• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.1
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• pp.36-46
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• 2013

The Geostationary Ocean Color Imager (GOCI), the first geostationary ocean color observation instrument launched in 2010 on board the Communication, Ocean, and Meteorological Satellite (COMS), has been generating the operational level 1 data. This study describes a methodology for creating the GOCI true color image and data processing software, namely the GOCI RGB maker. The algorithm uses a generic atmospheric correction and reprojection technique to produce the color composite image. Especially, the program is designed for educational purpose in a way that the region of interest and image size can be determined by the user. By distributing software to public, it would maximize the understanding and utilizing the GOCI data. Moreover, images produced from the geostationary observations are expected to be an excellent tool for monitoring environmental changes.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• Proceedings of the KSRS Conference
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• v.2
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• pp.959-962
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• 2006

Public education is undoubtedly a very important aspect for a country to develop space program. People have the rights to understand how the tax they paid is being used. This paper addresses the effectiveness of FORMOSAT-2 on public education in Taiwan. As the first remote sensing satellite of the National Space Organization (NSPO) of Taiwan, FORMOSAT-2 is a small satellite of 746 kg mass for two remote sensing missions: Earth and upward lightning observations. The mission orbit is sun-synchronous of 888 km altitude for exactly 14 revolutions per day. For earth observation, the payload is an advanced high resolution remote sensing instrument (RSI) with ground sampling distance (GSD) 2 m in panchromatic (PAN) band and 8 m in four multi-spectral (MS) bands. For upward lightning observation, the payload is an imager of sprites and upper atmospheric lightning (ISUAL). After more than two years of Earth observation started in June 2004, the effectiveness of FORMOSAT-2 images on public education in Taiwan is very promised. Five domestic universities and one private company in Taiwan have signed contracts respectively with NSPO to take the roles of satellite image investigator and distributor. A private company has signed contract with NSPO to generate and provide URMAP (= your map) in its website for general public applications by using FORMOSAT-2 images. The Newtonkids Book Company used FORMOSAT-2 images to publish a kind of calendar for children education purpose. Besides, a science team in National Cheng Kung University (NCKU) is doing the research work on the 3820 (up to 30 June 2006) transient luminous events (TLEs) observed by FORMOSAT-2.

• Proceedings of the KSRS Conference
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• v.1
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• pp.7-10
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• 2006

Four programs, i.e. TRMM, ADEOS2, ASTER, and ALOS are going on in Japanese Earth Observation programs. TRMM and ASTER are operating well, and TRMM operation will be continued to 2009. ADEOS2 was failed, but AMSR-E on Aqua is operating. ALOS (Advanced Land Observing Satellite) was successfully launched on $24^{th}$ Jan. 2006. ALOS carries three instruments, i.e., PRISM (Panchromatic Remote Sensing Instrument for Stereo Mapping), AVNIR-2 (Advanced Visible and Near Infrared Radiometer), and PALSAR (Phased Array L band Synthetic Aperture Radar). PRISM is a 3 line panchromatic push broom scanner with 2.5m IFOV. AVNIR-2 is a 4 channel multi spectral scanner with 10m IFOV. PALSAR is a full polarimetric active phased array SAR. PALSAR has many observation modes including full polarimetric mode and scan SAR mode. After the unfortunate accident of ADEOS2, JAXA still have plans of Earth observation programs. Next generation satellites will be launched in 2008-2012 timeframe. They are GOSAT (Greenhouse Gas Observation Satellite), GCOM-W and GCOM-C (ADEOS-2 follow on), and GPM (Global Precipitation Mission) core satellite. GOSAT will carry 2 instruments, i.e. a green house gas sensor and a cloud/aerosol imager. The main sensor is a Fourier transform spectrometer (FTS) and covers 0.76 to 15 ${\mu}m$ region with 0.2 to 0.5 $cm^{-1}$ resolution. GPM is a joint project with NASA and will carry two instruments. JAXA will develop DPR (Dual frequency Precipitation Radar) which is a follow on of PR on TRMM. Another project is EarthCare. It is a joint project with ESA and JAXA is going to provide CPR (Cloud Profiling Radar). Discussions on future Earth Observation programs have been started including discussions on ALOS F/O.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.301-306
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• 1998

Korea Aerospace Research Institute (KARI) is developing a Korea Multi-Purpose Satellite I (KOMPSAT-I) which accommodates Electro-Optical Camera (EOC), Ocean Scanning Multi-spectral Imager (OSMI), and Space Physics Sensor (SPS). The satellite has the weight of about 500kg and will be operated on the 10:50 AM sun-synchronized orbit with the altitude of 685 km. The satellite will be launched in 1999 and its lifetime is expected to be over 3 years. The main mission of EOC is the cartography to provide the images from a remote earth view for the production of 1/25000-scale maps of KOREA. EOC collects 510 ~ 730 nm panchromatic imagery with the ground sample distance(GSD) of 6.6 m and the swath width of 17 km by push broom scanning. EOC also can scan $\pm$45 degree across the ground track using body pointing method. The primary mission of OSMI is worldwide ocean color monitoring for the study of biological oceanography. It will generate 6 band ocean color images with 800 km swath width and 1km GSD by whiskbroom scanning. OSMI is designed to provide on-orbit spectral band selectability in the spectral range from 400 nm to 900 nm through ground command. This flexibility in band selection can be used for various applications and will provide research opportunities to support the next generation sensor design. SPS consists of High Energy Particle Detector (HEPD) and ionosphere Measurement Sensor (IMS). HEPD has missions to characterize the low altitude high-energy Particle environment and to study the effects of radiation environment on microelectronics. IMS measures densities and temperature of electrons in the ionosphere and monitors the ionospheric irregularities at the KOMPSAT orbit.

• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.255-266
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• 2012

The enormous disaster (Friday nightmare) occurred at 14:46 (JST) (05:46 UTC) on 11 March 2011, officially named "the 2011 Tohoku Earthquake and Tsunami". To monitor the variations of the marine environment after the earthquake, we used chlorophyll and Rrs(555) of GOCI and MODIS ocean color satellite data during March ~ May 2011. Before the earthquake, chlorophyll and Rrs(555) were relatively low around the Sendai areas. After the earthquake;their concentration and intensity were suddenly increased along the coast and the water column was disturbed by the tsunami wave. The severe distortions influenced by the tsunami occurred at less than 30 m water depth and the variations in offshore were difficult to discern the effect of the tsunami. The disturbance by the tsunami was still remained in the terrestrial environment after one month. However the ocean environment returned to the former condition in almost two month later.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.332-332
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• 2022

적설은 지구 기후시스템과 수문순환 과정에서 중요한 역할을 하고 있으며, 겨울철의 적설은 봄철에 녹으면서 식생과 수자원 제공에 큰 영향을 주는 인자로 알려져 있다. 동아시아가 위치한 북반구는 적설량의 90%가 관찰되고 토지의 약 42%가 긴 시간동안 눈으로 덮여 있어 지표 에너지와 물 균형에 영향을 주고, 특히 수자원 관리를 위한 유출이나 토양수분과 같은 수문 인자에 큰 영향을 미친다. 따라서 적설을 정확하게 예측하는 것은 수자원 관리에 있어 매우 중요한 일이다. 한편, 이러한 수문 순환을 정확히 예측하기 위해 수문 분야에서는 지면모형(Land Surface Model, LSM)을 많이 사용하고 있다. 지면모형은 지표면과 대기 사이의 상호작용을 모의하기 위해 개발되었고, 에너지, 수증기, 이산화탄소 등의 다양한 인자들의 교환에 대하여 해석하며, 토양수분, 유출량 등의 수자원 분야의 주요 인자들을 산출하여 수자원 관리에 적극적으로 활용되고 있다. 이에 본 연구에서는 National Center for Atmospheric Research(NCAR)에서 개발한 Community Land Model(CLM)을 사용하여 2001년부터 2016년까지 25km의 공간해상도로 동아시아 지역의 적설 모의를 평가하였다. CLM의 적설 모의 평가 인자는 Snow depth, Snow water equivalent의 2가지 인자를 대상으로 수행하였고, 모의 성능 평가를 위한 관측 자료로 NASA Aqua와 JAXA GCOM-W1 위성에 탑재된 Advanced Microwave Scanning Radiometer(AMSR) 센서에서 제공하는 위성 관측 자료와 Defense Meteorological Satellite Program(DMSP) 위성의 Special Sensor Microwave/Imager(SSM/I) 센서와 Nimbus-7 위성의 Scanning Multichannel Microwave Radiometer(SMMR) 센서에서 제공하는 위성 관측 자료를 기반으로 지상 기상 관측소 자료와 조합하여 재생성한 European Space Agency Global Snow Monitoring for Climate Research (ESA GlobSnow)의 자료를 사용하였다. 그 결과 CLM의 적설 모의는 과대 추정하는 것을 알 수 있었으며, 본 연구의 결과는 동아시아 적설 모의 개선을 위해 자료 동화를 사용하는 후속 연구의 기초자료로 사용할 수 있다.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.455-464
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• 2014

After launching the Geostationary Ocean Color Imager (GOCI) on June 2010, field campaigns were performed routinely around Korean peninsula to collect in-situ data for calibration and validation. Key measurements in the campaigns are radiometric ones with field radiometers such as Analytical Spectral Devices FieldSpec3 or TriOS RAMSES. The field radiometers must be regularly calibrated. We, in the paper, introduce the optical laboratory built in KOSC and the relative calibration method for in-situ measurement spectroradiometer. The laboratory is equipped with a 20-inch integrating sphere (USS-2000S, LabSphere) in 98% uniformity, a reference spectrometer (MCPD9800, Photal) covering wavelengths from 360 nm to 1100 nm with 1.6 nm spectral resolution, and an optical table ($3600{\times}1500{\times}800mm^3$) having a flatness of ${\pm}0.1mm$. Under constant temperature and humidity maintainance in the room, the reference spectrometer and the in-situ measurement instrument are checked with the same light source in the same distance. From the test of FieldSpec3, we figured out a slight difference among in-situ instruments in blue band range, and also confirmed the sensor spectral performance was changed about 4.41% during 1 year. These results show that the regular calibrations are needed to maintain the field measurement accuracy and thus GOCI data reliability.

• 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.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1553-1563
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• 2023

The Geostationary Ocean Color Imager-II (GOCI-II) is a satellite designed for ocean color observation, covering the Northeast Asian region and the entire disk of the Earth. It commenced operations in 2020, succeeding its predecessor, GOCI, which had been active for the previous decade. In this study, we aimed to enhance the atmospheric correction algorithm, a critical step in producing satellite-based ocean color data, by performing cross-calibration on the GOCI-II near-infrared (NIR) band using the GOCI NIR band. To achieve this, we conducted a cross-calibration study on the top-of-atmosphere (TOA) radiance of the NIR band and derived a vicarious calibration gain for two NIR bands (745 and 865 nm). As a result of applying this gain, the offset of two sensors decreased and the ratio approached 1. It shows that consistency of two sensors was improved. Also, the Rayleigh-corrected reflectance at 745 nm and 865 nm increased by 5.62% and 9.52%, respectively. This alteration had implications for the ratio of Rayleigh-corrected reflectance at these wavelengths, potentially impacting the atmospheric correction results across all spectral bands, particularly during the aerosol reflectance correction process within the atmospheric correction algorithm. Due to the limited overlapping operational period of GOCI and GOCI-II satellites, we only used data from March 2021. Nevertheless, we anticipate further enhancements through ongoing cross-calibration research with other satellites in the future. Additionally, it is essential to apply the vicarious calibration gain derived for the NIR band in this study to perform vicarious calibration for the visible channels and assess its impact on the accuracy of the ocean color products.