Our research projects

국가 연구 프로젝트 진행 현황
(1) 2025 - 2031   (과학기술정보통신부) AI기반 미래 기후 기술 개발 원천 연구사업
(2) 2025 - 2030 (과학기술정보통신부) InnoCore Program: AI-Transformed Aerospace
(3) 2024 - 2029 (과학기술정보통신부) 농경지/산림지역 한국형 수치예보모델 정확도 향상 연구를 위한 수동형 L-band 라디오미터 관측 시스템 도입
(4) 2025 - 2028 (과학기술정보통신부) AI 요소모델(Emulator)-수치예보모델 연계 기반 한국형 위험기상 조기예측 기술 개발: GNSS-R 군집위성 및 수문위성 활용
(5) 2025 - 2026 (과학기술정보통신부) 위성자료 기반 북한 기후변화 대응 역량 분석 및 남북 과학기술 협력모델 개발: 검증관측소 구축 가능성 중심으로
(6) 2025 - 2030 (환경부) AI-순환경제 클러스터링 특성화대학원 육성사업

Status of National Research Projects
‍(1) 2025 - 2031     (Ministry of Science and ICT) Fundamental Research Program for Developing AI-based Future Climate Technologies
(2) 2025 - 2030   (Ministry of Science and ICT) InnoCore Program: AI-Transformed Aerospace
(3) 2024 - 2029 (Ministry of Science and ICT) Introduction of Passive L-band Radiometer Observation System to Improve the Accuracy of the                               Korean Numerical Weather Prediction Model for Agricultural and Forested Areas
(4) 2025 - 2028 (Ministry of Science and ICT) Development of Early Warning Technology for Extreme Weather in Korea Based on AI Emulator–                              Numerical Weather Prediction Model Coupling: Utilizing GNSS-R Satellite Constellations and Water Resources Satellite
(5) 2025 - 2026 (Ministry of Science and ICT) Analysis of North Korea’s Climate Change Response Capacity Using Satellite Data and Development                           of a South-North Science and Technology Cooperation Model: Focusing on the Feasibility of Establishing Validation Observation                               Sites
(6) 2025 - 2030 (Ministry of Environment) Graduate School Program for Specialization in AI–Circular Economy Clustering

Multi-Scale Soil Moisture Monitoring Using ELBARA-III and Drone Radiometer

The Portable L-Band Radiometer, mounted on a drone, enables high-resolution soil moisture retrieval across diverse terrains. Operating at 1.4 GHz (L-band), it collects brightness temperature (TB) data over rice paddies and varied vegetation zones, complementing satellite missions like SMAP and SMOS. By integrating airborne observations with ground-based sensors, the system enhances spatial coverage and improves soil moisture retrieval models for climate and agricultural applications. This drone-based approach significantly increases data accuracy and efficiency compared to traditional methods.

Read this project

Developing the First Long-Term Soil Moisture and Brightness Temperature Measurement Site in South Korea Using L-Band Radiometers and Drones

Soil moisture (SM) is vital for hydrometeorology and agriculture, yet its retrieval faces challenges. Satellite-based methods (e.g., SMAP, SMOS) offer global coverage but have resolution limitations, model-based methods lack accuracy in extreme events, and ground-based observations provide precision but limited coverage. To improve satellite SM validation, this project establishes Korea’s first Core Validation Site (CVS) in Hampyeong-gun and Naju-si, deploying TEROS 12-6 sensors across 9 km, 3 km, and 1 km grids for continuous SM and temperature monitoring. The ESA ELBARA-III L-Band Radiometer and drone-mounted PoLRa will collect brightness temperature (TB) data, aiding SM retrieval model development with soil properties and climate data. This CVS will enhance climate resilience studies, SM monitoring, and satellite validation in Korea.

Read this project

AI-Based Water Quality Prediction for Inland Water using Satellite and Land Surface Model Data

Climate change-driven heatwaves and water cycle disruptions threaten inland water quality (WQ), necessitating efficient monitoring. Traditional methods are labor-intensive with limited coverage, prompting us to develop an AI-based model for predicting chlorophyll-a concentrations in lakes and rivers. By integrating high-resolution satellite imagery (Landsat-8/9, Sentinel-2/3) with land surface models (ERA5-Land, GLDAS, MERRA-2), our model tailors predictions to different water bodies. Future plans include incorporating socio-statistical data (e.g., population, livestock) and climate scenarios (RCP, SSP). Using AI, GIS, and high-performance computing, we explore low-concentration chlorophyll-a prediction, precipitation and flow speed impacts on WQ, transfer learning, multi-sensor data fusion, and uncertainty quantification. Beyond chlorophyll-a, we aim to extend predictions to turbidity and dissolved oxygen, providing a comprehensive AI-driven approach to monitoring and mitigating climate change effects on inland water quality.

Read this project

Harnessing Deep Learning to Predict and Decode the Mysteries of Flash Droughts (GAN/SHAP/3D-CNN with Transfer Learning)

The application of deep learning in predicting flash droughts offers a transformative approach to understanding and anticipating these rapid-onset events, significantly enhancing preparedness and response strategies. By unraveling the complex mechanisms behind flash droughts, this project aims to provide precise, timely forecasts, thereby mitigating the severe agricultural, ecological, and socioeconomic impacts associated with these phenomena.

Read this project

Streamflow and Drought Predictions over Ungaged Regions using Deep and Transfer Learning Approaches

Streamflow and flash drought predictions are essential for managing water resources and mitigating potential disasters in ungaged regions. With remotely-sensed data, deep and transfer learning approaches provide powerful tools to analyze complex hydrological data, enabling more accurate predictions and better decision-making in these areas.

Read this project

Applications of Bayesian Machine Learning in Big Data in Earth Science

Bayesian methods help us improve our guesses by using new information. In Earth science, these methods are applied to big data to better understand our planet. This approach is useful for predicting things like natural disaster patterns and climate changes. By continuously updating our knowledge with new data, we can make more accurate predictions and decisions in Earth science.

Read this project

Water Balance Budgeting with Bayesian Machine Learning

The water balance equation in Earth science, P = E + R + etc, describes the relationship between precipitation (P), evaporation (E), runoff (R), and etc (e.g., soil moisture, ground water) in a given area. Bayesian inference can be applied to solve this equation by incorporating prior knowledge and updating the probability distributions of the variables based on new data, ultimately improving water resource management and prediction.

Read this project

Integrating Earth Science and Engineering for Climate Resilience: Innovative Approaches to Infrastructure and Societal Justice

Earth science informs infrastructure development by providing insights into site suitability, resource management, and sustainable design, enhancing the resilience and long-term viability of projects. It also plays a crucial role in addressing societal justice related to climate change by helping identify vulnerable communities and develop mitigation strategies, ensuring equitable access to resources and protection from environmental hazards.

Read this project

Enhancing Earth Science Predictions through Advanced Data Assimilation Techniques

Data assimilation is vital in earth science as it integrates diverse observations and model simulations, improving the accuracy of forecasts and predictions. This process enhances our understanding of complex Earth systems, enabling better decision-making for environmental management and climate adaptation.

Read this project

Floods and Droughts Predictions using Machine Learning Approaches

Satellite data and machine learning transformed Earth science by predicting and monitoring natural disasters. This combination delivers precise and timely predictions, crucial for mitigating the impacts of events like floods and droughts.

Read this project

Data Error Characterizations

Characterizing the error of satellite data and land surface models is vital in Earth science, as it ensures the accuracy and reliability of information used for monitoring and predicting environmental phenomena. By understanding these errors, scientists can refine data interpretation, enhance models, and ultimately make better-informed decisions about the Earth's complex systems.

Read this project

Developing Algorithms to Improve the Temporal Sampling of Satellite Data

Enhancing the temporal repeat of satellite data for obtaining soil moisture information is a vital research area due to its implications for agriculture, water resource management, climate change research, and ecosystem health. It helps in making informed decisions, increasing productivity, and reducing the impact of natural disasters, as well as contributing to our understanding of the global climate system.

Read this project

Exploring the Impact of Human Activities on the Subdaily Global Terrestrial Water Cycle

Humans have been modifying the Earth's surface for thousands of years, with practices like clearing forests for agriculture and creating uniform land covers. But how do these changes impact the subdaily global terrestrial water cycle? That's the question a project aims to answer.

Read this project

Satellite Image Disaggregation with Machine Learning

Microwave soil moisture data is critical for agriculture, weather, and climate modeling, but has low spatial resolution. Disaggregation via machine learning can improve resolution, offering detailed local soil moisture data. Machine learning can handle complex relationships between microwave signals and soil moisture.

Read this project

Multi-Scale Soil Moisture Monitoring Using ELBARA-III and Drone Radiometer

The Portable L-Band Radiometer, mounted on a drone, enables high-resolution soil moisture retrieval across diverse terrains. Operating at 1.4 GHz (L-band), it collects brightness temperature (TB) data over rice paddies and varied vegetation zones, complementing satellite missions like SMAP and SMOS. By integrating airborne observations with ground-based sensors, the system enhances spatial coverage and improves soil moisture retrieval models for climate and agricultural applications. This drone-based approach significantly increases data accuracy and efficiency compared to traditional methods.

Read this project

Developing the First Long-Term Soil Moisture and Brightness Temperature Measurement Site in South Korea Using L-Band Radiometers and Drones

Soil moisture (SM) is vital for hydrometeorology and agriculture, yet its retrieval faces challenges. Satellite-based methods (e.g., SMAP, SMOS) offer global coverage but have resolution limitations, model-based methods lack accuracy in extreme events, and ground-based observations provide precision but limited coverage. To improve satellite SM validation, this project establishes Korea’s first Core Validation Site (CVS) in Hampyeong-gun and Naju-si, deploying TEROS 12-6 sensors across 9 km, 3 km, and 1 km grids for continuous SM and temperature monitoring. The ESA ELBARA-III L-Band Radiometer and drone-mounted PoLRa will collect brightness temperature (TB) data, aiding SM retrieval model development with soil properties and climate data. This CVS will enhance climate resilience studies, SM monitoring, and satellite validation in Korea.

Read this project

AI-Based Water Quality Prediction for Inland Water using Satellite and Land Surface Model Data

Climate change-driven heatwaves and water cycle disruptions threaten inland water quality (WQ), necessitating efficient monitoring. Traditional methods are labor-intensive with limited coverage, prompting us to develop an AI-based model for predicting chlorophyll-a concentrations in lakes and rivers. By integrating high-resolution satellite imagery (Landsat-8/9, Sentinel-2/3) with land surface models (ERA5-Land, GLDAS, MERRA-2), our model tailors predictions to different water bodies. Future plans include incorporating socio-statistical data (e.g., population, livestock) and climate scenarios (RCP, SSP). Using AI, GIS, and high-performance computing, we explore low-concentration chlorophyll-a prediction, precipitation and flow speed impacts on WQ, transfer learning, multi-sensor data fusion, and uncertainty quantification. Beyond chlorophyll-a, we aim to extend predictions to turbidity and dissolved oxygen, providing a comprehensive AI-driven approach to monitoring and mitigating climate change effects on inland water quality.

Read this project

Harnessing Deep Learning to Predict and Decode the Mysteries of Flash Droughts (GAN/SHAP/3D-CNN with Transfer Learning)

The application of deep learning in predicting flash droughts offers a transformative approach to understanding and anticipating these rapid-onset events, significantly enhancing preparedness and response strategies. By unraveling the complex mechanisms behind flash droughts, this project aims to provide precise, timely forecasts, thereby mitigating the severe agricultural, ecological, and socioeconomic impacts associated with these phenomena.

Read this project

Enhancing Earth Science Predictions through Advanced Data Assimilation Techniques

Data assimilation is vital in earth science as it integrates diverse observations and model simulations, improving the accuracy of forecasts and predictions. This process enhances our understanding of complex Earth systems, enabling better decision-making for environmental management and climate adaptation.

Read this project

Data Error Characterizations

Characterizing the error of satellite data and land surface models is vital in Earth science, as it ensures the accuracy and reliability of information used for monitoring and predicting environmental phenomena. By understanding these errors, scientists can refine data interpretation, enhance models, and ultimately make better-informed decisions about the Earth's complex systems.

Read this project

Developing Algorithms to Improve the Temporal Sampling of Satellite Data

Enhancing the temporal repeat of satellite data for obtaining soil moisture information is a vital research area due to its implications for agriculture, water resource management, climate change research, and ecosystem health. It helps in making informed decisions, increasing productivity, and reducing the impact of natural disasters, as well as contributing to our understanding of the global climate system.

Read this project

Satellite Image Disaggregation with Machine Learning

Microwave soil moisture data is critical for agriculture, weather, and climate modeling, but has low spatial resolution. Disaggregation via machine learning can improve resolution, offering detailed local soil moisture data. Machine learning can handle complex relationships between microwave signals and soil moisture.

Read this project

Multi-Scale Soil Moisture Monitoring Using ELBARA-III and Drone Radiometer

The Portable L-Band Radiometer, mounted on a drone, enables high-resolution soil moisture retrieval across diverse terrains. Operating at 1.4 GHz (L-band), it collects brightness temperature (TB) data over rice paddies and varied vegetation zones, complementing satellite missions like SMAP and SMOS. By integrating airborne observations with ground-based sensors, the system enhances spatial coverage and improves soil moisture retrieval models for climate and agricultural applications. This drone-based approach significantly increases data accuracy and efficiency compared to traditional methods.

Read this project

Integrating Earth Science and Engineering for Climate Resilience: Innovative Approaches to Infrastructure and Societal Justice

Earth science informs infrastructure development by providing insights into site suitability, resource management, and sustainable design, enhancing the resilience and long-term viability of projects. It also plays a crucial role in addressing societal justice related to climate change by helping identify vulnerable communities and develop mitigation strategies, ensuring equitable access to resources and protection from environmental hazards.

Read this project

Enhancing Earth Science Predictions through Advanced Data Assimilation Techniques

Data assimilation is vital in earth science as it integrates diverse observations and model simulations, improving the accuracy of forecasts and predictions. This process enhances our understanding of complex Earth systems, enabling better decision-making for environmental management and climate adaptation.

Read this project

Floods and Droughts Predictions using Machine Learning Approaches

Satellite data and machine learning transformed Earth science by predicting and monitoring natural disasters. This combination delivers precise and timely predictions, crucial for mitigating the impacts of events like floods and droughts.

Read this project

Exploring the Impact of Human Activities on the Subdaily Global Terrestrial Water Cycle

Humans have been modifying the Earth's surface for thousands of years, with practices like clearing forests for agriculture and creating uniform land covers. But how do these changes impact the subdaily global terrestrial water cycle? That's the question a project aims to answer.

Read this project

AI-Based Water Quality Prediction for Inland Water using Satellite and Land Surface Model Data

Climate change-driven heatwaves and water cycle disruptions threaten inland water quality (WQ), necessitating efficient monitoring. Traditional methods are labor-intensive with limited coverage, prompting us to develop an AI-based model for predicting chlorophyll-a concentrations in lakes and rivers. By integrating high-resolution satellite imagery (Landsat-8/9, Sentinel-2/3) with land surface models (ERA5-Land, GLDAS, MERRA-2), our model tailors predictions to different water bodies. Future plans include incorporating socio-statistical data (e.g., population, livestock) and climate scenarios (RCP, SSP). Using AI, GIS, and high-performance computing, we explore low-concentration chlorophyll-a prediction, precipitation and flow speed impacts on WQ, transfer learning, multi-sensor data fusion, and uncertainty quantification. Beyond chlorophyll-a, we aim to extend predictions to turbidity and dissolved oxygen, providing a comprehensive AI-driven approach to monitoring and mitigating climate change effects on inland water quality.

Read this project

Harnessing Deep Learning to Predict and Decode the Mysteries of Flash Droughts (GAN/SHAP/3D-CNN with Transfer Learning)

The application of deep learning in predicting flash droughts offers a transformative approach to understanding and anticipating these rapid-onset events, significantly enhancing preparedness and response strategies. By unraveling the complex mechanisms behind flash droughts, this project aims to provide precise, timely forecasts, thereby mitigating the severe agricultural, ecological, and socioeconomic impacts associated with these phenomena.

Read this project

Streamflow and Drought Predictions over Ungaged Regions using Deep and Transfer Learning Approaches

Streamflow and flash drought predictions are essential for managing water resources and mitigating potential disasters in ungaged regions. With remotely-sensed data, deep and transfer learning approaches provide powerful tools to analyze complex hydrological data, enabling more accurate predictions and better decision-making in these areas.

Read this project

Applications of Bayesian Machine Learning in Big Data in Earth Science

Bayesian methods help us improve our guesses by using new information. In Earth science, these methods are applied to big data to better understand our planet. This approach is useful for predicting things like natural disaster patterns and climate changes. By continuously updating our knowledge with new data, we can make more accurate predictions and decisions in Earth science.

Read this project

Water Balance Budgeting with Bayesian Machine Learning

The water balance equation in Earth science, P = E + R + etc, describes the relationship between precipitation (P), evaporation (E), runoff (R), and etc (e.g., soil moisture, ground water) in a given area. Bayesian inference can be applied to solve this equation by incorporating prior knowledge and updating the probability distributions of the variables based on new data, ultimately improving water resource management and prediction.

Read this project

Integrating Earth Science and Engineering for Climate Resilience: Innovative Approaches to Infrastructure and Societal Justice

Earth science informs infrastructure development by providing insights into site suitability, resource management, and sustainable design, enhancing the resilience and long-term viability of projects. It also plays a crucial role in addressing societal justice related to climate change by helping identify vulnerable communities and develop mitigation strategies, ensuring equitable access to resources and protection from environmental hazards.

Read this project

Floods and Droughts Predictions using Machine Learning Approaches

Satellite data and machine learning transformed Earth science by predicting and monitoring natural disasters. This combination delivers precise and timely predictions, crucial for mitigating the impacts of events like floods and droughts.

Read this project

Data Error Characterizations

Characterizing the error of satellite data and land surface models is vital in Earth science, as it ensures the accuracy and reliability of information used for monitoring and predicting environmental phenomena. By understanding these errors, scientists can refine data interpretation, enhance models, and ultimately make better-informed decisions about the Earth's complex systems.

Read this project

Developing Algorithms to Improve the Temporal Sampling of Satellite Data

Enhancing the temporal repeat of satellite data for obtaining soil moisture information is a vital research area due to its implications for agriculture, water resource management, climate change research, and ecosystem health. It helps in making informed decisions, increasing productivity, and reducing the impact of natural disasters, as well as contributing to our understanding of the global climate system.

Read this project

Exploring the Impact of Human Activities on the Subdaily Global Terrestrial Water Cycle

Humans have been modifying the Earth's surface for thousands of years, with practices like clearing forests for agriculture and creating uniform land covers. But how do these changes impact the subdaily global terrestrial water cycle? That's the question a project aims to answer.

Read this project

Satellite Image Disaggregation with Machine Learning

Microwave soil moisture data is critical for agriculture, weather, and climate modeling, but has low spatial resolution. Disaggregation via machine learning can improve resolution, offering detailed local soil moisture data. Machine learning can handle complex relationships between microwave signals and soil moisture.

Read this project

Contact me

If you have a keen interest in the intersection of climate change and its impact on hydrological research fields, I encourage you to consider pursuing a Master's, PhD, or postdoctoral position. By delving deeper into this critical area of study, you can play an essential role in addressing the world's most pressing environmental challenges and help safeguard our water resources, ecosystems, and communities. Your dedication and expertise can significantly contribute to the development of sustainable solutions and innovative approaches to hydrological research. Embark on this exciting journey and become part of the passionate community of scientists working towards a more resilient and environmentally responsible future.