• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1523-1535
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• 2020

In order to produce crop information using remote sensing, we use classification and growth monitoring based on crop phenology. Therefore, time-series satellite images with a short period are required. However, there are limitations to acquiring time-series satellite data, so it is necessary to use fusion with other earth observation satellites. Before fusion of various satellite image data, it is necessary to overcome the inherent difference in radiometric characteristics of satellites. This study performed Korea Multi-Purpose Satellite-3 (KOMPSAT-3) cross calibration with Landsat-8 as the first step for fusion. Top of Atmosphere (TOA) Reflectance was compared by applying Spectral Band Adjustment Factor (SBAF) to each satellite using hyperspectral sensor band aggregation. As a result of cross calibration, KOMPSAT-3 and Landsat-8 satellites showed a difference in reflectance of less than 4% in Blue, Green, and Red bands, and 6% in NIR bands. KOMPSAT-3, without on-board calibrator, idicate lower radiometric stability compared to ladnsat-8. In the future, efforts are needed to produce normalized reflectance data through BRDF (Bidirectional reflectance distribution function) correction and SBAF application for spectral characteristics of agricultural land.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.363-370
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• 2023

Various earth observation satellites need to provide accurate and high-quality data after launch. To maintain and enhance the quality of satellite data, it is crucial to employ a cross-calibration process that accounts for differences in sensor characteristics, such as the spectral band adjustment factor (SBAF). In this study, we utilized Landsat-8 and Sentinel-2A satellite imagery collected from desert sites in Libya4, Algeria3, and Mauritania2 among pseudo-invariant calibration sites to calculate and apply SBAF, thereby compensating the uncertainties arising from variations in bandwidths. We quantitatively compared the reflectance differences based on the similarity of bandwidths, including Blue, Green, Red, and both the near-infrared (NIR) narrow, and NIR bands of Sentinel-2A. Following the application of SBAF, significant results with reflectance differences of approximately 1% or less were observed for all bands except NIR. In the case of the Sentinel-2A NIR band, it exhibited a significantly larger bandwidth difference compared to the NIR narrow band. However, after applying SBAF, the reflectance difference fell within the acceptable error range (5%) of 1-2%. It indicates that SBAF can be applied even when there is a substantial difference in the bandwidths of the two sensors, particularly in situations where satellite utilization is limited. Therefore, it was determined that SBAF could be applied even when the bandwidth difference between the two sensors is large in a situation where satellite utilization is limited. It is expected to be helpful in research utilizing the quality and continuity of satellite data.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.3
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• pp.255-267
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• 2008

Using the commercial CFD code FLOW-$3D^{(R)}$ which has an implicit General Moving Object (GMO) method, the ship-induced wave has been simulated. In the implicit GMO method of the FLOW-$3D^{(R)}$, a rigid body's motion which is either user-prescribed (prescribed motion) or dynamically coupled to fluid flow (coupled motion) can be computed with six degrees of freedom (DOF). The simulated horizontal wave patterns are agree with the wave patterns represented by depth Froude number. The model has been well-simulated to generate the depth-dependent wave transformation in comparison of uniform depth case to complicated depth case. Additionally, it shows that ship-induced waves have been reasonably generated by two ships passing each other and by a ship moving in a curve. Therefore, it is suggested that the FLOW-$3D^{(R)}$ model calibrated with observed data should provide more accurate prediction for the ship-induced wave in a certain fairway or harbor.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.10
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• pp.1204-1211
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• 2012

Measurement and analysis results of the extracted radiation-patterns from the field-experiments which were conducted to acquire the generic technology for calibration and validation of the satellite SAR system(Synthetic Aperture Radar) are presented in this study. Prototype of active transponder is adjustable within maximum 63.1 dBsm of RCS (Radar Cross Section) and includes the receiving-function with external receiver. To increase an accuracy of these field experiments, we repetitively measured satellite SAR systems of the same operating mode(i.e., COSMO-SkyMed No. 2 & 3, hh-pol., strip-map himage mode, 3 m resolution). Then, the reliability of experimental results was cross-checked through analysis of the RCS of active transponder on SAR image. The property of azimuth radiation patterns of satellite SAR system extracted from them has $0.352^{\circ}$ of HPBW(half-power beamwidth), $0.691^{\circ}$ of FNBW(first-null beamwidth), and 11.17 dB of PSLR(peak to side lobe ratio), respectively.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1285-1300
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• 2022

Turbidity, the measure of the cloudiness of water, is used as an important index for water quality management. The turbidity can vary greatly in small river systems, which affects water quality in national rivers. Therefore, the generation of high-resolution spatial information on turbidity is very important. In this study, a turbidity retrieval model using the Korea Multi-Purpose Satellite-3 and -3A (KOMPSAT-3/3A) images was developed for high-resolution turbidity mapping of Han River system based on eXtreme Gradient Boosting (XGBoost) algorithm. To this end, the top of atmosphere (TOA) spectral reflectance was calculated from a total of 24 KOMPSAT-3/3A images and 150 Landsat-8 images. The Landsat-8 TOA spectral reflectance was cross-calibrated to the KOMPSAT-3/3A bands. The turbidity measured by the National Water Quality Monitoring Network was used as a reference dataset, and as input variables, the TOA spectral reflectance at the locations of in situ turbidity measurement, the spectral indices (the normalized difference vegetation index, normalized difference water index, and normalized difference turbidity index), and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived atmospheric products(the atmospheric optical thickness, water vapor, and ozone) were used. Furthermore, by analyzing the KOMPSAT-3/3A TOA spectral reflectance of different turbidities, a new spectral index, new normalized difference turbidity index (nNDTI), was proposed, and it was added as an input variable to the turbidity retrieval model. The XGBoost model showed excellent performance for the retrieval of turbidity with a root mean square error (RMSE) of 2.70 NTU and a normalized RMSE (NRMSE) of 14.70% compared to in situ turbidity, in which the nNDTI proposed in this study was used as the most important variable. The developed turbidity retrieval model was applied to the KOMPSAT-3/3A images to map high-resolution river turbidity, and it was possible to analyze the spatiotemporal variations of turbidity. Through this study, we could confirm that the KOMPSAT-3/3A images are very useful for retrieving high-resolution and accurate spatial information on the river turbidity.