• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.642-644
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• 2003

SAC is a compact electro-optical camera for imaging in visible-NIR spectral ranges. SAC provides highresolution images over the wide geometric and spectral ranges: 10 m ground sample distance (GSD) and 50 km swath width in the spectral ranges of 520 ${\sim}$ 890 nm. SAC is designed to produce high quality images: modulation transfer function (MTF) of more than 15 %; signal-to-noise ratio (SNR) of more than 100. The missions of SAC incorporate various imaging operations: multi-spectral imaging; super swath-width imaging with cameras in parallel; along-track stereo imaging with slanted 2 cameras.

• Journal of the Optical Society of Korea
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• v.4 no.1
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• pp.1-6
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• 2000

Adaptive optics(AO) removes or compensates the distortion caused by a turbulent atmosphere or medium. A wavefront sensormeasures the distortion, on which the correction of AO is based. A new idea of pupil plane wavefront sensing, which consists of a relay lens and a pyramidal-shaped prism, was previously proposed. This paper reviews the idea of pupil wavefrontsensing and presents prism, was previously proposed. The simulation shows that pupilwavefront sensing provides full wavefront sensing when the intensity peak of PSF is located within half of the Airy radius from the apex of the sensor. Adding to this, the sensor is shown to have optimum sensor output with a finite bevel size of the pyramidal prism.

• Journal of the Korean earth science society
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• v.30 no.2
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• pp.223-233
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• 2009

Three-dimensional (3-D) optimally-interpolated sea surface temperature (SST) field was produced by using AQUA/AMSR-E satellite data, and its limitations were described by comparing the temporal average of sea surface temperatures. The 3-D OI (Optimum Interpolation) SST showed a small error of less than $0.05^{\circ}C$ in the central North Pacific, but yielded large errors of greater than $0.4^{\circ}C$ at the coastal area where the satellite microwave data were not available. OI SST composite around pixels with no observation due to heavy rainfall or cloudy pixels had estimation errors of $0.1-0.15^{\circ}C$. Comparison with temporal means showed a tendency that overall OI SSTs were underestimated around heavy cloudy pixels and smoothed out by reducing the magnitude of SST fronts. In the low-latitude areas near the equator, OI SST field produced discontinuity, originated from the window size for the OI procedure. This was mainly caused by differences in the spatial scale of oceanic features. Infernal Rossby deformation radius, as a measure of spatial stale, showed dominant latitudinal variations with O(1) difference in the North Pacific. This study suggests that OI SST methodology should consider latitudinally-varying size of window and the characteristics of spatial scales of oceanic phenomena with substantial dependency on latitude and vertical structure of density.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1555-1565
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• 2018

Detection of subtle ground deformation of volcanoes plays an important role in evaluating the risk and possibility of volcanic eruptions. Ground-fixed observation equipment is difficult to maintain and cost-inefficient. In contrast, satellite remote sensing can regularly monitor at low cost. In this paper, following the study of Chadwick et al. (2006), which applied the interferometric SAR (InSAR) technique to the Sierra Negra volcano, Galapagos. In order to investigate the deformation of the volcano before 2005 eruption, the recent activities of this volcano were analyzed using Sentinel-1, the latest SAR satellite. We obtained the descending mode Sentinel-1A SAR data from January 2017 to January 2018, applied the Persistent Scatter InSAR, and estimated the depth and expansion quantity of magma in recent years through the Mogi model. As a result, it was confirmed that the activity pattern of volcano prior to the eruption in June 2018 was similar to the pattern before the eruption in 2005 and was successful in estimating the depth and expansion amount. The results of this study suggest that satellite SAR can characterize the activity patterns of volcano and can be possibly used for early monitoring of volcanic eruption.

• Journal of the Korean earth science society
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• v.32 no.6
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• pp.586-594
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• 2011

Gravity Recovery and Climate Experiment (GRACE), launched in April, 2002, makes it possible to monitor Earth's mass redistribution with its time-varying gravity observation. GRACE provides monthly gravity solutions as coefficients of spherical harmonics, and thus ones need to convert the gravity spectrum to gravity grids (or mass grids) via the spherical harmonics. GRACE gravity solutions, however, include spatial alias error as well as noise, which requires to suppress in order to enhance signal to noise ratio. In this study, we present the GRACE data processing procedures and introduce some applications of time-varying gravity, which are studies of terrestrial water storage changes, Antarctic and Greenland ice melting, and sea level rise. Satellite missions such as GRACE will continue up to early 2020, and they are expected to be an essential resource to understand the global climate changes.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.57-72
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• 2022

In this paper, we aimed to examine the flowering dates of cherry blossom and the peak dates of maple leaves in South Korea, by the combination of temperature observation data from ASOS (Automated Surface Observing System) and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate Resolution Imaging Spectroradiometer). The more recent years, the faster the flowering dates and the slower the peak dates. This is because of the impacts of climate change with the increase of air temperature in South Korea. By reflecting the climate change, our statistical models could reasonably predict the plant phenology with the CC (Correlation Coefficient) of 0.870 and the MAE (Mean Absolute Error) of 3.3 days for the flowering dates of cherry blossom, and the CC of 0.805 and the MAE of 3.8 for the peak dates of maple leaves. We could suppose a linear relationship between the plant phenology DOY (day of year) and the environmental factors like temperature and NDVI, which should be inspected in more detail. We found that the flowering date of cherry blossom was closely related to the monthly mean temperature of February and March, and the peak date of maple leaves was much associated with the accumulated temperature. Amore sophisticated future work will be required to examine the plant phenology using higher-resolution satellite images and additional meteorological variables like the diurnal temperature range sensitive to plant phenology. Using meteorological grid can help produce the spatially continuous raster maps for plant phenology.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.269-274
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• 2024

High-quality precipitation data are crucial for various industries, including disaster prevention. In South Korea, long-term high-quality data are collected through numerous ground observation stations. However, data between these stations are reprocessed into a grid format using interpolation methods, which may not perfectly match actual precipitation. A prime example of real-time observational grid data globally is the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) from National Aeronautics and Space Administration (NASA), while in South Korea, ground radar data are more commonly used. GPM and ground radar data exhibit distinct differences due to their respective processing methods. This study aims to analyze the characteristics of GPM and Constant Altitude Plan Position Indicator(CAPPI),representative real-time grid data, by comparing them with ground-observed precipitation data. The study period spans from 2021 to 2022, focusing on hourly data from Automated Synoptic Observing System (ASOS) sites in South Korea. The GPM data tend to underestimate precipitation compared to ASOS data, while CAPPI shows errors in estimating low precipitation amounts. Through this comparative analysis, the study anticipates identifying key considerations for utilizing these data in various applied fields, such as recalculating design rainfall, thereby aiding researchers in improving prediction accuracy by using appropriate data.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.1-15
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• 2018

Passive microwave sea surface temperatures (SST) were validated in the Northwest Pacific using a total of 102,294 collocated matchup data between Global Precipitation Measurement (GPM) / GPM Microwave Sensor(GMI) data and oceanic in-situ temperature measurements from March 2014 to December 2016. A root-mean-square (RMS) error and a bias error of the GMI SST measurements were evaluated to $0.93^{\circ}C$ and $0.05^{\circ}C$, respectively. The SST differences between GMI and in-situ measurements were caused by various factors such as wind speed, columnar atmospheric water vapor, land contamination near coastline or islands. The GMI SSTs were found to be higher than the in-situ temperature measurements at low wind speed (<6 m/s) during the daytime. As the wind speed increased at night, SST errors showed positive bias. In addition, other factors, coming from atmospheric water vapor, sensitivity degradation at a low temperature range, and land contamination, also contributed to the errors. One of remarkable characteristics of the errors was their latitudinal dependence with large errors at high latitudes above $30^{\circ}N$. Seasonal characteristics revealed that the errors were most frequently observed in winter with a significant positive deviation. This implies that SST errors tend to be large under conditions of high wind speeds and low SSTs. Understanding of microwave SST errors in this study is anticipated to compensate less temporal capability of Infrared SSTs and to contribute to increase a satellite observation rate with time, especially in SST composite process.

• Atmosphere
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• v.27 no.3
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• pp.359-370
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• 2017

In this study, the oceanic Total Precipitable Water (TPW) retrieval algorithm at 16 km altitude of High Altitude Long Endurance Unmanned Aerial Vehicle (HALE UAV) is described. Empirical equation based on Wentz method (1995) that uses the 18.7 and 22.235 GHz channels is developed using the simulated brightness temperature and SeeBor training dataset. To do radiative simulation, Satellite Data Simulator Unit (SDSU) Radiative Transfer Model (RTM) is used. The data of 60% (523) and 40% (349) in the SeeBor training dataset are used to develop and validate the TPW retrieval algorithm, respectively. The range of coefficients for the TPW retrieval at the altitude of 3~18 km with 3 km interval were 153.69~199.87 (${\alpha}$), 54.330~58.468 (${\beta}$), and 84.519~93.484 (${\gamma}$). The bias and RMSE at each altitude were found to be about $-0.81kg\;m^{-2}$ and $2.17kg\;m^{-2}$, respectively. Correlation coefficients were more than 0.9. Radiosonde observation has been generally operated over land. To validate the accuracy of the oceanic TPW retrieval algorithm, observation data from the Korea Meteorological Administration (KMA) Gisang 1 research vessel about six clear sky cases representing spring, autumn, and summer season is used. Difference between retrieved and observed TPW at 16 km altitude were in the range of $0.53{\sim}1.87kg\;m^{-2}$, which is reasonable for most applications. Difference in TPW between retrieval and observation at each altitude (3~15 km) is also presented. Differences of TPW at altitudes more than 6 km were $0.3{\sim}1.9kg\;m^{-2}$. Retrieved TPW at 3 km altitude was smaller than upper level with a difference of $-0.25{\sim}0.75kg\;m^{-2}$ compared to the observed TPW.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.119-130
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• 1999

At present, many ground stations all over the world are using NORAD orbit element data in order to track and communicate with Earth orbiting satellites. The North American Aerospace Defense Command (NORAD) observes thousands of Earth orbiting objects on daily basis and provides their orbital information via internet. The orbital data provided by NORAD, which is also called two line element (TLE) sets, allows ground stations to predict the time-varying positions of satellites accurately enough to communicate with the satellites. In order to complete the mission of a high resolution remote sensing satellite which requires very high positional determination and control accuracy, however, a mission control and tracking ground station is dedicated for the observation and positional determination of the satellite rather than using NORAD orbital sets. In the case of KITSAT-3, NORAD orbital elements are currently used for image acquisition planning and for the processing of acquired images due to the absence of a dedicated KITSAT-3 tracking ground system. In this paper, we tested and analyzed the accuracy of NORAD orbital elements and the appropriate prediction model to determine how accurately a satellite acquisites an image of the location of interest and how accurately a ground processing system can generate the catalog of the images.