• Atmosphere
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• v.21 no.3
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• pp.311-317
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• 2011

Aerosol optical properties from the radiation measurements by SKYNET PREDE skyradiometers, POM-01 and POM-02 were compared during the inter-calibration campaign at Seoul in February 2009. The monochromatic solar flux at the top of the atmosphere ($F_0$) gave a relative standard deviation (RSD) of 9-10% for both instruments. This comparatively high value of RSD was probably because $F_0$ was determined at short time intervals, in the morning and afternoon, using the measurements made in the polluted environment of Seoul. Although POM-02 was more effective in tracking the solar radiation, aerosol optical depths (AODs) from the two instruments were very similar after the cloud screening procedure. The squared correlation coefficients ($R^2$) of single scattering albedo (SSA) and real and imaginary refractive indices between the two instruments was around 0.5 but increased to 0.7-0.8 when only using AOD greater than 0.4. Nevertheless, mean values of the Angstrom exponent, SSA, and the imaginary refractive index of POM-02 were higher than those of POM-01.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.61.3-62
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• 2016

An asteroid family is a group of asteroidal objects in the proper orbital element space (a, e, and i), considered to have been produced by a disruption of a large parent body through a catastrophic collision. Family members usually have similar surface properties such as spectral taxonomy types, colors, and visible geometric albedo with a same dynamical age. Therefore an asteroid family could be called as a natural Solar System laboratory and is also regarded as a powerful tool to investigate space weathering and non-gravitational phenomena such as the Yarkovsky/YORP effects. We carry out time series photometric observations for a number of asteroid families to obtain their physical properties, including sizes, shapes, rotational periods, spin axes, colors, and H-G parameters based on nearly round-the-clock observations, using several 0.5-2 meter class telescopes in the Northern hemisphere, including BOAO 1.8 m, LOAO 1.0 m, SOAO 0.6 m facilities in KASI, McDonald Observatory 2.1 m instrument, NARIT 2.4 m and TUG 1.0 m telescopes. This study is expected to find, for the first time, some important clues on the collisional history in our Solar System and the mechanisms where the family members are being transported from the resonance regions in the Main-belt to the near Earth space.

• Korean Journal of Remote Sensing
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• v.9 no.1
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• pp.1-19
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• 1993

A simple bispectral threshold technique which reflects the temporal and spatial characteristics of the analysis area has been developed to classify the cloud type and estimate the cloud cover from GMS/S-VISSR(Stretched Visible and Infrared Spin Scan Radiometer) imagery. In this research, we divided the analysis area into land and sea to consider their different optical properties and used the same time observation data to exclude the solar zenith angle effects included in the raw data. Statistical clear sky radiance(CSRs) was constructed using maximum brightness temperature and minimum albedo from the S-VISSR imagery data during consecutive two weeks. The CSR used in the cloud anaysis was updated on the daily basis by using CSRs, the standard deviation of CSRs and present raw data to reflect the daily variation of temperature. Thresholds were applied to classify the cloud type and estimate the cloud cover from GMS/S-VISST imagery. We used a different thresholds according to the earth surface type and the thresholds were enough to resolve the spatial variation of brightness temperature and the noise in raw data. To classify the ambiguous pixels, we used the time series of 2-D histogram and local standard deviation, and the results showed a little improvements. Visual comparisons among the present research results, KMA's manual analysis and observed sea level charts showed a good agreement in quality.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.275-292
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• 2014

The objective of this study is to determine Tasseled Cap Transformation (TCT) coefficients for the Geostationary Ocean Color Imager (GOCI). TCT is traditional method of analyzing the characteristics of the land area from multi spectral sensor data. TCT coefficients for a new sensor must be estimated individually because of different sensor characteristics of each sensor. Although the primary objective of the GOCI is for ocean color study, one half of the scene covers land area with typical land observing channels in Visible-Near InfraRed (VNIR). The GOCI has a unique capability to acquire eight scenes per day. This advantage of high temporal resolution can be utilized for detecting daily variation of land surface. The GOCI TCT offers a great potential for application in near-real time analysis and interpretation of land cover characteristics. TCT generally represents information of "Brightness", "Greenness" and "Wetness". However, in the case of the GOCI is not able to provide "Wetness" due to lack of ShortWave InfraRed (SWIR) band. To maximize the utilization of high temporal resolution, "Wetness" should be provided. In order to obtain "Wetness", the linear regression method was used to align the GOCI Principal Component Analysis (PCA) space with the MODIS TCT space. The GOCI TCT coefficients obtained by this method have different values according to observation time due to the characteristics of geostationary earth orbit. To examine these differences, the correlation between the GOCI TCT and the MODIS TCT were compared. As a result, while the GOCI TCT coefficients of "Brightness" and "Greenness" were selected at 4h, the GOCI TCT coefficient of "Wetness" was selected at 2h. To assess the adequacy of the resulting GOCI TCT coefficients, the GOCI TCT data were compared to the MODIS TCT image and several land parameters. The land cover classification of the GOCI TCT image was expressed more precisely than the MODIS TCT image. The distribution of land cover classification of the GOCI TCT space showed meaningful results. Also, "Brightness", "Greenness", and "Wetness" of the GOCI TCT data showed a relatively high correlation with Albedo ($R^2$ = 0.75), Normalized Difference Vegetation Index (NDVI) ($R^2$ = 0.97), and Normalized Difference Moisture Index (NDMI) ($R^2$ = 0.77), respectively. These results indicate the suitability of the GOCI TCT coefficients.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.301-311
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• 2017

Aerosol optical properties as well as vertical location of layer can alter the radiative balance of the Earth by reflecting and absorbing solar radiation. In this study, radiative transfer model (RTM) and satellite-based analysis have been used to quantify the top-of-atmosphere (TOA) radiative effect of aerosol layers in the cloudy atmosphere of the northeast Asia. RTM simulation results show that the atmospheric warming effect of aerosols increases with their height in the presence of underlying cloud layer. This relationship is higher for stronger absorbing aerosols and higher surface albedo condition. Over study region ($20-50^{\circ}N$, $110-140^{\circ}E$) and aerosol event cases, it is possible to qualitatively identify absorbing aerosol effects in the presence of clouds by combining the UV Absorbing Aerosol Index (AAI) derived from Total Ozone Mapping Spectrometer (TOMS), cloud parameters derived from the Moderate Resolution Imaging Spectro-radiometer (MODIS), with TOA Upward Shortwave Flux (USF) from the Clouds and the Earth's Radiant Energy System (CERES). As the regional-mean radiative effect of aerosols, 6 - 26 % lower the USF between aerosols and cloud cover is taken into account. These results demonstrate the importance of estimation for the accurate quantification of aerosol's direct and indirect effect.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.56.3-57
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• 2015

As of early 2015, more than 12,000 Near-Earth Objects (NEOs) have been catalogued by the Minor Planet Center, however their observational properties such as broadband colors and rotational periods are known only for a small fraction of the population. Thanks to time series observations with the KMTNet, orbits, optical sizes (and albedo), spin states and three dimensional shapes of asteroids and comets including NEOs will be systematically investigated and archived for the first time. Based on SDSS and BVRI colors, their approximate surface mineralogy will also be characterized. This so-called DEEP-South (Deep Ecliptic Patrol of the Southern Sky) project will provide a prompt solution to the demand from the scientific community to bridge the gaps in global sky coverage with a coordinated use of the network of ground-based telescopes in the southern hemisphere. We will soon finish implementing dedicated software subsystem consisted of automated observation scheduler and data pipeline for the sake of increased discovery rate, rapid follow-up, timely phase coverage, and efficient data analysis. We will give a brief introduction to test runs conducted at CTIO with the first KMTNet telescope in February and March 2015 and experimental data processing. Preliminary scientific results will also be presented.

• Atmosphere
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• v.22 no.1
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• pp.117-128
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• 2012

Does snow depth initialization have a quantitative impact on sub-seasonal to seasonal prediction skill? To answer this question, a snow depth initialization technique for seasonal forecast system has been implemented and the impact of the initialization on the seasonal forecast of surface air temperature during the wintertime is examined. Since the snow depth observation can not be directly used in the model simulation due to the large systematic bias and much smaller model variability, an anomaly rescaling method to the snow depth initialization is applied. Snow depth in the model is initialized by adding a rescaled snow depth observation anomaly to the model snow depth climatology. A suite of seasonal forecast is performed for each year in recent 12 years (1999-2010) with and without the snow depth initialization to evaluate the performance of the developed technique. The results show that the seasonal forecast of surface air temperature over East Asian region sensitively depends on the initial snow depth anomaly over the region. However, the sensitivity shows large differences for different timing of the initialization and forecast lead time. Especially, the snow depth anomaly initialized in the late winter (Mar. 1) is the most effective in modulating the surface air temperature anomaly after one month. The real predictability gained by the snow depth initialization is also examined from the comparison with observation. The gain of the real predictability is generally small except for the forecasting experiment in the early winter (Nov. 1), which shows some skillful forecasts. Implications of these results and future directions for further development are discussed.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.97-104
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• 2016

We present linear particle depolarization ratio at 440, 675, 870, and 1020 nm retrieved from measurements with an AERONET sun/sky radiometer at Osaka, Japan. The retrieved data were compared with lidar derived linear particle depolarization ratio at 532 nm at the same site. We find good agreement between linear particle depolarization ratios derived with Sun photometer and measured by lidar except for those at 440 nm. The coefficients of determination between lidar derived data and sun/sky radiometer derived data were 0.28, 0.81, 0.88, and 0.89 at 440, 675, 870, and 1020 nm, respectively. We find that the linear particle depolarization ratio derived with sun/sky radiometer varies by the mixing between Asian dust and pollution particles. As the mixing ratio of Asian dust and pollution particles is increased, the linear particle depolarization ratio values are lower than the values of pure Asian dust. It was confirmed by the value of single-scattering albedo and particle size distribution.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.359-375
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• 2017

We developed fog detection algorithm (KNU_FDA) based on the optical and textural properties of fog using satellite (COMS) and ground observation data. The optical properties are dual channel difference (DCD: BT3.7 - BT11) and albedo, and the textural properties are normalized local standard deviation of IR1 and visible channels. Temperature difference between air temperature and BT11 is applied to discriminate the fog from other clouds. Fog detection is performed according to the solar zenith angle of pixel because of the different availability of satellite data: day, night and dawn/dusk. Post-processing is also performed to increase the probability of detection (POD), in particular, at the edge of main fog area. The fog probability is calculated by the weighted sum of threshold tests. The initial threshold and weighting values are optimized using sensitivity tests for the varying threshold values using receiver operating characteristic analysis. The validation results with ground visibility data for the validation cases showed that the performance of KNU_FDA show relatively consistent detection skills but it clearly depends on the fog types and time of day. The average POD and FAR (False Alarm Ratio) for the training and validation cases are ranged from 0.76 to 0.90 and from 0.41 to 0.63, respectively. In general, the performance is relatively good for the fog without high cloud and strong fog but that is significantly decreased for the weak fog. In order to improve the detection skills and stability, optimization of threshold and weighting values are needed through the various training cases.