• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1125-1135
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• 2022

Damages by heavy snow does not occur very often, but when it does, it causes damage to a wide area. To mitigate snow damage, it is necessary to know, in advance, the depth of snow that causes damage in each region. However, snow depths are measured at observatory locations, and it is difficult to understand the spatial distribution of snow depth that causes damage in a region. To understand the spatial distribution of snow depth, the point measurements are interpolated. However, estimating spatial distribution of snow depth is not easy when the number of measured snow depth is small and topographical characteristics such as altitude are not similar. To overcome this limit, satellite images such as Synthetic Aperture Radar (SAR) can be analyzed using Differential Interferometric SAR (DInSAR) method. DInSAR uses two different SAR images measured at two different times, and is generally used to track minor changes in topography. In this study, the spatial distribution of snow depth was estimated by DInSAR analysis using dual polarimetric IW mode C-band SAR data of Sentinel-1B satellite operated by the European Space Agency (ESA). In addition, snow depth was estimated using geostationary satellite Chollian-2 (GK-2A) to compare with the snow depth from DInSAR method. As a result, the accuracy of snow cover estimation in terms with grids was about 0.92% for DInSAR and about 0.71% for GK-2A, indicating high applicability of DInSAR method. Although there were cases of overestimation of the snow depth, sufficient information was provided for estimating the spatial distribution of the snow depth. And this will be helpful in understanding regional damage-causing snow depth.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.26-67
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• 2020

Synthetic Aperture Radar(SAR) is able to photograph the earth's surface regardless of weather conditions, day and night. Because of its possibility to search for hydrological factors such as soil moisture and groundwater, and its importance is gradually increasing in the field of water resources. SAR began to be mounted on satellites in the 1970s, and about 15 or more satellites were launched as of 2020, which around 10 satellites will be launched within the next 5 years. Recently, various types of SAR technologies such as enhancement of observation width and resolution, multiple polarization and multiple frequencies, and diversification of observation angles were being developed and utilized. In this paper, a brief history of the SAR system, as well as studies for estimating soil moisture and hydrological components were investigated. Up to now hydrological components that can be estimated using SAR satellites include soil moisture, subsurface groundwater discharge, precipitation, snow cover area, leaf area index(LAI), and normalized difference vegetation index(NDVI) and among them, soil moisture is being studied in 17 countries in South Korea, North America, Europe, and India by using the physical model, the IEM(Integral Equation Model) and the artificial intelligence-based ANN(Artificial Neural Network). RADARSAT-1, ENVISAT, ASAR, and ERS-1/2 were the most widely used satellite, but the operation has ended, and utilization of RADARSAT-2, Sentinel-1, and SMAP, which are currently in operation, is gradually increasing. Since Korea is developing a medium-sized satellite for water resources and water disasters equipped with C-band SAR with the goal of launching in 2025, various hydrological components estimation researches using SAR are expected to be active.

• Atmosphere
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• v.24 no.3
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• pp.433-444
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• 2014

Although the Radar-AWS Rainrate (RAR) calculation system operated by Korea Meteorological Administration estimated precipitation using 2-dimensional composite components of single polarization radars, this system has several limitations in estimating the precipitation accurately. To to overcome limitations of the RAR system, the Korea Meteorological Administration developed and operated the RMQ (Radar-based Multi-sensor Quantitative Precipitation Estimation) system, the improved version of NMQ (National Mosaic and Multi-sensor Quantitative Precipitation Estimation) system of NSSL (National Severe Storms Laboratory) for the Korean Peninsula. This study introduced the RMQ system domestically for the first time and verified the precipitation estimation performance of the RMQ system. The RMQ system consists of 4 main parts as the process of handling the single radar data, merging 3D reflectivity, QPE, and displaying result images. The first process (handling of the single radar data) has the pre-process of a radar data (transformation of data format and quality control), the production of a vertical profile of reflectivity and the correction of bright-band, and the conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) produces the 3D composite reflectivity field after correcting the quality controlled single radar reflectivity. The QPE process classifies the precipitation types using multi-sensor information and estimates quantitative precipitation using several Z-R relationships which are proper for precipitation types. This process also corrects the precipitation using the AWS position with local gauge correction technique. The last process displays the final results transformed into images in the web-site. This study also estimated the accuracy of the RMQ system with five events in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate) and RMQ systems. The RMQ system ($2.36mm\;hr^{-1}$ in RMSE on average) is superior to the RAR system ($8.33mm\;hr^{-1}$ in RMSE) and improved by 73.25% in RMSE and 25.56% in correlation coefficient on average. The precipitation composite field images produced by the RMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore, the RMQ system has contributed to improve the accuracy of precipitation estimation using weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.

• Atmosphere
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• v.26 no.4
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• pp.509-522
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• 2016

Heavy snowfall events have occurred frequently in the Yeongdong region but understanding of these events have trouble in lack of snowfall observation in this region because it is composed of complex topography like the "Taebaek mountains" and the "East sea". These problems can be solved by quantitative precipitation estimation technique using remote sensing such as radar, satellite, etc. Two radars which are able to cover over Yeondong region were installed at Gangneung (GNG) and Gwangdeoksan (GDK). This study uses radar and water equivalent of snow cover to investigate the characteristics of radar echoes and the $Z_e-R$ relations associated with the 10 Yeongdong heavy snowfall events during the last 5 years (2010~2014). It was found that the heights which the probability of detection (POD) of snow detection by GNG radar is more than 80% are 3,000 m and 1,500 m in convective cloud and stratiform cloud, respectively. The vertical gradient of radar reflectivity is less decreased in convective cloud than stratiform cloud. However, POD by GDK radar are lower than 80% at all layers because the majority of Yeondong observational stations are more than 100 km away from GDK radar site. Furthermore, we examined $Z_e-R$ relation from the 10 events using GNG radar and compared the "a" and "b" obtained from these examinations at Sokcho (SC) and Daegwallyeong (DG). These "a" and "b" are estimated from radar echo at 500 m (SC) and 1,500 m (DG). The values of "a" differ in their stations such as SC and DG are 30~116 and 6~39, respectively. But "b" is 0.4~1.7 irrespective of stations. Moreover, the value of "a" increased with surface air temperature. Therefore, quantitative precipitation estimation in heavy snowfall events by radar echo using fixed "a" and "b" is difficult because these values changed according to those precipitation characteristics.

• Journal of Satellite, Information and Communications
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• v.10 no.4
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• pp.117-127
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• 2015

Recently, research and development on satellite payloads are being developed such as the optical sensor, SAR etc. Satellite image for earth observation is being utilized both domestically and abroad. Advanced satellite payload technology has led to the collection and analysis of satellite images relying on the optical sensor. Currently, related organizations such as RDA(the Rural Development Administration) are collectively collaborating to plan a national project to develop a medium-sized satellite based on Korea's domestic technology independently. This paper investigated the cases of the past research on application of satellite images for agriculture and analyzed the technical specifications for satellite payload in each area of such application. Based on the results of the past surveys and consultation studies among local experts in satellite image application, we analyzed the current trends, plans and applications of domestic and overseas R&D in satellite payloads for earth observation in agriculture, and proposed the appropriate technical specifications for developing a future medium-sized satellite for agriculture. The proposed specifications were then incorporated into a simulated satellite to examine its performance to observe the Korean farming areas. The authors anticipate that the findings of this paper will form a useful technical basis for providing the appropriate specifications for developing future medium-sized satellite payloads to be used in agriculture and forestry, and enabling the end users to efficiently utilize the satellite.

• Atmosphere
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• v.24 no.2
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• pp.173-188
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• 2014

The KMA has cooperated with the Oklahoma University in USA to develop a Polarimetric Radar Data (PRD) simulator to improve the microphysical processes in Korea Local Analysis and Prediction System (KLAPS), which is critical for the utilization of PRD into Numerical Weather Prediction (NWP) field. The simulator is like a tool to convert NWP data into PRD, so it enables us to compare NWP data with PRD directly. The simulator can simulate polarimetric radar variables such as reflectivity (Z), differential reflectivity ($Z_{DR}$), specific differential phase ($K_{DP}$), and cross-correlation coefficient (${\rho}_{hv}$) with input of the Drop Size Distribution (DSD) and scattering calculation of the hydrometeors. However, the simulator is being developed based on the foreign observation data, therefore the PRD simulator development reflecting rainfall characteristics of Korea is needed. This study analyzed a potential application of the 2-Dimension Video Disdrometer (2DVD) data by calculating the raindrop axis ratio according to the rain-types to reflect Korea's rainfall characteristics into scattering module in the simulator. The 2DVD instrument measures the precipitation DSD including the fall velocity and the shape of individual raindrops. We calculated raindrop axis ratio for stratiform, convective and mixed rainfall cases after checking the accuracy of 2DVD data, which usually represent the scattering characteristics of precipitation. The raindrop axis ratio obtained from 2DVD data are compared with those from foreign database in the simulator. The calculated the dual-polarimetric radar variables from the simulator using the obtained raindrop axis ratio are also compared with in situ dual-polarimetric observation data at Bislsan (BSL). 2DVD observation data show high accuracies in the range of 0.7~4.8% compared with in situ rain gauge data which represents 2DVD data are sufficient for the use to simulator. There are small differences of axis ratio in the diameter below 1~2 mm and above 4~5 mm, which are more obvious for bigger raindrops especially for a strong convective rainfall case. These differences of raindrop axis ratio between domestic and foreign rainfall data base suggest that the potential use of disdrometer observation can develop of a PRD simulated suitable to the Korea precipitation system.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.737-750
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• 2019

Information of target changes in inaccessible areas is very important in terms of national security. Fast and accurate change detection of targets is very important to respond quickly. Spaceborne synthetic aperture radar can acquire images with high accuracy regardless of weather conditions and solar altitude. With the recent increase in the number of SAR satellites, it is possible to acquire images with less than one day temporal resolution for the same area. This advantage greatly increases the availability of change detection for inaccessible areas. Commonly available information in satellite SAR is amplitude and phase information, and change detection techniques have been developed based on each technology. Those are amplitude Change Detection (ACD), Coherence Change Detection (CCD). Each algorithm differs in the preprocessing process for accurate automatic classification technique according to the difference of information characteristics and the final detection result of each algorithm. Therefore, by analyzing the academic research trends for ACD and CCD, each technologies can be complemented. The goal of this paper is identifying current issues of SAR change detection techniques by collecting research papers. This study would help to find the prerequisites for SAR change detection and use it to conduct periodic detection research on inaccessible areas.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.398-398
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• 2018

OECD 발표에 의하면 물산업 관련 인프라 투자 전망은 전세계 GDP 대비 2010~2020년 약 1.01%에서 2020~2030년 약 1.03%로 확대될 전망으로 다른 통신, 전력, 철도 인프라 투자수요보다 많을 것으로 전망하고 있다(파이넨셜 뉴스, 2013.3.21.). 우리나라는 2005년 베트남 홍강종합개발사업을 시작으로 2015년 기준으로 세계 35개국에 진출하고 있다. 그러나 대부분의 물 산업 진출 대상 국가는 미계측 유역이 많고 지상에서 계측된 수문 자료가 부족한 실정이다. Namgung and Lee(2014)에 의하면 네팔의 수력발전소 건설에 관측된 강우량 자료가 없어 발전소 하류 10km 지점의 유하량 자료를 이용하여 자료의 정확도 검증을 대신하여 적용한 바 있다. 이와 같이 계측자료가 없거나 부족한 지역에 대하여 기상 위성을 이용하여 추정된 강수량 자료가 해당 지역의 강수 특성을 파악하는데 중요한 자료로 이용될 수 있다. 글로벌 위성 기반의 강수량 관측에 대한 역사는 1979년에 IR방법에 의해 위성으로부터 강우자료를 유도하는 개념이 도입된 이후 1987년 다중 채널의 마이크로파(MW) 복사계를 이용한 방법, 이후 두 IR과 MW를 혼합한 방법에서, 1997년 TRMM위성의 PR(Precpipitation Radar)의 레이더를 이용하는 방법, 그리고 2014년 GPM 핵심 위성(GPM Core Observatory)에 탑재된 Dual PR에 의한 방법으로 위성강수의 정확도를 매우 높여가고 있다. 본 연구는 KOICA 사업으로 진행중인 필리핀 메트로 마닐라 홍수조기경보 및 모니터링 체계 구축사업 중 파시그-마라키나강(Pasig-Marakina) 유역의 2012년 8월의 홍수사상에 대한 위성강우 및 글로벌 지형자료를 이용하여 홍수 유출량을 추정하는 것으로 목적으로 하고 있다. 유역내 6개 관측소의 일일 강우량 자료와 GPM IMERG 일강우량 자료 상관분석 결과 약 0.623, Bias는 -0.147, RMSE는 15.7정도로 분석되었다. 홍수량 분석은 2012년 8월 홍수가 발생한 시기인 2012년 8월 1일 00(UTC)부터 2012년 8월 16일 00(UTC)까지의 1시간 간격의 위성강우자료와 글로벌 지형자료를 이용하였고, 한국건설기술연구원의 MapWindow 기반 GRM 모형(mwGRM)을 이용하였다. 분석 결과 첨부홍수가 발생한 시기는 8월 7일 18:00(UTC)였고, 첨두 홍수량은 $4,073.9m^3/sec$로 분석되었다. 향후 수위-유량 관계식에 의해 정확도평가를 수행할 계획이다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.646-654
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• 2020

This paper is to develop an electromagnetic wave-based sensor that can measure the spatial distribution of precipitation, and to a electromagnetic wave rain gauge (hereinafter, "EWRG") capable of simultaneously measuring rainfall, snowfall, and wind field, which are the core of heavy rain observation. Through this study, the LFM transmission and reception signals were theoretically analyzed. In addition, In order to develop a radar transceiver, LFM transceiver design and simulation were conducted. In this paper, we developed a K-BAND pulse-driven 6W SSPA(Solid State Power Amplifiers) transceiver using a small HMIC(Hybrid Microwave Integrated Circuit). It has more than 6W of output power and less than 5dB of receiving NF(Noise Figure) with short duty of 1% in high temperature environment of 65 degrees. The manufactured module emits LFM and Square Pulse waveform with the built-in waveform generator, and the receiver has more than 40dB of gain. The transceiver developed in this paper can be applied to the other small weather radar.

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