater contents, total absorption (a) and backscattering(bb) coefficients ($R_{rs}$=0.046 $b_b$/(a+$b_b$). This modeling was based on the specific absorption and backscattering coefficients of 5 optically active seawater components； phytoplankton pigments, non-chlorophyllous suspended particles, dissolved organic matters, heterotrophic microorganisms, and the other unknown particle components. Simulated remote sensing reflectance($R_{rs}$) and water leaving radiance(Lw) spectra were well agreed with in-situ measurements obtained using a bi-directional fields remote spectrometer in coastal waters and open ocean. $R_{rs}$ values in SeaWiFS bands from the model were analyzed to develop 2-band ratio ocean color chlorophyll with those observed insitu. Also, chlorophyll algorithm based on remote reflectance developed in this study fell in those obtained by a SeaBAM working group. The model algorithms were examined and compared with those observed insitu. Also, chlorophyll algorithm based on remote reflectance developed in this study fell in those obtained by a SeaBAM working group. The remote reflectance model will be very helpful to understand the variation of water leaving radiances caused by the various components in the seawater, and to develop new ocean color algorithm for CASE-II water using neural network method or other analytical method, and in the model of fine atmospheric signal correction.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.1
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• pp.7-14
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• 1995

As a part of the multidisciplinary oceanographic study for the productivity enhancement in Deukryang Bay, temperature and salinity were observed from 1992 through 1993. From the results, only the data in summer of two years are compared. Owing to the contrary meteorolgical conditions in both summers both of temperature and salinity had the patterns of horizontal distributions quite different from each other. In 1992 with low precipitation, there was a tendency of temperature increase and salinity decrease from the bay mouth towards the bay head. In 1993 when the air temperature was abnormally low, isotherms and isotherms tended to be parallel to the local u3s of the bay where the warmer and less saline water distributed along the western coast. Reduced solar radiation and increase in the relative importance of the distribution of properties along the current that was parallel to the axis of the bay could be responsible for this result. Vertical structures of both temperature and salinity were dependent on the stirring effect of tidal current. Stratification was destroyed during the spring tide while it was formed during the neap tide.

• 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.