• Title/Summary/Keyword: Ocean Color

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Introduction to COMS Geostationary Ocean Color Imager

  • Kang Gumsil;Kim Jongah;Myung Hwan-Chun;Yeon Jeong-Heum;Kang Song-Doug;Youn Heong-Sik
    • Proceedings of the KSRS Conference
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    • pp.108-111
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    • 2005
  • The Communication Ocean, Meteorological Satellite (COMS) as the one of the national space program has been developed by Korea Aerospace Research Institute (KARl). The Geostationary Ocean Color Imager (GOCI) is one of the main payloads ofCOMS which will provide consistent monitoring of ocean-colour around the Korean Peninsula from geostationary platforms. The ocean color observation from geostationary platform is required to remedy the coverage constraints imposed by polar orbiting platforms. In this paper the main characteristics of GOCI are described and compared with the current ocean color sensors. The GOCI will provide the measurement data of 6 visible channels and 2 nearinfrared channels (40Onm - 900nm). The high radiometric sensitivity is essential of ocean color sensor because of the weak water leaving radiance.

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Development of the Regional Algorithms to Quantify Chlorophyll a and Suspended Solid in the Korean Waters using Ocean Color (한국 근해 Ocean Color 위성자료의 정량화)

  • Suh Young Sang;Jang Lee Hyun;Lee Na Kyung;Kim Bok Kee
    • Korean Journal of Fisheries and Aquatic Sciences
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    • v.35 no.3
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    • pp.207-215
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    • 2002
  • Ocean color properties can be quantified by the relationship between the band ratios of the sensor on the ocean color satellites and the measured field ocean color parameters, A tool to determine the abundance of primary organism using the observed ocean color properties from satellite is presented. Coincident to ocean color satellite passes over the Korean waters, the research vessels were deployed to survey the East Sea, the South Sea and the West Sea around the Korean waters, We have been able to have more than 101) data sets containing coincident in situ chlorophyll a and the estimated chlorophyll a derived from SeaWiFS (Sea-viewing Wide Field-of-view Sensor) from february, 1999 to October, 2001. We were able to develop three proper regional algorithms for the East Sea, the South Sea and the West Sea of the Korean peninsula to estimate chlorophyll a, and set up regional algorithms to quantify the suspended solid in the southern sea of the Korean peninsula, Futhermore we were successful in finding out a simple way of estimating chlorophyll a in the turbid water (Case 2 water) using the relationship between in situ chlorophyll a and the estimated chlorophyll a from the processed level 2 data, using the NASA's global algorithm.

Data Processing System for the Geostationary Ocean Color Imager (GOCI) (천리안해양관측위성을 위한 자료 처리 시스템)

  • Yang, Hyun;Yoon, Suk;Han, Hee-Jeong;Heo, Jae-Moo;Park, Young-Je
    • 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 Analysis of GOCI CDOM for Observation of Ocean Environment Change (해양환경변화관측을 위한 GOCI CDOM 자료 분석)

  • Jeong, Jong-Chul
    • Journal of Environmental Impact Assessment
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    • v.22 no.4
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    • pp.389-395
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    • 2013
  • Geostationary Ocean Color Imager(GOCI), the World's first spaceborne ocean color observation satellite operated in geostationary orbit, was successfully launched on May 2010. The main missions of GOCI is the coastal environment monitoring of GOCI in order to meet the necessity of long-term climate change monitoring and research. The GOCI have higher spatial resolution than MODIS, $500m{\times}500m$, and 8 spectral ocean color channels. GOCI have a capability for observation on the coastal environment change, GOCI perform the observation with 8 times a day. In this paper, we presented the more improved results for observation on the coastal environment change than MODIS ocean color sensor and detected the spatial difference of CDOM for monitoring coastal environment change.

Ocean Disaster Detection System(OD2S) using Geostationary Ocean Color Imager(GOCI) (천리안해양관측위성을 활용한 해양 재난 검출 시스템)

  • Yang, Hyun;Ryu, Jeung-Mi;Han, Hee-Jeong;Ryu, Joo-Hyung;Park, Young-Je
    • Journal of Information Technology Services
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    • v.11 no.sup
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    • pp.177-189
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    • 2012
  • We developed the ocean disaster detection system(OD2S) which copes with the occurrences of ocean disasters (e. g. the red and green tide, the oil spill, the typhoon, and the sea ice) by converging and integrating the ocean color remote sensing using the satellite and the information technology exploiting the mass data processing and the pattern recognitions. This system which is based on the cosine similarity detects the ocean disasters in real time. The existing ocean color sensors which are operated in the polar orbit platforms cannot conduct the real time observation of ocean environments because they support the low temporal resolutions of one observation a day. However, geostationary ocean color imager(GOCI), the first geostationary ocean color sensor in the world, produces the ocean color images(e. g. the chlorophyll, the colored dissolved organic matter(CDOM), and the total suspended solid(TSS)), with high temporal resolutions of hourly intervals up to eight observations a day. The evaluation demonstrated that the OD2S can detect the excessive concentration of chlorophyll, CDOM, and TSS. Based on these results, it is expected that OD2S detects the ocean disasters in real time.

Ocean Color Monitoring of Coastal Environments in the Asian Waters

  • Tang, Danling;Kawamura, Hiroshi
    • Journal of the korean society of oceanography
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    • v.37 no.3
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    • pp.154-159
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    • 2002
  • Satellite remote sensing technology for ocean observation has evolved considerably in these last twenty years. Ocean color is one of the most important parameters of ocean satellite measurements. This paper describes a remote sensing of ocean color data project - Asian I-Lac Project; it also introduces several case studies using satellite images in the Asian waters. The Asian waters are related to about 30 Asian countries, representing about 60% of the world population. The project aims at generating long-term time series images (planned for 10 years from 1996 to 2006) by combining several ocean color satellite data, i.e., ADEOS-I OCTS and SeaWiFS, and some other sensors. Some typical parameters that could be measured include Chlorophyll- a (Chl-a), Colored Dissolved Organic Matter (CDOM), and Suspended Material (SSM). Reprocessed OCTS images display spatial variation of Chl-a, CDOM, and SSM in the Asian waters; a short term variability of phytoplankton blooms was observed in the Gulf of Oman in November 1996 by analyzing OCTS and NOAA sea surface temperature (SST); Chl-a concentrations derived from OCTS and SeaWiFS have also been evaluated in coastal areas of the Taiwan Strait, the Gulf of Thailand, the northeast Arabian Sea, and the Japan Sea. The data system provides scientists with capability of testing or developing ocean color algorithms, and transferring images for their research. We have also analyzed availability of OCTS images. The results demonstrate the potential of long-term time series of satellite ocean color data for research in marine biology, and ocean studies. The case studies show multiple applications of satellite images on monitoring of coastal environments in the Asian Waters.

Detection of low salinity water in the northern East China Sea in summer using ocean color remote sensing

  • Suh, Young-Sang;Jang, Lee-Hyun;Lee, Na-Kyung;Kim, Bok-Kee
    • Proceedings of the KSRS Conference
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    • pp.649-654
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    • 2002
  • In summer season of 1998, a huge flood occurred around the Yangtze River in the eastern China. The low salinity water less than 28 psu from the river was detected around the southeastern part of the Jeju Island which is located in the southern part of the Korean peninsula. We studied how to detect low salinity water from the Yangtze River, which gives terrible damages to the Korean fisheries. We got the relationships between low surface salinity, turbid water from the Yangtze River and digital ocean color using remote sensing of SeaWiFS satellite in the northern East China Sea in summer seanson of 1998, 1999, 2000 and 2001. The charts of salinity in the northern East China Sea were made by the regenerating of the satellite ocean color data with the formula from the relationships between low salinity, in situ turbid water (transparency) and satellite ocean color.

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Detection of low Salinity Water in the Northern East China Sea During Summer using Ocean Color Remote Sensing

  • Suh, Young-Sang;Jang, Lee-Hyun;Lee, Na-Kyung
    • Korean Journal of Remote Sensing
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    • v.20 no.3
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    • pp.153-162
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    • 2004
  • In the summer of 1998-2001, a huge flood occurred in the Yangtze River in the eastern China. Low salinity water less than 28 psu from the river was detected around the southwestern part of the Jeju Island, which is located in the southern part of the Korean Peninsula. We studied how to detect low salinity water from the Yangtze River, that cause a terrible damage to the Korean fisheries. We established a relationships between low salinity at surface, turbid water from the Yangtze River and digital ocean color remotely sensed data of SeaWiFS sensor in the northern East China Sea, in the summer of 1998, 1999, 2000 and 2001. The salinity charts of the northern East China Sea were created by regeneration of the satellite ocean color data using the empirical formula from the relationships between in situ low salinity, in situ measured turbid water with transparency and SeaWiFS ocean color data (normalized water leaving radiance of 490 nm/555 nm).

Estimation of Sea Surface Current Vector based on Satellite Ocean Color Image around the Korean Marginal Sea

  • Kim, Eung;Ro, Young-Jae;Ahn, Yu-Hwan
    • Proceedings of the KSRS Conference
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    • v.2
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    • pp.816-819
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    • 2006
  • One of the most difficult parameters to measure in the sea is current speed and direction. Recently, efforts are being made to estimate the ocean current vectors by utilizing sequential satellite imageries. In this study, we attempted to estimated sea surface current vector (sscv) by using satellite ocean color imageries of SeaWifs around the Korean Peninsula. This ocean color image data has 1-day sampling interval and spatial resolution of 1x1 km. Maximum cross-correlation method is employed which is aimed to detect similar patterns between sequential images. The estimated current vectors are compared to the surface geostrophic current vectors obtained from altimeter of sea level height data. In utilizing the color imagery data, some limitations and drawbacks exist so that in warm water region where phytoplankton concentration is relatively lower than in cold water region, estimation of sscv is poor and unreliable. On the other hand, two current vector fields agree reasonably well in the Korean South Sea region where high concentration of chlorophyll-a and weak tide is observed. In the future, with ocean color images of shorter sampling interval by COMS satellite, the algorithm and methodology developed in the study would be useful in providing the information for the ocean current around Korean Peninsula.

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Comparison of CZCS and SeaWiFS Pigments for Merging the Higher Level Ocean Color Data

  • Jeong, Jong-Chul;Yoo, Shin-Jae
    • Korean Journal of Remote Sensing
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    • v.18 no.5
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    • pp.299-303
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    • 2002
  • Many ocean color sensors are being operated at present and will be continued to operatein the coming years. However, these ocean color sensors have different spectral bands locations and higher level product algorithms. Thus the continuity of ocean color data from the satellite with different missions will be important for monitoring of oceanographic variation with long term research. In this study, CZCS band and algorithm are compared with OCTS and SeaWiFS algorithm for estimating chlorophyll. Missing bands of OCTS and CZCS for chlorophyll algorithm are estimated by linear-interpolation using SeaWiFS data. We were able to evaluate the effectiveness of the correction methods using linear interpolation method. Surprisingly, linear interpolation gave a better result than those of other bands.