• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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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.

• 한국IT서비스학회지
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• 제11권sup호
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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.

• 한국지구과학회지
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• 제40권4호
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• pp.315-328
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• 2019

Since the Coastal Zone Color Scanner (CZCS)/Nimbus-7 was launched in 1978, a variety of studies have been conducted to retrieve ocean color variables from multi-satellites. Several algorithms and formulations have been suggested for estimating ocean color variables based on multi band data at different wavelengths. Chlorophyll-a (chl-a) concentration is one of the most important variables to understand low-level ecosystem in the ocean. To retrieve chl-a concentrations from the satellite observations, an appropriate algorithm depending on water properties is required for each satellite sensor. Most operational empirical algorithms in the global ocean have been developed based on the band-ratio approach, which has the disadvantage of being more adapted to the open ocean than to coastal areas. Alternative algorithms, including the semi-analytical approach, may complement the limits of band-ratio algorithms. As more sensors are planned by various space agencies to monitor the ocean surface, it is expected that continuous monitoring of oceanic ecosystems and environments should be conducted to contribute to the understanding of the oceanic biosphere and the impact of climate change. This study presents an overview of the past and present algorithms for the estimation of chl-a concentration based on multi-satellite data and also presents the prospects for ongoing and upcoming ocean color satellites.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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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.

• 환경영향평가
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• 제22권4호
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.103-106
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• 2003

Retrieval of ocean color information from Multispectral Camera (MSC) on KOMPSAT-2 was investigated to study and characterize small-scale biophysical features in the coastal oceans. Prior to the derivation of such information from space-acquired ocean color imageries, the atmospheric effects largely from path and the air-sea interface should be removed from the total signal recorded at the top of the atmosphere (T$_{TOA}$). In this study, the 'path-extraction' is introduced and demonstrated on the TM and SeaWiFS imageries of highly turbid coastal waters of Korea. The algorithms for retrieval of ocean color information were explored from the remote reflectance (R$_{rs}$) in the visible wavebands of MSC. The determination of coefficient (R$^{2}$) for log-transformed data [ N = 500] was 0.90. Similarly, the R$^{2}$ value for log-transformed data [ N = 500] was found to be 0.93.

• Journal of the korean society of oceanography
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• 제37권3호
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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.

• 대한원격탐사학회지
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• 제16권4호
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• pp.315-325
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• 2000

Observations were made to validate ocean color algorithms in the Ulleung Basin, East Sea in May 2000. Small scale and meso-scale surveys were conducted for the validation of ocean color products (nLw: normalized water-leaving radiance and chlorophyll concentration). There were discrepancies between SeaWiFS and in situ nLw showing the current aerosol models of standard SeaWiFS processing software are less than adequate (Gordon and Wang, 1994). Applying the standard SeaWiFS in-water algorithm resulted in an overestimation of chlorophyll concentration. This is because that CDOM absorption was higher than the estimated chlorophyll absorption. TSS concentration was also high. Therefore, the study region deviated from Case 1 waters. The source of these materials seems to be the entrainment of coastal water by the Tsushima Warm Current. Study of the bio-optical properties in other season is desirable.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.162-165
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• 2007

Chlorophyll concentration is an important factor for physical oceanography as well as biological oceanography. For these necessity many oceanographic researchers have been investigated it for a long time. But investigation using vessel is very inefficient, on the other hands, ocean color remote sensing is a powerful means to get fine-scale (spatial and temporal scale) measurements of chlorophyll concentration. Geostationary Ocean Color Imager (GOCI), for ocean color sensor, loaded on COMS (Communication, Ocean and Meteorological Satellite), will be launched on late 2008 in Korea. According to the necessity of algorithm for GOCI, we developed chlorophyll algorithm for GOCI in this study. There are two types of chlorophyll algorithms. One is an empirical algorithm using band ratio, and the other one is a fluorescence-based algorithms. To develop GOCI chlorophyll algorithm empirically we used bands centered at 412 nm, 443 nm and 555 nm for the DOM absorption, chlorophyll maximum absorption and for absorption of suspended solid material respectively. For the fluorescence-based algorithm we analyzed in-situ remote sensing reflectance $(R_{rs})$ data using baseline method. Fluorescence Line Height $({\Delta}Flu)$ calculated from $R_{rs}$ at bands centered on 681 nm and 688 nm, and ${\Delta}Flu_{(area)}$ are used for development of algorithm. As a result ${\Delta}Flu_{(area)}$ method leads the best fitting for squared correlation coefficient $(R^2)$.

• 한국수산과학회지
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• 제35권3호
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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.