• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.114-114
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• 2021

본 연구에서는 Sentinel-1 SAR 센서 기반 이미지자료와 딥러닝기법을 이용하여 고해상도 토양수분을 산정하였다. 입력자료는 지표특성(모래함량, 점토함량, 경사도), 인공위성 기반의 강우와 LANDSAT 기반의 이미지자료(NDVI, LST, 공간분포 토양수분)를 사용하였다. 강우자료의 경우 GPM(Global Precipitation Measurement) 일강우 자료를 사용하였으며, 관측일 기준으로 5일전까지의 강우자료와 5일평균강우를 구분하여 사용하였다. LANDSAT 기반의 토양수분 이미지자료와 지점관측 토양수분을 이용하여 검·보정 이후 딥러닝 모형의 입력자료로 사용하였다. 입력자료는 30m × 30m 해상도로 Resample 하여 딥러닝 모형의 학습을 진행하였으며, 학습에 사용된 모형을 이용하여 Sentinel-1 기반의 고해상도(10m × 10m) 토양수분이미지를 산정하였다. 검증지점은 거창군 거창읍, 계룡시 두마면, 장수군 장수읍 및 무주군 무주읍 토양수분 관측지점을 선정하였다. 거창군 거창읍의 산정결과, LANDSAT 기반의 토양수분 이미지와 DNN 기반의 토양수분 이미지가 매우 유사하게 나타났으며, 모의값(DNN 기반 토양수분)이 실측값(LANDSAT 기반의 토양수분)을 잘 반영한 것(R: 0.875 ; RMSE: 0.013)으로 나타났다. 또한 학습모형을 토지피복이 유사한 지역에 적용하여 토양수분을 산정한 결과 검증지점 계룡시(R: 0.897 ; RMSE: 0.014), 장수군(R: 0.770 ; RMSE: 0.024) 및 무주군(R: 0.909 ; RMSE: 0.012)의 모의값이 실측값과 매우 유사한 것으로 나타났다. 이를 바탕으로 Seninel-1 SAR센서 이미지자료와 딥러닝기법을 연계한 고해상도 토양수분자료가 농업, 수문, 환경 등 다양한 분야에서 활용될 수 있을 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.159-159
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• 2017

효율적인 물 관리를 위해서는 하천 유량 파악이 필수적이지만, 경제적 이유 등으로 인하여 현장에서 정확한 유량 자료를 꾸준히 확보하는 데에는 한계가 있다. 본 연구에서는 이러한 문제점을 극복하고자 SAR 영상을 이용하여 하천의 수위와 유량을 추정하였다. SAR 영상 자료는 악천후 및 주야의 영향을 받지 않는 ESA(European Space Agency)의 Sentinel-1 영상을 이용하였다. 위성자료에서 하천의 면적을 추출한 후 수위 및 유량과의 상관관계를 분석하였다. 촬영 시간 등에 의한 위성 영상의 조도 차이에 따른 하천 면적의 오차를 제거하기 위하여 영상을 보정하였고 주변 지역에 의한 오차를 줄이기 위하여 하천유역을 분리하여 면적을 추출하였다. 이를 통해 하천 면적과 수위 및 유량의 상관관계를 파악하였다. 국내 10여 개의 하천에 대하여 기법을 적용한 결과, 수위와 유량을 비교적 정확히 추정할 수 있었다. 본 연구의 결과는 미계측 유역의 수자원 관리 능력을 향상시킬 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.313-313
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• 2018

유량 자료는 수자원 계획 및 개발, 정책결정, 관련 시설 운영 등의 가장 기초가 되는 핵심 자료이다. 하지만 전세계적으로 많은 유역에서 경제적 이유 등으로 인해 현장에서 정확한 유량 자료를 확보하는데 한계가 있다. 본 연구에서는 이러한 문제점을 극복하고자 SAR 영상을 활용한 하천의 유량 추정 기법을 개발하였다. 악천후 및 주야의 영향을 받지 않는 SAR 영상 자료인 유럽항공우주국 ESA(European Space Agency)의 Sentinel-1 영상자료와 함께 한강홍수통제소에서 제공하는 지상 관측 자료를 사용하여, 위성 영상자료에서 하천의 면적을 추출한 후 유량과의 상관관계를 분석하였다. 촬영 시간 등에 의한 위성 영상의 조도 차이에 따른 하천 면적의 오차를 제거하기 위하여 각 관측소별로 영상을 보정하였고 주변 지역에 의한 오차를 줄이기 위하여 하천유역을 분리하여 면적을 추출하였다. 이를 통해 하천 면적과 유량의 상관관계를 파악하였다. 국내 10여 개의 하천에 대하여 기법을 적용한 결과, 유량을 비교적 정확히 추정할 수 있었다. 본 연구의 결과는 미계측 유역의 수자원 관리 능력을 향상시킬 것으로 기대된다.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1143-1153
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• 2018

For TOPS InSAR processing, high-precision image co-registration is required. We propose an image co-registration method suitable for the TOPS mode by comparing the performance of cross correlation method, the geometric co-registration and the enhanced spectral diversity (ESD) matching algorithm based on the spectral diversity (SD) on the Sentinel-1 TOPS mode image. Using 23 pairs of interferometric pairs generated from 25 Sentinel-1 TOPS images, we applied the cross correlation (CC), geometric correction with only orbit information (GC1), geometric correction combined with iterative cross-correlation (GC2, GC3, GC4), and ESD iteration (ESD_GC, ESD_1, ESD_2). The mean of co-registration errors in azimuth direction by cross correlation and geometric matching are 0.0041 pixels and 0.0016 pixels, respectively. Although the ESD method shows the most accurate result with the error of less than 0.0005 pixels, the error of geometric co-registration is reduced to 0.001 pixels by repetition through additional cross correlation matching between the reference and resampled slave image. The ESD method is not applicable when the coherence of the burst overlap areas is low. Therefore, the geometric co-registration method through iterative processing is a suitable alternative for time series analysis using multiple SAR data or generating interferogram with long time intervals.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.497-510
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• 2022

Agricultural reservoirs are essential structures for water supplies during dry period in the Korean peninsula, where water resources are temporally unequally distributed. For efficient water management, systematic and effective monitoring of medium-small reservoirs is required. Synthetic Aperture Radar (SAR) provides a way for continuous monitoring of those, with its capability of all-weather observation. This study aims to evaluate the applicability of SAR in monitoring medium-small reservoirs using Sentinel-1 (10 m resolution) and Capella X-SAR (1 m resolution), at Chari (CR), Galjeon (GJ), Dwitgol (DG) reservoirs located in Ulsan, Korea. Water detected results applying Z fuzzy function-based threshold (Z-thresh) and Chan-vese (CV), an object detection-based segmentation algorithm, are quantitatively evaluated using UAV-detected water boundary (UWB). Accuracy metrics from Z-thresh were 0.87, 0.89, 0.77 (at CR, GJ, DG, respectively) using Sentinel-1 and 0.78, 0.72, 0.81 using Capella, and improvements were observed when CV was applied (Sentinel-1: 0.94, 0.89, 0.84, Capella: 0.92, 0.89, 0.93). Boundaries of the waterbody detected from Capella agreed relatively well with UWB; however, false- and un-detections occurred from speckle noises, due to its high resolution. When masked with optical sensor-based supplementary images, improvements up to 13% were observed. More effective water resource management is expected to be possible with continuous monitoring of available water quantity, when more accurate and precise SAR-based water detection technique is developed.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.17-24
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• 2018

Grounding line of a glacier or ice shelf where ice bottom meets the ocean is sensitive to changes in the polar environment. Recent rapid changes of grounding lines have been observed especially in southwestern Antarctica due to global warming. In this study, ERS-1/2 and Sentinel-1A Synthetic Aperture Radar (SAR) image were interferometrically acquired in 1996 and 2015, respectively, to monitor the movement of the grounding line in the western part of Ronne Ice Shelf near the Antarctic peninsula. Double-Differential Interferometric SAR (DDInSAR) technique was applied to remove gravitational flow signal to detect grounding line from the interferometric phase due to the vertical displacement of the tide. The result showed that ERS-1/2 grounding lines are almost consistent with those from Rignot et al. (2011) which used the similar dataset, confirming the credibility of the data processing. The comparison of ERS-1/2 and Sentinle-1A DDInSAR images showed a grounding line retreat of $1.0{\pm}0.1km$ from 1996 to 2015. It is also proved that the grounding lines based on the 2004 MODIS Mosaic of Antarctica (MOA) images and digital elevation model searching for ice plain near coastal area (Scambos et al., 2017), is not accurate enough especially where there is a ice plain with no tidal motion.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2016.11a
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• pp.357-358
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• 2016

최근 재난에 대한 광역적 탐지 및 피해상황을 예측하는데 위성레이더 영상의 활용방안이 대두되고 있다. 본 논문에서는 SENTINEL-1 위성레이더 영상을 활용하여 지진발생으로 인한 지표변위를 관측하고자 하였다. 차분간섭기법(DinSAR)을 적용하여 최근 발생한 이탈리아 중부 지진과 한반도 경주 지진의 지표변위를 관측하고 피해범위를 예측하였다. 연구결과 규모 6.4 이탈리아 지진에서 최대 20.1cm의 침하를 관측하였으며, 규모 5.8 경주 지진의 경우 발생지역 20km 범위에서 약 3cm의 지표변위를 관측하였다. 향후 지상 SAR 자료를 구축할 예정이며 재난지역의 다각적 관측자료 취득 및 보다 정확한 재난 피해를 파악 할 수 있을 것으로 기대한다.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.609-620
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• 2023

In this study, we used Sentinel-1 C-band synthetic aperture radar to calculate the surface state factor (SSF) for distinguishing the frozen-thawed state of soil. The accuracy of SSF classification was analyzed through comparison with air temperature (AT), grass temperature (GT), and underground temperature (UT). For the analysis, 116 Sentinel-1B Descending nodes observed over a period of 4 years from 2017 to 2020 were established for the central region of South Korea. AT, GT, and UT data were obtained from 23 soil moisture observation points of the Rural Development Administration during the same period, and analyzed using the 06:00 am data adjacent to the shooting time of the Sentinel-1B images. The average accuracy and F1-score for all stations were 0.63 and 0.47 for AT, 0.63 and 0.48 for GT, and 0.57 and 0.21 for UT, respectively. For winter (December-February) data, the average accuracy and F1-score were 0.66 and 0.76 for AT, 0.67 and 0.76 for GT, and 0.47 and 0.44 for UT, respectively. The increase in accuracy during winter data may be attributed to the fact that errors occurring in other seasons are not included.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.125-125
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• 2020

European Space Agency's Sentinel-1 has an improved spatial and temporal resolution, as compared to previous satellite data such as Envisat Advanced SAR (ASAR) or Advanced Scatterometer (ASCAT). Thus, the assumption used for low-resolution retrieval algorithms used by ENVISAT ASAR or ASCAT is not applicable to Sentinel-1, because a higher degree of land surface heterogeneity should be considered for retrieval. The assumption of homogeneity over land surface is not valid any more. In this study, considering that soil roughness is one of the key parameters sensitive to soil moisture retrievals, various approaches are discussed. First, soil roughness is spatially inverted from Sentinel-1 backscattering over Yanco sites in Australia. Based upon this, Artificial Neural Networks data (feedforward multiplayer perception, MLP, Levenberg-Marquadt algorithm) are compared with Fractal approach (brownian fractal, Hurst exponent of 0.5). When using ANNs, training data are achieved from theoretical forward scattering models, Integral Equation Model (IEM). and Sentinel-1 measurements. The network is trained by 20 neurons and one hidden layer, and one input layer. On the other hand, fractal surface roughness is generated by fitting 1D power spectrum model with roughness spectra. Fractal roughness profile is produced by a stochastic process describing probability between two points, and Hurst exponent, as well as rms heights (a standard deviation of surface height). Main interest of this study is to estimate a spatial variability of roughness without the need of local measurements. This non-local approach is significant, because we operationally have to be independent from local stations, due to its few spatial coverage at the global level. More fundamentally, SAR roughness is much different from local measurements, Remote sensing data are influenced by incidence angle, large scale topography, or a mixing regime of sensors, although probe deployed in the field indicate point data. Finally, demerit and merit of these approaches will be discussed.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.933-944
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• 2019

This study analyzes the tide deformation of land boundary regions on the east (Region A) and west (Region B) sides of the Ross Ice Shelf in Antarctica using Double-Differential Interferometric Synthetic Aperture Radar (DDInSAR). A total of seven Sentinel-1A SAR images acquired in 2015-2016 were used to estimate the accuracy of tide prediction model and Young's modulus of ice shelf. First, we compared the Ross Sea Height-based Tidal Inverse (Ross_Inv) model, which is a representative tide prediction model for the Antarctic Ross Sea, with the tide deformation of the ice shelf extracted from the DDInSAR image. The accuracy was analyzed as 3.86 cm in the east region of Ross Ice Shelf and it was confirmed that the inverse barometric pressure effect must be corrected in the tide model. However, in the east, it is confirmed that the tide model may be inaccurate because a large error occurs even after correction of the atmospheric effect. In addition, the Young's modulus of the ice was calculated on the basis of the one-dimensional elastic beam model showing the correlation between the width of the hinge zone where the tide strain occurs and the ice thickness. For this purpose, the grounding line is defined as the line where the displacement caused by the tide appears in the DDInSAR image, and the hinge line is defined as the line to have the local maximum/minimum deformation, and the hinge zone as the area between the two lines. According to the one-dimensional elastic beam model assuming a semi-infinite plane, the width of the hinge region is directly proportional to the 0.75 power of the ice thickness. The width of the hinge zone was measured in the area where the ground line and the hinge line were close to the straight line shown in DDInSAR. The linear regression analysis with the 0.75 power of BEDMAP2 ice thickness estimated the Young's modulus of 1.77±0.73 GPa in the east and west of the Ross Ice Shelf. In this way, more accurate Young's modulus can be estimated by accumulating Sentinel-1 images in the future.