• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.141-141
• /
• 2019

토양수분은 수문 분석에 있어 매우 중요한 인자 중 하나이며 최근 기후변화로 인한 가뭄, 홍수 및 산불발생과 같은 물 관련 재해 발생에 직 간접적으로 영향을 미치기 때문에 지표 토양수분산정은 매우 중요하다. Sentinel-1 SAR(Synthetic Aperture Radar)는 능동형 위성으로 10m의 공간해상도로 제공되기 때문에 기존의 토양수분 전용위성인 SMOS(Soil Moisure and Ocean Salinity), SMAP(Soil Moisture Active Passive) 및 GCOM-W1(Global Change Observation Mission Water) 등 다르게 고해상도 토양수분 산정이 가능하다. 그러나 Sentinel-1 SAR 센서에서는 고해상도 지표 관측 이미지 자료만 제공하며, 토양수분 자료를 직접적으로 제공하지 않는다. 따라서 본 연구에서는 2018년도 Sentinel-1 A/B IW(Interferometric Wide swath) 모드의 VH(Vertical Transmit - Horizontal Receive) 편파 영상과 Sentinel-1 SAR 위성자료 전처리 도구인 SNAP(Sentinel Application Platform)을 이용하여 후방산란계수를 산정하였으며, 산정된 후 방산란계수와 농촌진흥청에서 제공하는 65개 지점의 실측 TDR(Time Domain Reflectrometry) 토양수분의 관계를 이용하여 회귀모형을 도출 및 토양수분 공간분포를 산정하였다. 비록 불확실성은 어느정도 발생 하였으나, 전체적으로 TDR 관측값과 $10m{\times}10m$ 해상도의 Sentinel-1 SAR 기반 토양수분이 일치하는 경향을 보였다. 본 연구 결과는 수문, 농업, 산림, 재해 등 다양한 분야에 활용될 수 있을 것으로 판단된다.

• Korean Journal of Remote Sensing
• /
• v.37 no.4
• /
• pp.789-802
• /
• 2021

Synthetic Aperture Radar (SAR) at C-band is an ideal remote sensing system for crop monitoring owing to its short wavelength, which interacts with the upper parts of the crop canopy. This study evaluated the potential of dual polarimetric Sentinel-1 at C-band for monitoring rice phenology. Rice phenological variations occur in a short period. Hence, the short revisit time of Sentinel-1 SAR system can facilitate the tracking of short-term temporal morphological variations in rice crop growth. The sensitivity of SAR backscattering coefficients, backscattering ratio, and polarimetric decomposition parameters on rice phenological stages were investigated through a time-series analysis of 33 Sentinel-1 Single Look Complex images collected from 10th April to 25th October 2020 in Gimhae, South Korea. Based on the observed temporal variations in SAR parameters, we could identify and distinguish the phenological stages of the Gimhae rice growth cycle. The backscattering coefficient in VH polarisation and polarimetric decomposition parameters showed high sensitivity to rice growth. However, amongst SAR parameters estimated in this study, the VH backscattering coefficient realistically identifies all phenological stages, and its temporal variation patterns are preserved in both Sentinel-1A (S1A) and Sentinel-1B (S1B). Polarimetric decomposition parameters exhibited some offsets in successive acquisitions from S1A and S1B. Further studies with data collected from various incidence angles are crucial to determine the impact of different incidence angles on polarimetric decomposition parameters in rice paddy fields.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_2
• /
• pp.737-746
• /
• 2022

Utilizing weather independent SAR images along with machine learning based object detector is effective in robust vessel monitoring. While conventional SAR images often applied amplitude data from Single Look Complex, exploitation of polarimetric parameters acquired from multiple polarimetric SAR images was yet to be implemented to vessel detection utilizing machine learning. Hence, this study used four polarimetric parameters (H, p₁, DoP, DPRVI) retrieved from eigen-decomposition and two backscattering coefficients (γ₀, _VV, γ₀, _VH) from radiometric calibration; six bands in total were respectively exploited from 52 Sentinel-1 SAR images, accompanied by vessel training data extracted from AIS information which corresponds to acquisition time span of the SAR image. Evaluating different cases of combination, the use of polarimetric indexes along with amplitude values derived enhanced vessel detection performances than that of utilizing amplitude values exclusively.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.61 no.5
• /
• pp.89-99
• /
• 2019

In this study, we estimated the spatially-distributed soil moisture at the high resolution ($10m{\times}10m$) using the satellite-based Sentinel-1A/B SAR (Synthetic Aperture Radar) sensor images. The Sentinel-1A/B raw data were pre-processed using the SNAP (Sentinel Application Platform) tool provided from ESA (European Space Agency), and then the pre-processed data were converted to the backscatter coefficients. The regression equations were derived based on the relationships between the TDR (Time Domain Reflectometry)-based soil moisture measurements and the converted backscatter coefficients. The TDR measurements from the 51 RDA (Rural Development Administration) monitoring sites were used to derive the regression equations. Then, the soil moisture values were estimated using the derived regression equations with the input data of Sentinel-1A/B based backscatter coefficients. Overall, the soil moisture estimates showed the linear trends compared to the TDR measurements with the high Pearson's correlations (more than 0.7). The Sentinel-1A/B based soil moisture values matched well with the TDR measurements with various land surface conditions (bare soil, crop, forest, and urban), especially for bare soil (R: 0.885~0.910 and RMSE: 3.162~4.609). However, the Mandae-ri (forest) and Taean-eup (urban) sites showed the negative correlations with the TDR measurements. These uncertainties might be due to limitations of soil surface penetration depths of SAR sensors and complicated land surface conditions (artificial constructions near the TDR site) at urban regions. These results may infer that qualities of Sentinel-1A/B based soil moisture products are dependent on land surface conditions. Although uncertainties exist, the Sentinel-1A/B based high-resolution soil moisture products could be useful in various areas (hydrology, agriculture, drought, flood, wild fire, etc.).

• Korean Journal of Remote Sensing
• /
• v.33 no.6_3
• /
• pp.1215-1221
• /
• 2017

SeNtinel's Application Platform (SNAP) is an open source software developed by the European Space Agency and consists of several toolboxes that process data from Sentinel satellite series, including SAR (Synthetic Aperture Radar) and optical satellites. Among them, S1TBX (Sentinel-1 ToolBoX)is mainly used to process Sentinel-1A/BSAR images and interferometric techniques. It provides flowchart processing method such as Graph Builder, and has convenient functions including automatic downloading of DEM (Digital Elevation Model) and image mosaicking. Therefore, if computer memory is sufficient, InSAR (Interferometric SAR) and DInSAR (Differential InSAR) perform smoothly and are widely used recently in the world through rapid upgrades. S1TBX also includes existing SAR data processing functions, and since version 5, the processing capability of KOMPSAT-5 has been added. This paper shows an example of processing the interference technique of KOMPSAT-5 SAR image using S1TBX of SNAP. In the open mine of Tavan Tolgoi in Mongolia, the difference between DEM obtained in KOMPSAT-5 in 2015 and SRTM 1sec DEM obtained in 2000 was analyzed. It was found that the maximum depth of 130 meters was excavated and the height of the accumulated ore is over 70 meters during 15 years. Tidal and topographic InSAR signals were observed in the glacier area near Jangbogo Antarctic Research Station, but SNAP was not able to treat it due to orbit error and DEM error. In addition, several DInSAR images were made in the Iraqi desert region, but many lines appearing in systematic errors were found on coherence images. Stacking for StaMPS application was not possible due to orbit error or program bug. It is expected that SNAP can resolve the problem owing to a surge in users and a very fast upgrade of the software.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.102-102
• /
• 2019

토양수분은 수문 및 기상 현상의 주요 요인으로 가뭄, 홍수 및 범람과 같은 자연 재해와 관련이 깊은 인자이다. 이러한 토양수분의 관측 기술 중 위성 데이터를 활용한 원격탐사 기술은 광범위한 지역의 관측이 용이하고 지점이 아닌 공간 데이터를 제공하는 장점을 지니고 있어 토양수분의 관측에 유리하다. 특히 높은 해상도의 위성기반 토양수분 데이터는 토양수분의 변동성이 큰 지역의 수문, 기상학적 현상을 보다 자세히 분석할 수 있게 해주며 가뭄 및 범람과 같은 수자원 관련 재해를 정확하게 분석하는데 요구된다. 이로 인해 최근 Sentinel-1 위성에서 운용중인 Synthetic Aperture Radar(SAR) 데이터를 이용한 매우 높은 공간해상도(10m~1km)를 지니고 있는 토양수분데이터 생산에 관한 연구가 세계적으로 활발히 진행되고 있다. 그러나 국내에서는 Sentinel-1 위성을 이용한 토양수분 데이터 복원에 관한 연구가 미비한 실정이다. 따라서 본 연구에서는 파주 감악산 설마천 유역에서의 Sentinel-1 위성의 SAR 데이터를 이용한 고해상도 토양수분 데이터를 복원하고자 한다. 파주 설마천 유역은 감악산 일대로 경사가 심하고 식생이 두터운 산악지형이다. SAR를 이용하여 산지에서 신뢰성 있는 토양수분 자료를 복원하기 위해서는 가장 큰 오차의 원인으로 작용하는 경사와 식생을 고려하여야 한다. 먼저 표면 경사의 영향의 경우 SAR 센서의 레이더 입사각과 수치 표고 모델을 이용하여 고려하고자 한다. 다음 과정으로 표면 경사가 고려된 Sentinel-1 데이터의 후방산란계수와 Landsat-8 데이터 및 지점 토양수분 데이터를 이용하여 식생에 따른 후방산란계수의 거동을 Water Cloud Model을 이용하여 분석하였다. Water Cloud Model은 토양위의 식생의 수분이 후방산란계수에 혼동을 주는 구름과 같이 작용한다고 가정하고 식생수분을 후방산란계수와 레이더 입사각 및 식생지수를 통해 계산하는 모델이며 이를 이용하여 토양수분 복원에 있어 식생의 영향을 제거하고자 하였다. 이를 통해 식생과 표면 경사를 고려하여 복원된 토양수분 데이터를 설마천 유역의 지점 데이터와 비교 분석하고 다른 위성기반 토양수분 데이터 및 강우 데이터를 이용하여 평가하였다. 본 연구결과를 통해 한반도 산지에서의 SAR 데이터를 이용한 토양수분 복원 기술의 기초가 마련될 것이며 이를 통해 산지가 대부분인 한반도의 토양수분 거동을 이해하는데 유용한 자료를 제공할 수 있을 것으로 기대된다. 본 연구 이후에는 연구결과분석을 통한 산지에서의 고해상도 토양수분 복원 알고리즘을 분석, 보완하고 한반도에서의 SAR 기반 토양수분 데이터의 정확도를 높이는 연구가 진행되어야 할 것이다.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_3
• /
• pp.901-912
• /
• 2022

The frequency of disasters is increasing due to global climate change, and unusual heavy rains and rainy seasons are occurring in Korea. Periodic monitoring and rapid detection are important because these weather conditions can lead to drought and flooding, causing secondary damage. Although research using optical images is continuously being conducted to determine the waterbody, there is a limitation in that it is difficult to detect due to the influence of clouds in order to detect floods that accompany heavy rain. Therefore, there is a need for research using synthetic aperture radar (SAR) that can be observed regardless of day or night in all weather. In this study, using Sentinel-1 SAR images that can be collected in near-real time as open data, the UNet model among deep learning algorithms that have recently been used in various fields was applied. In previous studies, waterbody detection studies using SAR images and deep learning algorithms are being conducted, but only a small number of studies have been conducted in Korea. In this study, to determine the applicability of deep learning of SAR images, UNet and the existing algorithm thresholding method were compared, and five indices and Sentinel-2 normalized difference water index (NDWI) were evaluated. As a result of evaluating the accuracy with intersect of union (IoU), it was confirmed that UNet has high accuracy with 0.894 for UNet and 0.699 for threshold method. Through this study, the applicability of deep learning-based SAR images was confirmed, and if high-resolution SAR images and deep learning algorithms are applied, it is expected that periodic and accurate waterbody change detection will be possible in Korea.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_1
• /
• pp.571-585
• /
• 2022

As the threat of natural disasters such as droughts, floods, forest fires, and landslides increases due to climate change, social demand for high-resolution soil moisture retrieval, such as Synthetic Aperture Radar (SAR), is also increasing. However, the domestic environment has a high proportion of mountainous topography, making it challenging to retrieve soil moisture from SAR data. This study evaluated the usability of Sentinel-1 SAR, which is applied with the Artificial Neural Network (ANN) technique, to retrieve soil moisture. It was confirmed that the backscattering coefficient obtained from Sentinel-1 significantly correlated with soil moisture behavior, and the possibility of stand-alone use to correct vegetation effects without using auxiliary data observed from other satellites or observatories. However, there was a large difference in the characteristics of each site and topographic group. In particular, when the model learned on the mountain and at flat land cross-applied, the soil moisture could not be properly simulated. In addition, when the number of learning points was increased to solve this problem, the soil moisture retrieval model was smoothed. As a result, the overall correlation coefficient of all sites improved, but errors at individual sites gradually increased. Therefore, systematic research must be conducted in order to widely apply high-resolution SAR soil moisture data. It is expected that it can be effectively used in various fields if the scope of learning sites and application targets are specifically limited.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.117-117
• /
• 2021

본 연구에서는 Sentinel-1A/B C-band SAR(Synthetic Aperture Radar) 위성영상을 기반으로 인공신경망(Artificial Neural Network, ANN) 모형을 활용해 금강 유역 상류 40×50 km² 면적에 대한 토양수분을 산정하였다. 10 m 공간 해상도의 Sentinel-1A/B SAR 영상은 8일 간격으로 2015년부터 2019년까지 5년 동안 구축하였고, SNAP(SentiNel Application Platform)을 통해 기하 보정, 방사 보정 및 잡음(Noise) 보정을 수행하고 VV 및 VH 편파 후방산란계수로 변환하였다. ANN 모형 검증자료로 TDR(Time Domain Reflectometry)로 측정된 9개 지점의 실측 토양수분 자료를 구축하였으며, 수문학적 개념인 선행강우를 고려하기 위해 동지점에 대한 강수량 자료를 구축하였다. ANN은 각 지점에 해당하는 토양 속성별로 모델링하고, 전체 기간 및 계절별로 나누어 모의하였으며, 전체 자료의 60%와 40%를 각각 훈련 및 테스트 데이터로 사용하였다. 산정된 토양수분은 상관계수(Correlation Coefficient, R)와 평균제곱근오차(Root Mean Square Error, RMSE)를 활용하여 검증을 수행할 예정이다.

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