• Atmosphere
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• v.25 no.1
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• pp.117-127
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• 2015

The presences of clouds significantly influence the accuracy of ozone retrievals from satellite measurements. This study focuses on the influence of clouds on Ozone Monitoring instrument (OMI) ozone profile retrieval based on an optimal estimation. There are two operational OMI cloud products; OMCLDO2, based on absorption in $O_2-O_2$ at 477 nm, and OMCLDRR, based on filling in Fraunhofer lines by rotational Raman scattering (RRS) at 350 nm. Firstly, we characterize differences between $O_2-O_2$ and RRS effective cloud pressures using MODIS cloud optical thickness (COT), and then compare ozone profile retrievals with different cloud input data. $O_2-O_2$ cloud pressures are significantly smaller than RRS by ~200 hPa in thin clouds, which corresponds to either low COT or cloud fraction (CF). On the other hand, the effect of Optical centroid pressure (OCP) on ozone retrievals becomes significant at high CF. Tropospheric ozone retrievals could differ by up to ${\pm}10$ DU with the different cloud inputs. The layer column ozone below 300 hPa shows the cloud-induced ozone retrieval error of more than 20%. Finally, OMI total ozone is validated with respect to Brewer ground-based total ozone. A better agreement is observed when $O_2-O_2$ cloud data are used in OMI ozone profile retrieval algorithm. This is distinctly observed at low OCP and high CF.

• Korean Journal of Remote Sensing
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• v.32 no.4
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• pp.383-401
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• 2016

In this paper, we described the statistical modeling of crop yields using satellite images, climatic datasets, soil property maps, and fertilizer data for the Midwestern United States during 2001-2012. Satellite images were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and climatic datasets were provided by the Parameter-elevation Regressions on Independent Slopes Model (PRISM) Climate Group. Soil property maps were derived from the Harmonized World Soil Database (HWSD). Our multivariate regression models produced quite good prediction accuracies, with differences of approximately 8-15% from the governmental statistics of corn and soybean yields. The unfavorable conditions of climate and vegetation in 2012 could have resulted in a decrease in yields according to the regression models, but the actual yields were greater than predicted. It can be interpreted that factors other than climate, vegetation, soil, and fertilizer may be involved in the negative biases. Also, we found that soybean yield was more affected by minimum temperature conditions while corn yield was more associated with photosynthetic activities. These two crops can have different potential impacts regarding climate change, and it is important to quantify the degree of the crop sensitivities to climatic variations to help adaptation by humans. Considering the yield decreases during the drought event, we can assume that climatic effect may be stronger than human adaptive capacity. Thus, further studies are demanded particularly by enhancing the data regarding human activities such as tillage, fertilization, irrigation, and comprehensive agricultural technologies.

• Journal of Ecology and Environment
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• v.34 no.2
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• pp.149-156
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• 2011

Phenological variables derived from remote sensing are useful in determining the seasonal cycles of ecosystems in a changing climate. Satellite remote sensing imagery is useful for the spatial continuous monitoring of vegetation phenology across broad regions; however, its applications are substantially constrained by atmospheric disturbances such as clouds, dusts, and aerosols. By way of contrast, a tower-based ground remote sensing approach at the canopy level can provide continuous information on canopy phenology at finer spatial and temporal scales, regardless of atmospheric conditions. In this study, a tower-based ground remote sensing system, called the "Phenological Eyes Network (PEN)", which was installed at the Gwangneung Deciduous KoFlux (GDK) flux tower site in Korea was introduced, and daily phenological progressions at the canopy level were assessed using ratios of red, green, and blue (RGB) spectral reflectances obtained by the PEN system. The PEN system at the GDK site consists of an automatic-capturing digital fisheye camera and a hemi-spherical spectroradiometer, and monitors stand canopy phenology on an hourly basis. RGB data analyses conducted between late March and early December in 2009 revealed that the 2G_RB (i.e., 2G - R - B) index was lower than the G/R (i.e., G divided by R) index during the off-growing season, owing to the effects of surface reflectance, including soil and snow effects. The results of comparisons between the daily PEN-obtained RGB ratios and daily moderate-resolution imaging spectroradiometer (MODIS)-driven vegetation indices demonstrate that ground remote sensing data, including the PEN data, can help to improve cloud-contaminated satellite remote sensing imagery.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.615-617
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• 2003

Carbon monoxide (CO) is one of the important trace gases because its concentration in the troposphere directly influences the concentrations of tropospheric hydroxyl (OH), which controls the lifetimes of tropospheric trace gases. CO traces the transport of global and regional pollutants from industrial activities and large scale biomass burning. The distributions of CO were analyzed using the MOPITT data for East Asia, which were compared with the ozone distributions. In general, seasonal CO variations are characterized by a peak in the spring, which decrease in the summer. The monthly average for CO shows a similar profile to that for O$_3$. This fact clearly indicates that the high concentration of CO in the spring is possibly due to one of two causes: the photochemical production of CO in the troposphere, or the transport of the CO into East Asia. The seasonal cycles for CO and O$_3$ in East Asia are extensively influenced by the seasonal exchanges of different air mass types due to the Asian monsoon. The continental air masses contain high concentrations of O$_3$ and CO, due to the higher continental background concentrations, and sometimes to the contribution from regional pollution. In summer this transport pattern is reversed, where the Pacific marine air masses that prevail over Korea bring low concentrations of CO and O$_3$, which tend to give the apparent summer minimums.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.653-662
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• 2011

Land surface temperature (LST) is a one of the key variables of land surface which can be estimated from geostationary meteorological satellite. In this study, we have developed the three sets of LST retrieval algorithm from MTSAT-2 data through the radiative transfer simulations under various atmospheric profiles (TIGR data), satellite zenith angle, spectral emissivity, and surface lapse rate conditions using MODTRAN 4. The three LST algorithms are daytime, nighttime and total LST algorithms. The weighting method based on the solar zenith angle is developed for the consistent retrieval of LST at the early morning and evening time. The spectral emissivity of two thermal infrared channels is estimated by using vegetation coverage method with land cover map and 15-day normalized vegetation index data. In general, the three LST algorithms well estimated the LST without regard to the satellite zenith angle, water vapour amount, and surface lapse rate. However, the daytime LST algorithm shows a large bias especially for the warm LST (> 300 K) at day time conditions. The night LST algorithm shows a relatively large error for the LST (260 ~ 280K) at the night time conditions. The sensitivity analysis showed that the performance of weighting method is clearly improved regardless of the impacting conditions although the improvements of the weighted LST compared to the total LST are quite different according to the atmospheric and surface lapse rate conditions. The validation results of daytime (nighttime) LST with MODIS LST showed that the correlation coefficients, bias and RMSE are about 0.62~0.93 (0.44~0.83), -1.47~1.53 (-1.80~0.17), and 2.25~4.77 (2.15~4.27), respectively. However, the performance of daytime/nighttime LST algorithms is slightly degraded compared to that of the total LST algorithm.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.137-149
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• 2013

Solar insolation is essential to understand the interaction between the earth and solar system, and it is a significant parameter that is utilized in various research fields including earth science, agriculture, and energy engineering. Although solar insolation is broadly measured in the ground-based observation station, it is difficult to identify the spatial distribution of solar insolation accurately. The remote sensing approach is known to have several benefits because it can provide continuous data sets for large area. In this study, we conducted the validation of solar insolation from COMS in the South Korea by comparing with flux tower observation. The results showed that the correlations between COMS and observation were high in both 30 minutes interval data and daily average data. Thus, we can identify that COMS can provide a reasonable estimate of solar insolation.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.105-114
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• 2013

Desertification of the East Asia area induced by human's indiscriminate activities and natural causes has gradually expanded and demanded scientific research for monitoring and predicting land cover condition. Therefore, this research classified land types which were compared to MODIS land cover and analyzed the extent of barren zone effecting Korea through yellow dust using S10-DAY MVC NDVI from SPOT between 1999 and 2011. This study used unsupervised classification after processing NDVI Correction and Water Mask for eliminating noise values included in the data for enhancement of classification accuracy. The results of analysis are that there are active variations near the borders of desert, especially the Mongolian steppe and the Gobi Desert in central Asia. In addition, the extent of entire desert has been decreased in the middle of the last decade, although desertification is in going on in East Asia.

• Journal of Korean Society on Water Environment
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• v.29 no.2
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• pp.247-258
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• 2013

A grid-based configuration of Land Surface Models (LSMs) coupled with a climate model can be advantageous in impact assessment of climate change for a large scale area. We assessed the applicability of Common Land Model (CoLM) to runoff and land surface temperature (LST) simulations at the domain that encompasses the Nakdong river basin. To establish a high resolution model configuration of a $1km{\times}1km$ grid size, both surface boundary condition and atmospheric inputs from the observed weather data in 2009 were adjusted to the same resolution. The Leaf Area Index (LAI) was collected from MODerate esolution Imaging Spectroradiometer (MODIS) and the downward short wave flux was produced by a nonstationary multi-site weather state model. Compared with the observed runoffs at the stations on Nakdong river, simulated runoffs properly responded to rainfall. The spatial features and the seasonal variations of the domain fairly well were captured in the simulated LSTs as well. The monthly and seasonal trend of LST were described well compared to the observations, however, the monthly averaged simulated LST exceeded the observed up to $2^{\circ}C$ at the 24 stations. From the results of our study, it is shown that high resolution LSMs can be used to evaluate not only quantity but also quality of water resources as it can capture the geographical features of the area of interest and its rainfall-runoff response.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1607-1611
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• 2009

본 연구에서는 현재 가뭄을 관측하는데 주로 이용되는 가뭄지수의 단점 등을 보완하고자 가뭄에 관련되는 식생지수를 연계한 공간해상도 높은 가뭄지수를 제시하였다. 우리나라 지상관측을 통해 산출할 수 있는 PDSI(Palmer Drought Severity Index)와 SPI(Standardized Precipitation Index) 같은 가뭄지수는 기온과 강수량 등의 기후자료만을 이용하여 산정할 수 있다. 두 가뭄지수는 관측하기 어려운 가뭄의 시기와 심도를 설명하고자 여러 연구를 통해 개발한 지수이지만, 두 가뭄지수만을 가지고 우리나라 전역의 가뭄의 공간적인 분포를 설명하기에는 다소 무리가 있다. PDSI의 경우 강수량과 기온과 토양의 수분함유량을 가지고 산출하는데, 전 관측지점을 똑같은 토양수분함유량을 가지고 있다는 가정 하에 계산되고, SPI의 경우 강수량만을 이용하여 산정한다. PDSI의 경우 과거의 가뭄의 정도를 판단하는데 매우유용하다고 알려져 있다. 하지만, 현재의 가뭄정도를 나타내는 데는 문제를 가지고 있고, SPI의 경우는 누적강수량을 가지고 시간단위로 계산한다는 점에서 다양한 가뭄의 정도를 예측할 수 있지만, 입력 자료로 강수량만 들어간다는 점에서 약점을 가진다. 이런 기후지수만을 이용한 가뭄정보 생산이 공간정보를 구현하는데 한계를 가지는 문제점을 개선하고자 가뭄에 직간접적으로 관련이 있는 보다 세밀한 공간정보를 가진 식생, 토지이용, 고도 등의 자료와 기후정보로부터 산정된 가뭄지수간의 관계를 분석하였다. 나아가 기존의 기후지수보다 고해상도를 가진 위성의 정규식생지수(NDVI; Normalized Difference Vegetation Index)와 같은 식생지수를 이용하여 기존보다 더 향상된 해상도의 가뭄지수를 산정하고자 하였다. 우리나라 지상관측소 76개 지점 중에 MODIS(Moderate Resolution Imaging Spectroradiometer) 정규식생지수 자료와의 관계를 분석하고자 자료의 보유기간이 짧은 지점과 섬지점 등을 제외한 57개 지점을 선정하고, 연구기간동안의 강수량과 기온자료를 이용하여 PDSI와 SPI를 산출하였다. PDSI와 SPI자료를 고해상도 가뭄지수 산정의 기본 변수로 사용하기 위하여 역거리가중평균법을 이용한 연구기간동안의 한반도 지역 PDSI와 SPI 가뭄지수 지도를 생산하였다. 각각의 가뭄지수와 식생 상태를 나타내는 NDVI와의 상관특성과 계절 변화에 따른 변화특성을 분석하고, CART(Classification and Regression Trees) 알고리즘을 이용하여, 지상 자료만을 사용한 가뭄지수가 가지는 시공간적 변화 특성 제시에 대한 문제점을 개선한 보다 해상도가 높은 조합가뭄지수를 제시하였다.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.6
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• pp.629-640
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• 2008

This study investigates long-term trends and characteristics of aerosol optical depth ($\tau_a$) and Angstrom exponent (${\AA}$) around Korea in order to understand aerosol effects on the regional climate change. The analysis period is mainly from 1999 to 2006, and the analysis sites are Anmyun and Gosan, the background monitoring sites in Korea, and two other sites of Xianghe in China and Shirahama in Japan. The annual variations of $\tau_a$ at Anmyun and Gosan have slightly systematic increasing and decreasing trends, respectively. $\tau_a$ at Anmyun shows more substantial variation, probably because of it's being closer and vulnerable to anthropogenic influence from China and/or domestic sources than Gosan. Both values at Gosan and Anmyun are approximately 1.5 times greater than those at Shirahama. The monthly variation of $\tau_a$ exhibits the highest values at late Spring and the lowest at late-Summer, which are thought to be associated with the accumulation of fine aerosol formed through the photochemical reaction before the Jangma period and the scavenging effect after the Jangma period, respectively. Meanwhile, the episode-average $\tau_a$ for the Yellow dust period increases 2 times greater than that for the non-Yellow dust period. A significant decrease in ${\AA}$ for the Yellow dust period is attributable to an increase in the loading of especially the coarse particles. Also we found no weekly periodicity of $\tau_a$'s, but distinct weekly cycle of $PM_{10}$ concentrations, such as an increase on weekdays and a decrease on weekends at Anmyun and Gosan. We expect these findings would help to initiate a study on aerosol-cloud interactions through the combination of surface aerosol and satellite remote sensing (MODIS, Calipso and CloudSat) in East Asia.