• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.83-86
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• 2006

이어도 종합해양과학기지의 스펙트로미터로 측정된 해색 스펙트럼 자료들과 SeaWiFS 해색센서로부터 측정된 스펙트럼 자료들을 계절별로 비교 분석하여 해색영상 자료를 처리하는데 사용된 대기보정 알고리즘이 제주도 남쪽 해역과 동중국해 해역에서 어느 정도의 오차를 가지고 있는지 연구하였다. 또한 분석된 자료들을 이용하여 SeaWiFS에서 측정한 스펙트럼 자료들을 보정하고자 하였으며, 이것은 인공위성에서 측정한 클로로필 농도값이 현장관측자료와 비교했을 때 갖는 오차의 범위를 줄여줄 수 있을 것으로 생각된다. 이와 같은 연구결과들은 차후 운용될 COMS 위성의 GOCI 해색센서에 사용될 대기보정 알고리즘과 해양환경 분석 알고리즘을 개발하는데 많은 도움이 될 것으로 생각한다.

• 한국전산유체공학회:학술대회논문집
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• 2010.05a
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• pp.174-180
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• 2010

Conductive and radiative thermal model configurations of an imager of a geostationary satellite are presented. A two-plane method is introduced for three dimensional conductive coupling which is not able to be treated by thin shell plate thermal modeling technique. Especially the two-plane method is applied to massive matters and PIP(Payload Interface Plate) in the imager model. Some massive matters in the thermal model are modified by adequate correction factors or equivalent thickness in order to obtain the numerical results of thermal modeling to be consistent with the analytic model. More detailed nodal breakdown is specially employed to the object which has the rapid temperature gradient expected by a rule of thumb. This detailed thermal model of the imager is supposed to be used for detailed analyses and test predictions, and be correlated with the thermal vacuum test results before final in-flight predictions.

• Aerospace Engineering and Technology
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• v.9 no.2
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• pp.44-50
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• 2010

COMS accommodates multiple payloads; Meteorological Image(MI), Ocean Color Imager(GOCI) and Ka-band communication payloads. In order to improve the quality of MI visible channel, the moon image has been taken into account as backup reference in addition to Albedo monitoring. However, obtaining the moon image by adding special mission schedule is not recommended after IOT, because we may miss chances to obtain meteorological images during the time slots for special imaging. As an alternative solution, an approach extracting moon image from MI FD(Full Disk) image has been proposed when the moon is positioned near to the earth. However, prediction of acquisition time of moon image is somewhat difficult as the moon moves while the MI is scanning type sensor. And the moon can not be seen when it is behind the earth or outside of FD field of view. This paper discusses how effectively the moon can be detected by the MI FD imaging. For that purpose, this paper describes an approach taken to predict the time when the moon image is achievable and then introduces the results obtained from computer simulation.

• Journal of computational fluids engineering
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• v.15 no.2
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• pp.28-34
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• 2010

Conductive and radiative thermal model configurations of an imager of a geostationary satellite are presented. A two-plane method is introduced for three dimensional conductive coupling which is not able to be treated by thin shell plate thermal modeling technique. Especially the two-plane method is applied to massive matters and PIP(Payload Interface Plate) in the imager model. Some massive matters in the thermal model are modified by adequate correction factors or equivalent thickness in order to obtain the numerical results of thermal modeling to be consistent with the analytic model. More detailed nodal breakdown is specially employed to the object which has the rapid temperature gradient expected by a rule of thumb. This detailed thermal model of the imager is supposed to be used for analyses and test predictions, and be correlated with the thermal vacuum test results before final in-flight predictions.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.451-463
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• 2018

The empirical/statistical models to estimate the ground Particulate Matter ($PM_{2.5}$) concentration from Geostationary Ocean Color Imager (GOCI) Aerosol Optical Depth (AOD) product were developed and analyzed for the period of 2015 in Seoul, South Korea. In the model construction of AOD-$PM_{2.5}$, two vertical correction methods using the planetary boundary layer height and the vertical ratio of aerosol, and humidity correction method using the hygroscopic growth factor were applied to respective models. The vertical correction for AOD and humidity correction for $PM_{2.5}$ concentration played an important role in improving accuracy of overall estimation. The multiple linear regression (MLR) models with additional meteorological factors (wind speed, visibility, and air temperature) affecting AOD and $PM_{2.5}$ relationships were constructed for the whole year and each season. As a result, determination coefficients of MLR models were significantly increased, compared to those of empirical models. In this study, we analyzed the seasonal, monthly and diurnal characteristics of AOD-$PM_{2.5}$model. when the MLR model is seasonally constructed, underestimation tendency in high $PM_{2.5}$ cases for the whole year were improved. The monthly and diurnal patterns of observed $PM_{2.5}$ and estimated $PM_{2.5}$ were similar. The results of this study, which estimates surface $PM_{2.5}$ concentration using geostationary satellite AOD, are expected to be applicable to the future GK-2A and GK-2B.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.777-786
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• 2018

This study propose a new method to detect Cochlodinium polykrikoides on satellite images using logistic regression and decision tree. We used spectral profiles(918) extracted from red tide, clear water and turbid water as training data. The 70% of the entire data set was extracted and used for model training, and the classification accuracy of the model was evaluated by using the remaining 30%. As a result of the accuracy evaluation, the logistic regression model showed about 97% classification accuracy, and the decision tree model showed about 86% classification accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1353-1364
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• 2018

This study proposed a method to detect Cochlodinium polykrikoides red tide pixels in satellite images using a logistic regression model of machine learning technique under Imbalanced data. The spectral profiles extracted from red tide, clear water, and turbid water were used as training dataset. 70% of the entire data set was extracted and used for as model training, and the classification accuracy of the model was evaluated using the remaining 30%. At this time, the white noise was added to the spectral profile of the red tide, which has a relatively small number of data compared to the clear water and the turbid water, and over-sampling was performed to solve the unbalanced data problem. As a result of the accuracy evaluation, the proposed algorithm showed about 94% classification accuracy.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.57-81
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• 2019

Satellite images can be integrated into a crop model to strengthen the advantages of each technique for crop monitoring and to compensate for weaknesses of each other, which can be systematically applied for monitoring inaccessible croplands. The objective of this study was to outline the productivity of paddy rice based on simulation of the yield of all paddy fields in North Korea, using a grid crop model combined with optical satellite imagery. The grid GRAMI-rice model was used to simulate paddy rice yields for inaccessible North Korea based on the bidirectional reflectance distribution function-adjusted vegetation indices (VIs) and the solar insolation. VIs and solar insolation for the model simulation were obtained from the Geostationary Ocean Color Imager (GOCI) and the Meteorological Imager (MI) sensors of the Communication Ocean and Meteorological Satellite (COMS). Reanalysis data of air temperature were achieved from the Korea Local Analysis and Prediction System (KLAPS). Study results showed that the yields of paddy rice were reproduced with a statistically significant range of accuracy. The regional characteristics of crops for all of the sites in North Korea were successfully defined into four clusters through a spatial analysis using the K-means clustering approach. The current study has demonstrated the potential effectiveness of characterization of crop productivity based on incorporation of a crop model with satellite images, which is a proven consistent technique for monitoring of crop productivity in inaccessible regions.