• Title/Summary/Keyword: SeaWiFS

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On the Yellow Sand Detection using KOMPSAT OSMI Data (KOMPSAT OSMI 자료를 이용한 황사탐지)

  • 김영섭;박경원;서애숙
    • Korean Journal of Remote Sensing
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    • v.18 no.4
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    • pp.201-207
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    • 2002
  • Radiative transfer model was used to detect the yellow sand using KOMPSAT-1/0SMI data. With OSMI and SeaWiFS data, spectrum analysis for spatial and channel were carried out to investigate the characteristics of sensor for the detection of yellow sand. It was compared and analyzed the optical depth of OSMI and SeaWiFS data. Spectral characteristics of x-axis is similar in 765 and 865nm according to spectral analysis for OSMI and SeaWiFS data. It is considered that band 7 and 8(765 and 865nm) of OSMI is suitable for detecting the yellow sand. Compared the yellow sand images by OSMI and MODIS, the data of OSMI are applicable to monitor the yellow sand phenomena. The optical depth of yellow sand event was about 0.8 with 1.0 maximum.

Calibration and Validation of Ocean Color Satellite Imagery (해양수색 위성자료의 검.보정)

  • ;B. G. Mitchell
    • Journal of Environmental Science International
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    • v.10 no.6
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    • pp.431-436
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    • 2001
  • Variations in phytoplankton concentrations result from changes of the ocean color caused by phytoplankton pigments. Thus, ocean spectral reflectance for low chlorophyll waters are blue and high chlorophyll waters tend to have green reflectance. In the Korea region, clear waters and the open sea in the Kuroshio regions of the East China Sea have low chlorophyll. As one moves even closer In the northwestern part of the East China Sea, the situation becomes much more optically complicated, with contributions not only from higher concentration of phytoplankton, but also from sediments and dissolved materials from terrestrial and sea bottom sources. The color often approaches yellow-brown in the turbidity waters (Case Ⅱ waters). To verify satellite ocean color retrievals, or to develop new algorithms for complex case Ⅱ regions requires ship-based studies. In this study, we compared the chlorophyll retrievals from NASA's SeaWiFS sensor with chlorophyll values determined with standard fluorometric methods during two cruises on Korean NFRDI ships. For the SeaWiFS data, we used the standard NASA SeaWiFS algorithm to estimate the chlorophyll_a distribution around the Korean waters using Orbview/ SeaWiFS satellite data acquired by our HPRT station at NFRDl. We studied In find out the relationship between the measured chlorophyll_a from the ship and the estimated chlorophyll_a from the SeaWiFs satellite data around the northern part of the East China Sea, in February, and May, 2000. The relationship between the measured chlorophyll_a and the SeaWiFS chlorophyll_a shows following the equations (1) In the northern part of the East China Sea. Chlorophyll_a =0.121Ln(X) + 0.504, R²= 0.73 (1) We also determined total suspended sediment mass (55) and compared it with SeaWiFS spectral band ratio. A suspended solid algorithm was composed of in-.situ data and the ratio (L/sub WN/(490 ㎚)L/sub WN/(555 ㎚) of the SeaWiFS wavelength bands. The relationship between the measured suspended solid and the SeaWiFS band ratio shows following the equation (2) in the northern part of the East China Sea. SS = -0.703 Ln(X) + 2.237, R²= 0.62 (2) In the near future, NFRDI will develop algorithms for quantifying the ocean color properties around the Korean waters, with the data from regular ocean observations using its own research vessels and from three satellites, KOMPSAT/OSMl, Terra/MODIS and Orbview/SeaWiFS.

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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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Characteristics of Speckle Errors of SeaWiFS Chlorophyll-α Concentration in the East Sea (동해 SeaWiFS 클로로필-α 농도의 스펙클 오차 특성)

  • Chae, Hwa-Jeong;Park, Kyung-Ae
    • Journal of the Korean earth science society
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    • v.30 no.2
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    • pp.234-246
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    • 2009
  • Characteristics of speckle errors of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) chlorophyll-${\alpha}$ concentration were analyzed, and its causes were investigated by using SeaWiFS data in the East Sea from September 1997 to December 2007. The speckles with anomalously high concentrations were randomly distributed and showed remarkably high bias of greater than $10mg/m^3$, compared with their neighboring pixels. The speckles tended to appear frequently in winter, which might be related to cloud distribution. Ten-year averaged cloudiness of winter was much higher over the southeastern part, with frequent speckles, than the northwestern part of the East Sea. Statistical analysis results showed that the number of the speckles was increased as cloudiness increased. Normalized water-leaving radiance of the speckle pixel was considerably low at the short wavelengths (443, 490, and 510 nm), whereas the radiance at 555 nm band was normal. These low measurements produced extraordinarily high concentration from the chlorophyll-${\alpha}$ estimation formula. This study presented the speckle errors of SeaWiFS chlorophyll-${\alpha}$ concentration in the East Sea and suggested that more reliable chlorophyll-${\alpha}$ data based on appropriate ocean color remote sensing techniques should be used for the oceanic application researches.

Global Distribution of Ocean Color Speckles

  • Chae, Hwa-Jeong;Park, Gyeong-Ae
    • 한국지구과학회:학술대회논문집
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    • pp.135-135
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    • 2010
  • 1997년에 발사된 해색센서 Sea-viewing Wide Field-of-view Sensor (SeaWiFS)의 클로로필-a 자료는 많은 연구자들에게 전 지구 규모나 지역 규모에 대한 해양표층의 식물 플랑크톤 증식을 연구하는데 널리 활용되고 있다. 그런데 NASA로부터 제공되는 동해의 SeaWiFS Level-3 클로로필 자료에는 비정상적으로 높은 농도 값이 스펙클 형태로 분포한다. 본 연구에서는 1998년부터 2007까지 전구의 SeaWiFS 자료를 이용하여 SeaWiFS 클로로필-a 농도가 가지는 스펙클 오차의 분포 특성에 관해 조사하였다. 10년간의 월별 자료로부터 각 화소의 최댓값을 분석한 결과 정상적 농도 범위에서 벗어난 높은 농도 값이 스펙클 형태로 출현하였다. 이런 특성은 북반구에서 전구의 80% 이상 높게 나타났으며, 특히 $40-75^{\circ}N$의 중위도 영역에 집중분포하였다. 또한 스펙클들은 연안 가까이에서 매우 높게 나타난 반면 연안과 멀리 떨어진 외해에서도 $10-80mg/m^3$정도의 높은 값을 가지고 산발적으로 분포하였다. 스펙클들은 해마다 상당한 연 변동을 보였으며, 해역별로 크게 분포하는 시기가 다르게 나타났다. 북태평양에서는 봄철에 크게 나타난 반면 북대서양에서는 봄철과 가을철에 고르게 분포하였다. 2009년의 SeaWiFS 재처리 이후에 스펙클 오차 보정에 대한 처리가 이루어졌으며, 상당량 줄어든 분포를 보였으나 여전히 자료 안에서 매우 높은 농도로 분포하며 자료에 오차를 유발하고 있다. 따라서 본 연구는 전구에서 나타나는 스펙클 오차 분포 특성을 분석함으로 해색자료가 가지는 오차에 관한 문제점을 제기하였으며, 이를 통해 좀 더 신뢰도 있는 자료를 해양 연구에 사용해야함을 제시한다.

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Characteristics of Speckle Errors of SeaWiFS Chlorophyll-$\alpha$ in the East Sea (동해 SeaWiFS 클로로필-$\alpha$ 농도의 스펙클 오차 특성)

  • Chae, Hwa-Jeong;Park, Kyung-Ae
    • Proceedings of the KSRS Conference
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    • pp.337-343
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    • 2009
  • 1997년 9월부터 2007년 12월까지 인공위성 해색센서 SeaWiFS의 클로로필-$\alpha$ 농도가 가지는 오차 특성을 분석하고 그 원인을 조사하였다. 동해의 클로로필-$\alpha$ 월별 분포에는 비정상적으로 높은 농도값이 스펙클(speckle) 형태로 출현하였다. 스펙클들은 시공간적으로 연계성이 없이 산발적으로 분포하였으며 주변 평균에 대해 $10mg/m^3$ 이상의 편차를 보였다. 스펙클들은 주로 겨울철에 나타났으며 구름 분포와 관련이 있었다. 10년간 월별 운량 분석 결과 겨울철 운량은 다른 계절과 달리 남동해상에 집중적으로 분포하였으며, 운량이 클수록 스펙클의 농도가 크게 나타나는 통계적 특성을 보였다. 특히 스펙클이 나타나는 화소의 각 밴드별 정규화된 수출광량을 분석해 본 결과 짧은 파장 영역(443, 490, 510 nm)은 전체적으로 수출광량이 낮게 나타난 반면 550 nm 밴드는 정상 화소와 유사한 분포를 보였다. 짧은 파장 영역의 낮은 수출광량은 555 nm 밴드에 대한 비율로 구해지는 클로로필-$\alpha$ 농도값을 비정상적으로 증폭시켰으며 SeaWiFS 자료에 스펙클을 유발하였다. 본 연구는 동해의 SeaWiFS 클로로필-$\alpha$ 농도자료가 지니는 스펙클 오차에 대한 문제점을 제기하고 오차 특성 분석을 통해 좀 더 신뢰도 있는 자료를 해양 응용 연구에 사용해야 함을 제시한다.

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VARIABILITY OF THE TRENDS OBSERVED FROM SEAWIFS-DERIVED SUB-MICRON AEROSOL FRACTION OVER EAST ASIAN SEAS BASED ON DIFFERENT CLOUD MASKING ALGORITHMS

  • Li, Li-Ping;Fukushima, Hajime;Takeno, Keisuke
    • Proceedings of the KSRS Conference
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    • v.1
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    • pp.316-319
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    • 2006
  • Monthly-mean aerosol parameters derived from the 1998-2004 SeaWiFS observations over East Asian waters are analyzed. SeaWiFS GAC Level 1 data covering the Northeast Asian area are collected and processed by the standard atmospheric correction algorithm released by the SeaWiFS Project to produce daily aerosol optical thickness (AOT) and ${{\AA}}ngstr{\ddot{o}}m$ exponent imageries. Monthly mean AOT and ${{\AA}}ngstr{\ddot{o}}m$ exponent values are extracted from the daily composite images for six study areas chosen from the surrounding waters of Japan. A slight increasing trend of ${{\AA}}ngstr{\ddot{o}}m$ exponent is found and interpreted as about 4-5% increase in submicron fraction of aerosol optical thickness at 550nm. Two cloud screening methods, including the standard cloud masking method of SeaWiFS and the one based on the local variance method, are applied to the SeaWiFS data processing, in an attempt to inspect the influence to the observed statistical uptrend which probably induced by different cloud mask algorithms. The variability comes from the different cloud masking algorithms are discussed.

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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.

Features of Yellow Sand in SeaWiFS Data and Their Implication for Atmospheric Correction

  • Sohn, Byung-Ju;Hwang, Seok-Gyu
    • Proceedings of the KSRS Conference
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    • pp.404-408
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    • 1998
  • Yellow sand event has been studied using SeaWiFS data in order to examine the aerosol optical characteristics in the Yellow Sea and their influences on the atmospheric correction for the ocean color remote sensing. Two SeaWiFS images of April 18 and April 25, 1998, representing Yellow Sand event and clear-sky case respectively, are selected for emphasizing the impact of high aerosol concentration on the ocean color remote sensing. It was shown that NASA's standard atmospheric correction algorithm treats yellow sand area as either too high radiance or cloud area, in which ocean color information is not generated. SeaWiFS aerosol optical thickness is compared with nearby ground-based sun photometer measurements and also is compared with radiative transfer simulation in conjunction with yellow sand model, examining the performance of NASA's atmospheric correction algorithm in case of the heavy dust event.

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The comparison of Sea WiFS and MODIS/Terra in the Japan/East Sea

  • Kim Hyun-cheol;Yoo Sinjae;Oh Im Sang
    • Proceedings of the KSRS Conference
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    • pp.693-695
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    • 2004
  • This study is a comparative analysis of chlorophyll a retrievals in the JES from SeaWiFS and MODIS/Terra. SeaWiFS and MODIS/Terra data over period from 2000 through 2003 were compared. The chlorophyll concentration from the SeaWiFS was generally higher than that from MODIS during the period. There are some possible causes for such discrepancy: differences in the sensor sensitivity, chlorophyll algorithms, and atmospheric correction algorithms. We checked some of these possibilities. We also compared the data from other regions. The deviation between the two data sets was highly correlated with chlorophyll concentrations Atmospheric corrections seem the major cause of the discrepancy.

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