• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.453-462
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• 2021

As the frequency of localized heavy rainfall has increased during recent years, the importance of high-resolution radar data has also increased. This study aims to correct the bias of Dual Polarization radar that still has a spatial and temporal bias. In many studies, various statistical techniques have been attempted to correct the bias of radar rainfall. In this study, the bias correction of the S-band Dual Polarization radar used in flood forecasting of ME was implemented by a Convolutional Autoencoder (CAE) algorithm, which is a type of Convolutional Neural Network (CNN). The CAE model was trained based on radar data sets that have a 10-min temporal resolution for the July 2017 flood event in Cheongju. The results showed that the newly developed CAE model provided improved simulation results in time and space by reducing the bias of raw radar rainfall. Therefore, the CAE model, which learns the spatial relationship between each adjacent grid, can be used for real-time updates of grid-based climate data generated by radar and satellites.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.229-241
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• 2024

With the advancement of big data processing technology using cloud platforms, access, processing, and analysis of large-volume data such as satellite imagery have recently been significantly improved. In this study, the Change Detection Method, a relatively simple technique for retrieving soil moisture, was applied to the backscattering coefficient values of pre-processed Sentinel-1 synthetic aperture radar (SAR) satellite imagery product based on Google Earth Engine (GEE), one of those platforms, to estimate the surface soil moisture for six observatories within the Yongdam Dam watershed in South Korea for the period of 2015 to 2023, as well as the watershed average. Subsequently, a correlation analysis was conducted between the estimated values and actual measurements, along with an examination of the applicability of GEE. The results revealed that the surface soil moisture estimated for small areas within the soil moisture observatories of the watershed exhibited low correlations ranging from 0.1 to 0.3 for both VH and VV polarizations, likely due to the inherent measurement accuracy of the SAR satellite imagery and variations in data characteristics. However, the surface soil moisture average, which was derived by extracting the average SAR backscattering coefficient values for the entire watershed area and applying moving averages to mitigate data uncertainties and variability, exhibited significantly improved results at the level of 0.5. The results obtained from estimating soil moisture using GEE demonstrate its utility despite limitations in directly conducting desired analyses due to preprocessed SAR data. However, the efficient processing of extensive satellite imagery data allows for the estimation and evaluation of soil moisture over broad ranges, such as long-term watershed averages. This highlights the effectiveness of GEE in handling vast satellite imagery datasets to assess soil moisture. Based on this, it is anticipated that GEE can be effectively utilized to assess long-term variations of soil moisture average in major dam watersheds, in conjunction with soil moisture observation data from various locations across the country in the future.

• Journal of Korea Water Resources Association
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• v.49 no.7
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• pp.589-597
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• 2016

In this study, we investigated the use of satellite-derived flow (SDF) signals and a data-based model for the estimation of outflow for the river reach where in situ measurements are either completely unavailable or are difficult to access for hydraulic and hydrology analysis such as the upper basin of Imjin River. It has been demonstrated by many studies that the SDF signals can be used as the river width estimates and the correlation between SDF signals and river width is related to the shape of cross sections. To extract the nonlinear relationship between SDF signals and river outflow, Artificial Neural Network (ANN) model with SDF signals as its inputs were applied for the computation of flow discharge at Imjin Bridge located in Imjin River. 15 pixels were considered to extract SDF signals and Partial Mutual Information (PMI) algorithm was applied to identify the most relevant input variables among 150 candidate SDF signals (including 0~10 day lagged observations). The estimated discharges by ANN model were compared with the measured ones at Imjin Bridge gauging station and correlation coefficients of the training and validation were 0.86 and 0.72, respectively. It was found that if the 1 day previous discharge at Imjin bridge is considered as an input variable for ANN model, the correlation coefficients were improved to 0.90 and 0.83, respectively. Based on the results in this study, SDF signals along with some local measured data can play an useful role in river flow estimation and especially in flood forecasting for data-scarce regions as it can simulate the peak discharge and peak time of flood events with satisfactory accuracy.

• Journal of Korea Water Resources Association
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• v.54 no.8
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• pp.577-587
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• 2021

Drought is generally considered to be a natural disaster caused by accumulated water shortages over a long period of time, taking months or years and slowly occurring. However, climate change has led to rapid changes in weather and environmental factors that directly affect agriculture, and extreme weather conditions have led to an increase in the frequency of rapidly developing droughts within weeks to months. This phenomenon is defined as 'Flash Drought', which is caused by an increase in surface temperature over a relatively short period of time and abnormally low and rapidly decreasing soil moisture. The detection and analysis of flash drought is essential because it has a significant impact on agriculture and natural ecosystems, and its impacts are associated with agricultural drought impacts. In South Korea, there is no clear definition of flash drought, so the purpose of this study is to identify and analyze its characteristics. In this study, flash drought detection condition was presented based on the satellite-derived drought index Evaporative Stress Index (ESI) from 2014 to 2018. ESI is used as an early warning indicator for rapidly-occurring flash drought a short period of time due to its similar relationship with reduced soil moisture content, lack of precipitation, increased evaporative demand due to low humidity, high temperature, and strong winds. The flash droughts were analyzed using hydrometeorological characteristics by comparing Standardized Precipitation Index (SPI), soil moisture, maximum temperature, relative humidity, wind speed, and precipitation. The correlation was analyzed based on the 8 weeks prior to the occurrence of the flash drought, and in most cases, a high correlation of 0.8(-0.8) or higher(lower) was expressed for ESI and SPI, soil moisture, and maximum temperature.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.447-459
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• 2022

This study estimated the grid-type precipitation quantile for the Korean Peninsula using PERSIANN-CCS-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record), a satellite based re-analysis precipitation data. The period considered is a total of 38 years from 1983 to 2020. The spatial resolution of the data is 0.04° and the temporal resolution is 3 hours. For the probability distribution, the Gumbel distribution which is generally used for frequency analysis was used, and the probability weighted moment method was applied to estimate parameters. The duration ranged from 3 hours to 144 hours, and the return period from 2 years to 500 years was considered. The results were compared and reviewed with the estimated precipitation quantile using precipitation data from the Automated Synoptic Observing System (ASOS) weather station. As a result, the parameter estimates of the Gumbel distribution from the PERSIANN-CCS-CDR showed a similar pattern to the results of the ASOS as the duration increased, and the estimates of precipitation quantiles showed a rather large difference when the duration was short. However, when the duration was 18 h or longer, the difference decreased to less than about 20%. In addition, the difference between results of the South and North Korea was examined, it was confirmed that the location parameters among parameters of the Gumbel distribution was markedly different. As the duration increased, the precipitation quantile in North Korea was relatively smaller than those in South Korea, and it was 84% of that of South Korea for a duration of 3 h, and 70-75% of that of South Korea for a duration of 144 h.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.144-159
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• 2006

Hydrological models are tools intended to realistically represent the basin's complex system in which hydrological characteristics result from a number of physical, vegetative, climatic, and anthropomorphic factors. Spatially distributed hydrological models were first developed in the 1960s, Remote sensing(RS) data and Geographical Information System(GIS) play a rapidly increasing role in the field of hydrology and water resources development. Although very few remotely sensed data can applied in hydrology, such information is of great. One of the greatest advantage of using RS data for hydrological modeling and monitoring is its ability to generate information in spatial and temporal domain, which is very crucial for successful model analysis, prediction and validation. In this paper, SLURP model is selected as semi-distributed hydrological model and MODIS Leaf Area Index(LAI), Normalized Difference Vegetation Index(NDVI) as Remote sensing input data to hydrological modeling of Kyung An-chen basin. The outlet of the Kyung An stage site was simulated, We evaluated two RS data, based on ability of SLURP model to simulate daily streamflows, and How the two RS data influence the sensitivity of simulated Evapotranspiration.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2002.09a
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• pp.253-256
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• 2002

거제도는 남쪽에서 두 번째로 큰 섬으로써 총면적은 399.96$\textrm{km}^2$이며 총면적의 71.85%가 임야로 이루어져 있고 하천이 짧으며 유역면적이 좁은 관계로 지하수의 함양이 어려우며 해안으로 유출되는 지하수가 상당량이 될 것으로 사료되었다. 따라서 유출지하수의 특성을 연구하여 지하수의 유출가능성이 높은 지역을 찾을 수 있도록 여러 가지 분석을 통하여 알아보았다. 이를 위하여 기본적으로 기온, 강수량 등의 기상자료와 지하수온도, 지하수위등의 수문자료 및 해수표면온도 등의 해양관측자료를 이용하였으며, 해수와 지하수의 온도차가 많은 달의 Lanset 7 ETM+ 인공위성 영상자료와 NOAA 인공영상자료를 이용하여 온도자료를 비교하고, 각개 영사의 열분포도를 분석함으로써 유출지하수의 가능성이 높은 지역을 추출하였다. 추출한 지역에서 인구밀집지역, 공단지역, 기 공급지역을 제외하였으며, 수문지질학 적으로 유리한 지역을 선정하고, 평균해수분포차가 큰 지역을 추출함으로써 이후에 이루어질 현장조사시에 접근이 용이하도록 하였다. 연구결과 거제도 일대의 해안유출지하수 가능지점은 10개소 이상이며 자연적, 사회학석인 여건을 고려한다면 지하수개발가능 지역은 6개 정도로 예상된다. 또한 해수면의 온도와 지하수의 온도가 차이가 클 때는11~13$^{\circ}C$의 분포를 보이고 있어, 이후 이와 같은 연구에 충분히 활용할 수 있을 것이며, 해상도가 높은 자료와 연계하면 보다 정확한 자료의 추출이 가능해 앞으로의 국내에 활용되지 못한 수자원 개발에도 많은 도움이 될 것으로 판단된다.하게도 유기물과의 친화력이 높은 것으로 알려진 Cu 역시 F1과 F2에 대하여 높은 함량을 나타내어 오염원으로부터의 Cu의 확산을 지시하였다. 외국에 비하여, 그동안 국내에서는 사격장 주변의 자연환경변화에 관하여 연구된 결과가 거의 전무하였다. 본 연구 결과는, 이와 유사한 사격장 주변 환경에서의 중금속 분포와 거동 특성에 대하여 종합적인 모니터링(즉, 체계적인 환경지구화학적 조사ㆍ연구)이 시급함을 시사해 주고 있다.할 수 있었다.연구지역을 대상으로 추정한 함양율은 지하수이용에 따른 지하수위하강에 대한 보정을 할 필요가 있으며 지하수이용실태조사를 추가로 하여 그 이용량만큼을 지하수함양량에 더하여야 할것이다.의 특성 등을 고려하여 거기에 맞는 기술들을 복합적으로 또는 단독으로 사용하되 처리방법 채택 시 신중을 기할 것이 요망된다.정시에는 SeaWiFS 위성과 관련된 global algorithms 중에서 490nm와 555nm의 복합밴드를 포함하는 OC2 알고리즘(ocean color chlorophyll 2 algorithm)을 사용하는 것이 OC2 series 및 OC4 알고리즘보다 좋은 추정 값을 도출할 수 있을 것으로 기대된다.환경에서는 5일에서 7월에 주로 이 충체의 유충이 발육되고 전파되는 것으로 추측되었다.러 가지 방법들을 적극 적용하여 금후 검토해볼 필요가 있을 것이다.잡은 전혀 삭과가 형성되지 않았다. 이 결과는 종간 교잡종을 자방친으로 하고 그 자방친의 화분친을 사용할 때만 교잡이 이루어지고 있음을 나타내고 있다. 따라서 여교잡을 통한 종간잡종 품종육성 활용방안을 금후 적극 확대 검토해야 할 것이다하였다.함을 보

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1405-1423
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• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.194-208
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• 2021

The Hwanggang Dam in North Korea is located upstream of the Imjin River which is a shared river in the border area. It is known to have a reservoir capacity of 350 million cubic meters and releases a discharge primarily for generating hydroelectric power and partly for transferring to the Yesung River basin. Due to the supply of water from the Hwanggang Dam to another basin, the flow of the Imjin River has decreased, which has a negative impact on the water supply, river maintenance flow, water quality, and ecological environment in Korea. However, due to the special national security issue of the South and North Korea border region, the hydrological data is not shared, and the operation method of the Hwanggang Dam is unknown, so there is a risk of damage to the southern part of the downstream area. In this study, the monthly diversion as the long-term runoff concept was derived through the calibrated hydrological model based on optical remotely sensed Images and water balance analysis. As a result of the water balance analysis from January 2019 to September 2021, the average diversion of the Hwanggang Dam was 29.2m³/s, which is equivalent to 922 million tons per year and 45.6% of the annual inflow of 2.02 million tons into the Hwanggang Dam.