Publications

Check our peer-reviewed journal papers and conference papers.

Improving Weather Forecast Skill of the Korean Integrated Model (KIM) by Assimilating SMAP Soil Moisture Anomalies

Quarterly Journal of the Royal Meteorological Society

November 1, 2024

This study examines the impact of assimilating Soil Moisture Active Passive (SMAP) data into the Korean Integrated Model (KIM) to improve global soil moisture estimates and weather forecasts. SMAP soil moisture retrievals are integrated into the Noah land surface model using the ensemble Kalman filter through NASA’s Land Information System. Experiments from March to July 2022 show that assimilating SMAP data, particularly with anomaly-based bias correction, significantly enhances soil moisture estimates and improves weather forecasts, especially in northern Africa and West

A Global Scale Analysis of Subsurface Scattering Signals Impacting ASCAT Soil Moisture Retrievals

IEEE Transactions on Geoscience and Remote Sensing

August 31, 2024

This research discusses the retrieval and validation of soil moisture data from the Advanced Scatterometer (ASCAT), focusing on its applications and challenges. ASCAT data, crucial for various uses like weather prediction and drought monitoring, are compared with similar data from other satellite missions. Validations using multiple datasets highlight dependencies on land cover and vegetation, revealing unexpected quality variations across different environments. A notable issue identified is subsurface scattering, often misattributed to other factors, impacting ASCAT data quality significantly. The study recommends masking affected data using developed indicators and masks to enhance accuracy in practical applications.

Seasonal-scale intercomparison of SMAP and fused SMOS-SMAP soil moisture products

Frontiers in Remote Sensing

August 30, 2024

This study evaluates the performance of two L-band passive microwave satellite sensors, the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), in monitoring surface soil moisture (SM). The newly developed fused SM product (Fused-IB) derived from SMOS and SMAP observations is compared with the enhanced SMAP-L3 (SMAP-E) SM product against in situ SM data from the International Soil Moisture Network (ISMN) over the period 2016-2020. The comparison considers overall and seasonal performance, focusing on different land use and land cover (LULC) types. Results show that Fused-IB generally outperforms SMAP-E, especially in forested areas, due to the robust SMAP-IB algorithm and higher data coverage. Both products demonstrate higher accuracy in summer and autumn, but face increased uncertainties in forests, grasslands, and croplands during spring and winter due to vegetation growth and rainfall. This study highlights the importance of accounting for seasonal and eco-hydrological factors to improve the accuracy of SM retrieval algorithms.

Evaluation of a Combined Drought Indicator against Crop Yield Estimations and Simulations over the Argentine Humid Pampas

Theoretical and Applied Climatology

July 1, 2024

• The study evaluated the Combined Drought Indicator (CDI) in the Argentine Humid Pampas, focusing on its ability to track and characterize drought severity using precipitation deficits, soil moisture, and vegetation health anomalies.

• Strong spatial and temporal correlations were found among these variables, with the highest correlations observed for time lags of 0, 10, and 20 days, effectively capturing major drought events.

• The CDI aligned well with soybean and corn yield estimations and simulations, as well as official agricultural emergency declarations, demonstrating its effectiveness in representing drought impacts on agriculture.

• The study analyzed two significant drought events: the gradual and long-lasting 2008-2009 drought and the rapid-onset 2017-2018 flash drought, both of which severely affected crop yields and were well-represented by the CDI.

• Suggestions for improving the CDI include enhancing the temporal resolution of precipitation data and refining spatial resolution to better detect and monitor drought-affected regions, emphasizing the importance of collaborative efforts for advancing drought early warning systems.

Utility of Publicly Availability Stream Gauges Datasets and Deep Learning in Predicting Monthly Basin-scale Runoff in Ungauged Regions

Advances in Water Resources

June 1, 2024

•This study tackles the challenge of unbalanced stream gauge distributions by leveraging publicly available datasets and deep learning to enhance hydrological modeling in under-gauged regions.

•This study introduces a novel framework combining LSTM-based deep learning with GLDAS and GSIM data for effective runoff prediction across continents.

•This study assesses the sensitivity of LSTM models using data from diverse regions, emphasizing the importance of hydrological similarities for accurate runoff predictions in ungauged basins.

•LSTM models demonstrate superior runoff prediction skills over traditional GLDAS datasets, influenced by the hydrological similarities between training and test regions.

Intercomparison and Combination of Inland Water Observations from CYGNSS, MODIS, Landsat, and High-Resolution Commercial Satellite Imagery,

Geoscience Letters

February 1, 2024

Accurate and timely information about the extent and location of inland water bodies is crucial for various tasks related to managing water resources. A promising tool for identifying these water bodies is the Cyclone Global Navigation Satellite System (CYGNSS). CYGNSS uses eight microsatellites with special radar technology to observe surface reflectivity characteristics over both dry and wet land. This study compares data from CYGNSS with data from two other Earth observation systems—Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Global Surface Water product—for the contiguous United States.

We conducted a 1-kilometer comparison of water masks (representations of where water is located) for the year 2019, focusing on the area between latitudes 24°N and 37°N. To assess how well these water masks performed, we used statistical measurements called confusion matrices and high-resolution satellite images.

By using specific thresholds for different sub-regions observed by CYGNSS, we improved the performance of our water mask data, with up to a 34% increase in a measurement called the F1-score. We also calculated a metric that compares the amount of inland water to the size of the surrounding area, and found that CYGNSS, MODIS, and Landsat provided very similar estimates, differing by less than 2.3% for each sub-region.

In summary, this study sheds light on how well water masks derived from optical (visible light) and radar-based satellite observations compare in terms of their accuracy and spatial representation of inland water bodies.

* = mentored by Dr. Kim

Changes in the Speed of the Global Terrestrial Water Cycle Due To Human Interventions

Hyunglok Kim, Wade T. Crow, and Venkataraman Lakshmi
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Under Preperation

Exceeding 60% precipitation transformed into terrestrial water storage in global river basins

Baoming Tian, Yulong Zhong, Hyunglok Kim, Xing Yuan, Xinyue Liu, Enda Zhu, Yunlong Wu, Lizhe Wang
Communications Earth & Environment
Under Review

Developing Independent CYGNSS Soil Moisture Retrieval Algorithm with Mitigated Vegetation Effects: Incorporating a Two-Step and Relative SNR Approaches

Ziyue Zhu, Hyunglok Kim*, Venkataraman Lakshmi
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Under Preperation

A Novel Soil Moisture Validation Method Utilizing Brightness Temperature

Ziyue Zhu, Runze Zhang, Bin Fang, Hyunglok Kim, Venkataraman Lakshmi
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Major Revision

Observational Analysis of Long-term Streamflow Response to Flash Drought in the Mississippi River Basin

Sophia Bakar, Hyunglok Kim, Venkataraman Lakshmi
Weather and Climate Extremes
Major Reivison

From Theory to Hydrological Practice: Leveraging CYGNSS Data Over Seven Years for Advanced Soil Moisture Monitoring

Hoang Hai Nguyen, Hyunglok Kim*, Wade Crow, Wolfgang Wagner, Simon Yueh, Fangni Lei, Jean-Pierre Wigneron, Andreas Colliander, Frédéric Frappart
Remote Sensing of Environment
Major Revision

Towards Self-calibration of Rainfall Estimation through Soil Dynamics and its Signals Using Supervised and Unsupervised Machine Learning Clustering Methods over CONUS

Mohammad Saeedi, Hyunglok Kim, and Venkataraman Lakshmi
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under review

A Stand-Alone Framework for Predicting Spatiotemporal Errors in Satellite-Based Soil Moisture Using Tree-Based Models and Deep Neural Networks

Subin Kim, Hai Nguyen, Yonghwan Kwon, Hyunglok Kim
GIScience & Remote Sensing
under review

Deep Learning and Bayesian Inference via Samplings and Variational Approximations to Characterize Spatially Continuous Global-scale Satellite-based Soil Moisture Error Patterns

H. Kim, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2021

Impact of Climate Change on Road Networks: Travel Demand, Machine Learning, and Flooding Simulation Models

S. Ryu, H. Kim, E. Cho, R. Zhang
US-KOREA Conference on Science, Technology, and Entrepreneurship
January 1, 2021

Leveraging Soil Moisture for Early Flood Detection

V. Sunkara, C. Doyle, H. Kim, B. Tellman, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2020

Detecting Inland Waterbodies Using GNSS-R Data: Intercomparison of Previous Methods and a New Machine Learning Approach

G. Pavur, H. Kim, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2020

An Integrated Framework to Predict Peak Flood and Map Inundation Areas in the Chesapeake Bay Using Machine Learning Methods with High-Resolution Lidar DEM and Satellite Data

R. Zhang, H. Kim, L. Band, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2020

Error Characteristic Assessments of Soil Moisture Estimates from Satellites and Land Surface Models: Focusing on Forested and Irrigated Regions

H. Kim, J. Wigneron, S.V. Kumar, J. Dong, W. Wagner, M.H. Cosh, D.D. Bosch, C.H. Collins, P.J. Starks, M.S. Seyfried, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2020

Producing Satellite-based Diurnal Time-scale Soil Moisture Retrievals using Existing Microwave Satellites and GNSS-R Data

H. Kim, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2020

Assimilation of SMAP-enhanced and SMAP/Sentinel-1A/B soil moisture data into land surface models

H. Kim, V. Lakshmi, S. Kumar, Y. Kwon
European Geosciences Union, General Assembly Conference
December 1, 2020

Assimilation of GPS soil moisture data from CYGNSS into land surface models

H. Kim, Y. Kwon, S.V. Kumar, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2019

The Impact of Irrigation on the Water Cycle in the Continental United States (CONUS)

H. Kim, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2018

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.