• Journal of electromagnetic engineering and science
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• v.12 no.1
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• pp.115-121
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• 2012

This paper presents a scattering model and measurements of backscattering coefficients for soybean fields. The polarimetric radar backscatters of a soybean field were measured using the ground-based X-band polarimetric scatterometer in an angular range from $20^{\circ}$ to $60^{\circ}$. The backscattering coefficients were also obtained using the COSMO-SkyMed (Spotlight mode, HH-polarization) from July to October 2010. The backscattering coefficients of the soybean field were computed using the 1st-order radiative transfer model (RTM) with field-measured input parameters. The soybean layer is composed of the stems, branches, leaves, and soybean pods. The stems, branches, and pods are modeled with lossy dielectric cylinders, the leaves are modeled with lossy dielectric disks. The estimated backscattering coefficients agree quite well with the field-measured radar backscattering coefficients.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1545-1553
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• 2018

Recent eruptive activity at Kīlauea Volcano started on at the end of April in 2018 showed rapid ground deflation between May and June in 2018. On summit area Halema'uma'u lava lake continued to drop at high speed and Kīlauea's summit continued to deflate. GPS receivers and electronic tiltmeters detected the surface deformation greater than 2 meters. We explored the time-series surface deformation at Kīlauea Volcano, focusing on the early stage of eruptive activity, using multi-temporal COSMO-SkyMed SAR imagery. The observed maximum deformation in line-of-sight (LOS) direction was about -1.5 meter, and it indicates approximately -1.9 meter in subsiding direction by applying incidence angle. The results showed that summit began to deflate just after the event started and most of deformation occurred between early May and the end of June. Moreover, we confirmed that summit's deflation rarely happened since July 2018, which means volcanic activity entered a stable stage. The best-fit magma source model based on time-series surface deformation demonstrated that magma chambers were lying at depths between 2-3 km, and it showed a deepening trend in time. Along with the change of source depth, the center of each magma model moved toward the southwest according to the time. These results have a potential risk of including bias coming from single track observation. Therefore, to complement the initial results, we need to generate precise magma source model based on three-dimensional measurements in further research.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.15-28
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• 2019

By applying the method of SAR interferometry to X-band synthetic aperture radar (SAR) image of COSMO-SkyMed, detailed motion patterns of five glaciers in the Parlung Zangbo River basin, Tibetan Plateau, in January 2010 have been derived. The results indicate that flow patterns are generally constrained by the valley geometry and terrain complexity. The maximum of $123.9ma^{-1}$ is observed on glacier No.1 and the minimum of $39.4ma^{-1}$ is found on glacier No.3. The mean values of five glaciers are between 22.9 and $98.2ma^{-1}$. Glaciers No.1, No.2, No.4 and No.5 exhibit high velocities in their upper sections with big slope and low velocities in the lower sections. A moraine lake accelerates the speed of mass exchange leading to a fast flow at the terminal of glacier No.3. These glaciers generally move along the direction of decreased elevation and present a macroscopic illustration of the motion from the northwest to the southeast. The accuracy of DEM and registration conditions of DEM-simulated terrain phases has certain effects on calculations of glacier flow direction and velocity. The error field is relatively fragmented in areas inconsistent with the main flow line of the glaciers, and the shape and uniformity of glacier are directly related to the continuous distribution of flow velocity errors.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.57-67
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• 2021

An earthquake occurred on 17 November 2017 in Pohang, South Korea with a strength of 5.4 Mw. This is the second strongest earthquake recorded by local authorities since the equipment was first installed. In order to improve understanding of earthquakes and surface deformation, many studies have been conducted according to these phenomena. In this research, we will estimate the surface deformation using the Okada model equation. The SAR images of three satellites with different wavelengths (ALOS-2, Cosmo SkyMed and Sentinel-1) were used to produce the interferogram pairs. The interferogram is used as a reference for surface deformation changes by using Okada to determine the source of surface deformation that occurs during an earthquake. The Non-linear optimization (Levemberg-Marquadrt algorithm) and Monte Carlo restart was applied to optimize the fault parameter on modeling process. Based on the modeling results of each satellite data, the fault geometry is ~6 km length, ~2 km width and ~5 km depth. The root mean square error values in the surface deformation model results for Sentinel, CSK and ALOS are 0.37 cm, 0.79 cm and 1.47 cm, respectively. Furthermore, the results of this modeling can be used as learning material in understanding about seismic activity to minimize the impacts that arise in the future.

• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.375-387
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• 2013

Accuracy assessment of tide models in polar ocean has to be performed to accurately analyze tidal response of glaciers by using Double-Differential Interferometric SAR (DDInSAR) technique. In this study, we used 120 DDInSAR images generated from 16 one-day tandem COSMO-SkyMed DInSAR pairs obtained for 2 years and in situ tide height for 11 days measured by a pressure type wave recorder to assess the accuracy of tide models such as TPXO7.1, FES2004, CATS2008a and Ross_Inv in Terra Nova Bay, East Antarctica. Firstly, we compared the double-differential tide height (${\Delta}\dot{T}$) for Campbell Glacier Tongue extracted from the DDInSAR images with that predicted by the tide models. Tide height (T) from in situ measurement was compared to that of the tide models. We also compared 24-hours difference of tide height ($\dot{T}$) from in situ tide height with that from the tide models. The root mean square error (RMSE) of ${\Delta}\dot{T}$, T and $\dot{T}$ decreased after the inverse barometer effect (IBE)-correction of the tide models, from which we confirmed that the IBE of tide models should be corrected requisitely. The RMSE of $\dot{T}$ and ${\Delta}\dot{T}$ were smaller than that of T. This was because $\dot{T}$ is the difference of tide height during temporal baseline of the DInSAR pairs (24 hours), in which the errors from mean sea level of the tide models and in situ tide, and the tide constituents of $S_2$, $K_2$, $K_1$ and $P_1$ used in the tide models were canceled. This confirmed that $\dot{T}$ and ${\Delta}\dot{T}$ predicted by the IBE-corrected tide models can be used in DDInSAR technique. It was difficult to select an optimum tide model for DDInSAR in Terra Nova Bay by using in situ tide height measured in a short period. However, we could confirm that Ross_Inv is the optimum tide model as it showed the smallest RMSE of 4.1 cm by accuracy assessment using the DDInSAR images.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1357-1369
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• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.