• Title/Summary/Keyword: 엽록소 알고리듬

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The Validation of chlorophyll-a band ratio algorithm of coastal area using SeaWiFS wavelength (SeaWiFS 밴드역에 의한 연안해역의 엽록소 밴드비율 알고리듬 검증)

  • 정종철;유신재
    • Korean Journal of Remote Sensing
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    • v.16 no.1
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    • pp.37-45
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    • 2000
  • Since being launched for ocean observing in 1997, the SeaWiFS sensor has supplied data on ocean chlorophyll distribution and environmental conditions of the atmosphere. Until now, a lot of SeaWiFS data have been archived and utilized for ocean monitoring and land observation. The SeaWiFS sensor has 1km spatial resolution, therefore, it is difficult to obtain data at the coastal zone. Since atmospheric correction algorithms at the coastal area have not been confirmed for chlorophyll algorithm, the ocean color data analysis for coastal zone is not common. In particular, domestic coastal areas have high suspended sediments concentrations and higher absorption influence of colored dissolved organic matter (CDOM), released from in-land, than open-sea. Thus, a useful algorithm for analysis of chlorophyll distribution in domestic coastal areas has not been developed. In this study, empirical algorithms, using data from the ocean color sensor, were developed for monitoring of chlorophyll distribution of coastal areas. In the process of the development of the algorithms, we can find that the red band (665nm) should be used for analyzing of domestic coastal areas near the Yellow Sea.

Comparison of Bio-Optical Properties of the Yellow Sea and the East Sea using SeaWiFS Data (SeaWiFS 자료를 이용한 황해와 동해의 생물광학 특성 비교)

  • Jeong, Jong-Chul
    • Journal of the Korean Association of Geographic Information Studies
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    • v.4 no.2
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    • pp.38-45
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    • 2001
  • Three lines from $36_{\circ}$ N, $124_{\circ}$ E, and $132_{\circ}$ E of the East Sea and the Yellow Sea were chosen to extract spectra of normalized water leaving radiances. Comparative analysis of the OCTS algorithm and SeaWiFS(OC-2) algorithms was presented here. OCTS algorithm have more overestimate than SeaWiFS(OC-2 algorithm) for detecting chlorophyll concentration. Atmospheric correction algorithm that is excluded the effect of SS in the case 2 water need for long term ocean environmental monitoring of the East Sea and the Yellow Sea. And, considered the effect of CDOM and SS, bio-optical algorithm have to be developed in this research.

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Comparison of Estimation Methods of Primary Production of the Yellow Sea for Geostationary Ocean Color Imager (GOCI) Data (정지궤도 해색탑재체(GOCI) 자료 활용을 위한 황해의 일차생산력 추정방법 비교)

  • Park, Ji-Soo;Yoo, Sin-Jae
    • Korean Journal of Remote Sensing
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    • v.26 no.2
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    • pp.221-237
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    • 2010
  • To estimate marine primary production, satellite data are essential for providing much better spatial and temporal resolutions. However, primary production estimation for turbid coastal water such as the Yellow Sea still needs much improvement. Here we evaluate currently available methods of primary production estimation in the Yellow Sea. We focus on comparison of eight combinations from four chlorophyll-a algorithms and two primary production algorithms of the Yellow Sea. Estimated primary production by the eight combinations ranged from 96.5 to $610.2\;gC\;m^{-2}\;yr^{-1}$ in the central region of the Yellow Sea. The new chlorophyll algorithms (presently under development by Korea, China, and Japan scientists) are expected to improve the retrieval of chlorophyll-a in turbid regions compared to the standard algorithm but there are certain unresolved problems. The new algorithm for primary production (which uses adjusted physiological parameters with in-situ data) also needs further improvement.

The Remote Sensing Algorithm for Analysis of Suspended Sediments Distribution in Lake Sihwa and Coastal Area (시화호와 연안해역의 부유사 분포 분석을 위한 원격탐사 알고리듬)

  • Jeong, Jongchul;Yoo, Sinjae;Kim, Jungwook
    • Journal of the Korean Association of Geographic Information Studies
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    • v.2 no.2
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    • pp.59-68
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    • 1999
  • The study for detecting suspended sediment distribution in Lake Sihwa, which has a large surface area and coastal area, using remote sensing technique was carried out with development of satellite data collected since 1970. The research, however, analysis of spatial distribution and quantity, is not common in domestic study and useful algorithms have not been proposed. In this study, a suspended sediment algorithm was composed with in-situ data obtained in study area and remote sensing reflectance obtained in-water optical instrument, which has SeaWiFS wavelength bands. However, when the algorithm was applied to Landsat TM data, including an in-situ data set, and some problems arose. The composition of the algorithm which was structured with band difference and band ratio showed the correlation of $R^2$=0.7649 with concentration of suspended sediments. And, between calculated and observed concentration of suspended sediments there was a correlation of $R^2$=0.6959. However, remote sensing reflectance obtained from Landsat TM is not good for the estimation of concentration of suspended sediments, because of high concentration of chlorophyll and CDOM(colored dissolved organic matter).

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Multi-temporal Remote Sensing Data Analysis using Principal Component Analysis (주성분분석을 이용한 다중시기 원격탐사 자료분석)

  • Jeong, Jong-Chul
    • Journal of the Korean Association of Geographic Information Studies
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    • v.2 no.3
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    • pp.71-80
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    • 1999
  • The aim of the present study is to define and tentatively to interpret the distribution of polluted water released from Lake Sihwa into the Yellow Sea using Landsat TM. Since the region is an extreme Case 2 water, empirical algorithms for detecting concentration of chlorophyll-a and suspended sediments have limitations. This work focuses on the use of multi-temporal Landsat TM data. We applied PCA to detect evolution of spatial feature of polluted water after release from the lake Sihwa. The PCA results were compared with in situ data, such as chlorophyll-a, suspended sediments, Secchi disk depth(SDD), surface temperature, remote sensing reflectance at six channel of SeaWiFS. Also, the in situ remote sensing reflectance obtained by PRR-600(Profiling Reflectance Radiometer) was compared with PCA results of Landsat TM data sets to find good correlation between first Principal Component and Secchi disk depth($R^2$=0.7631), although other variables did not result in such a good correlation. Therefore, Problems in applying PCA techniques to multi-spectral remotely sensed data were also discussed in this paper.

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