• Aerospace Engineering and Technology
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• v.8 no.2
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• pp.61-67
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• 2009

This paper proposes an on-board orbit data generation algorithm for geostationary satellites. The concept of the proposed algorithm is as follows. From the ground, the position and velocity deviations with respect to the assumed reference orbit are computed for 48 hours of time duration in 30 minutes interval, and the generated data are up-loaded to the satellite to be stored. From the table, three nearest data sets are selected to compute position and velocity deviation for asked epoch time by applying $2^{nd}$ order polynomial interpolation. The computed position and velocity deviation data are added to reference orbit to recover absolute orbit information. Here, the reference orbit is selected to be ideal geostationary orbit with a zero inclination and zero eccentricity. Thanks to very low computational burden, this algorithm allows us to generate orbit data at 1Hz or even higher. In order to support 48 hours autonomy, maximum 3K byte memory is required as orbit data storage. It is estimated that this additional memory requirement is acceptable for geostationary satellite application.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.4
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• pp.309-316
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• 2003

Rapid and accurate data processing is required in many GPS(Global Positioning System) applications including surveying. While one can use four different kinds of GPS satellite orbits, we evaluated the accuracy and precision of each kind of orbits to find the best candidate for rapid and accurate data processing. The four different kinds of orbits we: broadcast orbits from GPS satellites; and ultra-rapid orbits, rapid orbits, and precise orbits provided by international GPS data analysis centers such as IGS. With GIPSY and ultra-rapid orbits, we could get the positioning accuracy of 1.5cm from seven days of GPS data. From this study, we conclude that rapid and accurate data processing is achieved with GIPSY and ultra-rapid orbits.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.1-16
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• 2008

Steam and advection fogs are frequently observed in the Yellow Sea from March to July except for May. This study uses remote sensing (RS) data for the monitoring of sea fog. Meteorological data obtained from the Ieodo Ocean Research Station provided a valuable information for the occurrence of steam and advection fogs as a ground truth. The RS data used in this study were GOES-9, MTSAT-1R images and QuikSCAT wind data. A dual channel difference (DCD) approach using IR and shortwave IR channel of GOES-9 and MTSAT-1R satellites was applied to detect sea fog. The results showed that DCD, texture-related measurement and the weak wind condition are required to separate the sea fog from the low cloud. The QuikSCAT wind data was used to provide the wind speed criteria for a fog event. The laplacian computation was designed for a measurement of the homogeneity. A new combined method, which includes DCD, QuikSCAT wind speed and laplacian computation, was applied to the twelve cases with GOES-9 and MTSAT-1R. The threshold values for DCD, QuikSCAT wind speed and laplacian are -2.0 K, $8m\;s^{-1}$ and 0.1, respectively. The validation results showed that the new combined method slightly improves the detection of sea fog compared to DCD method: improvements of the new combined method are $5{\sim}6%$ increases in the Heidke skill score, 10% decreases in the probability of false detection, and $30{\sim}40%$ increases in the odd ratio.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.3
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• pp.483-488
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• 2019

The propagation prediction model of the earth-space communication link used as an international standard was used to calculate and analyze the total losses on the communication path. The standard definition and scope of ITU-R Rec. were analyzed for each parameter(rain, scintillation, atmospheric gas, clouds) used to calculate the total loss. The total losses were calculated using the standard model for each parameter and the statistical data provided by ITU-R, and the results were analyzed using the validation examples data. The rain losses were calculated using long-term local rainfall attenuation statistics data measured in the region, and compared with the calculation results using a rainfall map in the ITU-R Recommendation. The data of Cheollian satellites for the L-Band and Ka-Band were used to calculate the rainfall attenuation. In the range of 0.01% to 0.1%, it was found to have a greater attenuation slope when using local data than attenuation by the model of ITU-R.

• Economic and Environmental Geology
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• v.49 no.4
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• pp.269-280
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• 2016

I presented three vector crustal magnetic anomaly components and six gradients by using spherical Slepian functions over the cap area of $20^{\circ}$ of radius centered on the South Pole. The Swarm mission, launched by European Space Agency(ESA) in November of 2013, was planned to put three satellites into the low-Earth orbits, two in parallel in East-West direction and one in cross-over of the higher altitude. This orbit configuration will make the gradient measurements possible in North-South direction, vertical direction, as well as E-W direction. The gravity satellites, such as GRACE and GOCE, have already implemented their gradient measurements for recovering the accurate gravity of the Earth and its temporal variation due to mass changes on the subsurface. However, the magnetic gradients have little been applied since Swarm launched. A localized magnetic modeling method is useful in taking an account for a region where data availability was limited or of interest was special. In particular, computation to get the localized solutions is much more efficient and it has an advantage of presenting high frequency anomaly features with numbers of solutions fewer than the global ones. Besides, these localized basis functions that were done by a linear transformation of the spherical harmonic functions, are orthogonal so that they can be used for power spectrum analysis by transforming the global spherical harmonic coefficients. I anticipate in scientific and technical progress in the localized modeling with the gradient measurements from Swarm and here will do discussion on the results of the localized solution to represent the three vector and six gradient anomalies over the Antarctic area from the synthetic data derived from a global solution of the spherical harmonics for the crustal magnetic anomalies of Swarm measurements.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1231-1244
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• 2022

The frequency of exposure of field crops to stress situations is increasing due to abnormal weather conditions. In South Korea, large-scale diseases in representative paddy rice cultivation area were happened. There are limits to field investigation on the crop damage due to large-scale. Satellite-based remote sensing techniques are useful for monitoring crops in cities and counties, but the sensitivity of vegetation index measured from satellite under abnormal growth of crop should be evaluated. The goal is to evaluate satellite-based normalized difference vegetation index (NDVI) retrieved from different spatial scales using drone imagery. In this study, Sentinel-2 and Landsat-8 satellites were used and they have spatial resolution of 10 and 30 m. Drone-based NDVI, which was resampled to the scale of satellite data, had correlation of 0.867-0.940 with Sentinel-2 NDVI and of 0.813-0.934 with Landsat-8 NDVI. When the effects of bias were minimized, Sentinel-2 NDVI had a normalized root mean square error of 0.2 to 2.8% less than that of the drone NDVI compared to Landsat-8 NDVI. In addition, Sentinel-2 NDVI had the constant error values regardless of diseases damage. On the other hand, Landsat-8 NDVI had different error values depending on degree of diseases. Considering the large error at the boundary of agricultural field, high spatial resolution data is more effective in monitoring crops.

• Aerospace Engineering and Technology
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• v.13 no.1
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• pp.27-36
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• 2014

Generally, satellites continuously monitor that its major functions are working properly and their hardware are in a good status using several SOH data. In case a fault that is not recognized as a temporal problem or a failure that can be considered to propagate its damage to the other parts are detected, fault management logic is performed automatically without any contact of ground station. In this paper, attitude error detection using sun sensors on a rotating solar array is proposed. Attitude error can be detected by comparing the offset angle between the actual data computed from the sun sensor and the data predicted from the orbit and ephemeris information for the two types of solar array operation method. During the eclipse, the output of attitude error detection method becomes zero because the sun sensor output cannot be provided. Finally, the proposed method is analyzed through the data processing using on-orbit data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1236-1238
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• 2003

The Earth's total magnetic field was extracted from on board TAM (Three Axis Magnetometer) observations of KOMPSAT-1 satellite between June 19th and 21st, 2000. In the pre-processing, the TAM's telemetry data were transformed from ECI (Earth Centered Inertial frame) to ECEF (Earth Centered Earth Fixed frame) and then to spherical coordination, and self-induced magnetic field by satellite bus itself were removed by using an on-orbit magnetometer data correction method. The 2-D wavenumber correlation filtering and quadrant-swapping method were applied to the pre-processed data in order to eliminate dynamic components and track-line noise, respectively. Then, the spherical harmonic coefficients are calculated from KOMPSAT-1 data. To test the validity of the TAM's geomagnetic field, Danish/NASA/French ${\phi}$rsted satellite's magnetic model and IGRF2000 model were used for statistical comparison. The correlation coefficient between ${\phi}$rsted and TAM is 0.97 and IGRF and TAM is 0.96. It was found that the data from on board magnetometer observations for attitude control of Earth-observing satellites can be used to determinate the Earth's total magnetic field and that they can be efficiently used to upgrade the global geomagnetic field coefficients, such as IGRF by providing new information at various altitudes with better temporal and spatial coverage.

• Journal of the Korean Society of Hazard Mitigation
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• v.4 no.2 s.13
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• pp.61-69
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• 2004

Drought is one of the mai or environmental disasters. Weather data, particularity rainfall, are currently the primary source of information widely used for drought monitoring. However, weather data are often from a very sparse meteorological network. Therefore, data obtained from the Advanced Very High Resolution Radiometer(AVHRR) sensor boarded on the NOAA polar-orbiting satellites have been studied as a tool for drought monitoring. The normalized difference vegetation index(NDVI) and vegetation condition index(VCI) were used in this study. Also, a simple method to detect drought Is Proposed based on climatic water balance using NOAA/AVHRR data. The results clearly show that temporal and spatial characteristics of drought in Korea can be detected and mapped by the moisture index.

• Journal of Korea Water Resources Association
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• v.36 no.5
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• pp.839-849
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• 2003

Drought is one of the major environmental disasters. Weather data, particularity rainfall, are currently the primary source of information widely used for drought monitoring. However, weather data are often from a very sparse meteorological network, incomplete and/or not always available in good time to enable relatively accurate and timely drought detection. Data from remote sensing platforms can be used to complements weather data in drought. Therefore, data obtained from the Advanced Very High Resolution Radiometer(AVHRR) sensor on board the NOAA polar-orbiting satellites have been studied as a tool for drought monitoring. The normalized difference vegetation index(NDVI)-based vegetation condition index(VCI) were used in this study These indices showed their excellent ability to detect vegetation stress due to drought. The results clearly show that temporal and spatial characteristics of drought in Korea can be detected and mapped by the VCI index.