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Welcome to WreBigDL@Kyung Hee University!
Leading Research in Earth Observation Big Data for Water Resources
Our lab, the Water Resources and Environment Big Data Lab (WreBigDL), focuses on validating, improving, and applying Earth observation big data to address critical challenges in water resource management.
By integrating satellite-based data analytics and big data technologies, we push the boundaries of hydrological research. Our mission is to drive innovative and impactful research that enhances data reliability, paving the way for sustainable management of global water resources in a changing climate.
News
July 2025: PhD student Yaggesh Sharma departed for a Research Practicum at UNSW Sydney Water Research Centre—wishing him success in this international program!
May 2025: Congrats to PhD student Yaggesh Sharma for publishing in Remote Sensing!
Jan 2025: New paper published in Environ. Model. Softw. with Drs. Yoon, Marshall (Macquarie), and Sharma (UNSW)!
Jan 2025: Welcome to our new undergraduate research assistants, Sangyoon and Jungmin!
Dec 2024: Congrats to PhD student Yaggesh Sharma for publishing in Groundwater for Sustainable Development!
Nov 2024: Prof. Kim visited NASA GSFC and USDA-ARS to initiate academic collaborations.
Research Highlights
Hydrology Without Gauges:
A Surrogate Model for Streamflow Prediction
Yoon, H.N., Marshall, L., Sharma, A., & Kim, S. (2025). Doing Hydrology when no in-situ data exists: Surrogate River discharge Model (SRM). Environmental Modelling & Software, 186, 106334.
Advanced Monitoring of Complex Water Bodies:
The Measurement of Reservoir Level from Altimetry (MoRLa)
Han, K., Kim, S., Mehrotra, R., & Sharma, A. (2024). Enhanced water level monitoring for small and complex inland water bodies using multi-satellite remote sensing. Environmental Modelling & Software, 180, 106169.
Water Balance Approach for filling gaps in satellite-based soil moisture data
Zhang, R., Kim, S., Kim, H., Fang, B., Sharma, A., & Lakshmi, V. (2023). Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting, Water Resour. Res., 59(12), e2023WR034457.
SNR-opt: a new data merging method outperforming the existing Triple Collocation-based weighted averaging
Kim S., Sharma A., Liu Y., Young I. S. (2022). Rethinking Satellite Data Merging: From Averaging to SNR Optimization, IEEE Trans. Geosci. Remote Sens., 60, 1–15.
Five Key Peer-Reviewed Journal Contributions
Yoon, H.N., Marshall, L., Sharma, A., & Kim, S. (2025). Doing Hydrology when no in-situ data exists: Surrogate River discharge Model (SRM). Environmental Modelling & Software, 186, 106334.
Sharma, Y. K., Kim, S., Tayerani Charmchi, A. S., Kang, D., & Batelaan, O. (2025). Strategic Imputation of Groundwater Data Using Machine Learning: Insights from Diverse Aquifers in the Chao-Phraya River Basin. Groundwater for Sustainable Development, 28, 101394.
Shah, S., Liu, Y., Kim, S., & Sharma, A. (2024). Advancing High-Mountain Precipitation Reconstruction through Merging of Multiple Data Sources: Triple Collocation versus Signal-to-Noise Ratio Optimization. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-15.
Han, K., Kim, S., Mehrotra, R., & Sharma, A. (2024). Enhanced Water Level Monitoring for Small and Complex Inland Water Bodies Using Multi-Satellite Remote Sensing. Environmental Modelling & Software, 106169.
Kim, S., Wasko, C., Sharma, A., & Nathan, R. (2024). The role of regional water vapor dynamics in creating precipitation extremes. Journal of Hydrology X, 24, 100181.
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