• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.487-499
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• 2009

Atmospheric correction of satellite measurements is a major step to estimate accurate surface reflectance of solar spectrum channels. In this study, Simplified Method for the Atmospheric Correction (SMAC) radiative transfer model used to retrieve surface reflectance from MODIS (MODerate resolution Imaging Spectrometer) top of atmosphere (TOA) reflectance. It is fast and simple atmospheric correction method, so it uses for work site operation in various satellite. This study attempts a test of accuracy of SMAC through a sensitivity test to detected error sources and to improve accuracy of surface reflectance using SMAC. The results of SMAC as compared with MODIS surface reflectance (MOD09) was represented that low accuracy ($R^2\;=\;0.6196$, Root Means Square Error (RMSE) = 0.00031, bias = - 0.0859). Thus sensitivity analysis of input parameters and coefficients was conducted to searching error sources. Among the input parameters, Aerosol Optical Depth (AOD) is the most influence input parameter. In order to modify AOD term in SMAC code, Stepwise multiple regression was performed with testing and remove variable in three stages with independent variables of AOD at 550nm, solar zenith angle, viewing zenith angle. Surface reflectance estimation by using Newly proposed AOD term in the study showed that improve accuracy ($R^2\;=\;0.827$, RMSE = 0.00672, bias = - 0.000762).

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.71-87
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• 2007

In this study, 14 heavy snow events in Yeongdong area which are local phenomena are analyzed using MODIS cloud products provided from NASA/GSFC. The clouds of Yeongdong area at observed at specific time by MODIS are classified into A, B, C Types, based on the characteristic of cloud properties: cloud top temperature, cloud optical thickness, Effective Particle Radius, and Cloud Particle Phase. The analysis of relations between cloud properties and precipitation amount for each cloud type show that there are statistically significant correlations between Cloud Optical Thickness and precipitation amount for both A and B type and also significant correlation is found between Cloud Top Temperature and precipitation amount for A type. However, for C type there is not any significant correlations between cloud properties and precipitation amount. A-type clouds are mainly lower stratus clouds with small-size droplet, which may be formed under the low level cold advection derived synoptically in the East sea. B-type clouds are developed cumuliform clouds, which are closely related to the low pressure center developing over the East sea. On the other hand, C-type clouds are likely multi-layer clouds, which make satellite observation difficult due to covering of high clouds over low level clouds directly related with Yeongdong heavy snow. It is, therefore, concluded that MODIS cloud products may be useful except the multi-layer clouds for understanding the mechanism of heavy snow and estimating the precipitation amount from satellite data in the case of Yeongdong heavy snow.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2001.11a
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• pp.138-140
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• 2001

The Earth, along with its major components - land, atmosphere, and oceans, is at the core of the global environmental system. Changes in any component of the Earth thus strongly affect the global and regional environment. With the advent of the new century, many important decisions on agricultural, industrial, societal and political problems will depend upon the Earth's environment. Monitoring the Earth is thus important to capture any sign from the Earth which might be related to the environmental change. (omitted)

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.99-105
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• 2011

This research presents the correction method to correct the location error of cloud caused by parallax error, and how the method can reduce the position error. The procedure has two steps: first step is to retrieve the corrected satellite zenith angle from the original satellite zenith angle. Second step is to adjust the location of the cloud with azimuth angle and the corrected satellite zenith angle retrieved from the first step. The position error due to parallax error can be as large as 60km in case of 70 degree of satellite zenith angle and 15 km of cloud height. The validation results by MODIS(Moderate-Resolution Imaging Spectrometer) show that the correction method in this study properly adjusts the original cloud position error and can increase the utilization of geostationary satellite data.

• 한국방재학회:학술대회논문집
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• 2011.02a
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• pp.106-106
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• 2011

지표면으로 입사하는 태양 복사 에너지를 정확하게 산출하는 것은 에너지 수지 방법을 이용한 유역 분석의 신뢰도를 높이는데 기여할 수 있다. 태양 복사 에너지는 지형 인자와 대기 인자를 이용하여 산정할 수 있으나 기상관측장비 특성상 지점값 위주의 연구가 이루어지고 있다. 본 연구에서는 이러한 공간적 제약을 완화하기 위해 원격탐사 기법을 이용하여 지표면에 들어오는 태양 복사 에너지를 산출하고자 하였다. 시간, 공간적으로 중규모 해상도를 가지고 있는 Moderate Resolution Imaging Spectroradiometer(MODIS) 위성 관측 이미지를 이용하여 태양 복사 에너지의 시공간 분포를 산정하고 그 결과를 연구 지역인 광릉/해남 KoFlux site의 지상 관측값을 이용하여 검증함으로써 산정 모형의 국내 적용성을 확인하였다. 비교적 적은 수의 인자를 필요로 하는 Allen et al.(2007) 태양 복사 에너지 산정 모형과 36가지의 서로 다른 파장 이미지를 이용하여 산출된 MODIS 대기 자료를 이용하여 결과를 산정함으로써 모형의 간편성 및 효율성을 확인할 수 있었다. 특히 광릉/해남 KoFlux site 관측치와 모형 산정값과의 상관계수가 각각 0.95, 0.96으로 매우 높은 값을 가짐으로써 모형의 높은 신뢰성을 검토하였다. 향후 연구의 결과로써 얻어진 태양 복사 에너지의 시공간 분포특성 분석을 통해 에너지 수지 방법의 정확성을 향상시키고자 한다.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.171-180
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• 2003

The merging of different scales or multi-sensor image data is becoming a widely used procedure of the complementary nature of various data sets. Ideally, the merging method should not distort the characteristics of the high-spatial and high-spectral resolution data used. To present an effective merging method for survey of reclaimed land, this paper compares the results of Intensity Hue Saturation (IHS), Principal Component Analysis (PCA), Color Normalized(CN) and High Pass Filter(HPF) methods used to merge the information contents of the high-resolution (6.6 m) Electro-Optical Camera (EOC) panchromatic image of the first Korea Multi-Purpose Satellite 1 (KOMPSAT-1) and the multi-spectral Moderate Resolution Imaging Spectroradiometer (MODIS) image data. The comparison is made by visual evaluation of three-color combination images of IHS, PCA, CN and HPF results based on spatial and spectral characteristics. The use of a contrasted EOC panchromatic image as a substitute for intensity in merged images with MODIS bands 1, 2 and 3 was found to be particularly effective in this study.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.16 no.5
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• pp.59-69
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• 2013

Global warming also has effects on the phenology. The limitation of phenology study is an acquisition of phenology data. Satellite images analysis can make up limitation of monitering data. This study is to analyze spatial distribution and characteristics of phenology changes using MODIS images. Research data collected images of 16 day intervals of 11 years from year 2001 to 2010. The data analyzed 228 images of 11 years. It can figure out changes of phenology by analyzing enhanced vegetation index of MODIS image. We made a comparison between changes of phenology and flowering of cherry blossoms. As a results, Startup of season spatially was getting late from southern area to north area. Startup of Phenology was foreshortened 13 days during 11 years, and change ratios of cherry blooming was getting more faster from 0.18 dat to 0.22 day per year during that same period.

• Asian Journal of Atmospheric Environment
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• v.3 no.1
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• pp.1-8
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• 2009

Enhancement of aerosol optical thickness (AOT) and surface aerosol mass concentration in Korea for an active forest fire episode in Northeast Asia were estimated by Community Multi-scale Air Quality (CMAQ) model. MODIS/TERRA remote detects of fires in Northeast Asia for May 2003 gave a constraint for estimation of wildfire emissions with an NDVI distribution for recent five years. The simulated wildfire plumes and enhancement of AOT were evaluated and well resolved by comparing multiple satellite observations such as MODIS, TOMS, and others. Scatter plots of observed daily mean aerosol extinction coefficient versus $PM_{10}$ concentration in ground level in Korea showed distinctively different trends based on the ambient relative humidity.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.491-492
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• 2003

The large amount of smoke produced near Lake Baikal was transported to Northeast Asia with high AOT (Aerosol Optical Thickness) as seen in satellite images. Aerosol retrieval using a separation technique was applied to MODIS (Moderate Imaging Spectroradiometer) and SeaWiFS (Sea-viewing Wide Field-of-view Sensor) data observed during 14-22 May 2003. Large AOT, 2.0~5.0 was observed on 20 May 2003 over Korea due to the influence of the long range transport of smoke aerosol plume from the Russian fires, resulting in high PM10 concentration was observed at the surface.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.315-329
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• 2014

In order to analyze the Land Surface Temperature (LST) in metropolitan area including Seoul, Landsat and MODIS land surface temperature, Automatic Weather Station (AWS) temperature, digital elevation model and landuse are used. Analysis method among the Landsat and MODIS LST and AWS temperature is basic statistics using by correlation coefficient, root-mean-square error and linear regression etc. Statistics of Landsat and MODIS LST are a correlation coefficient of 0.32 and Root Mean Squared Error (RMSE) of 4.61 K, respectively. And statistics of Landsat and MODIS LST and AWS temperature have the correlations of 0.83 and 0.96 and the RMSE of 3.28 K and 2.25 K, respectively. Landsat and MODIS LST have relatively high correlation with AWS temperature, and the slope of the linear regression function have 0.45 (Landsat) and 1.02 (MODIS), respectively. Especially, Landsat 5 has lower correlation about 0.5 or less in entire station, but Landsat 8 have a higher correlation of 0.5 or more despite of lower match point than other satellites. Landsat 7 have highly correlation of more than 0.8 in the center of Seoul. Correlation between satellite LSTs and AWS temperature with landuse (urban and rural) have 0.8 or higher. Landsat LST have correlation of 0.84 and RMSE of more than 3.1 K, while MODIS LST have correlation of more than 0.96 and RMSE of 2.6 K. Consequently, the difference between the LSTs by two satellites have due to the difference in the optical observation and detection the radiation generated by the difference in the area resolution.