Check our peer-reviewed journal papers and conference papers.

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.

Systematic modelling errors undermine the application of land data assimilation systems for hydrological and weather forecasting

Journal of Hydrometeorology

January 1, 2024

Recent advancements in land data systems and satellite soil moisture data show that using this data can help improve surface condition estimates in land models. However, this improvement in estimating soil moisture doesn't necessarily translate to better predictions of water movements like evaporation and runoff. This issue might be due to inaccuracies in how these models link soil moisture with water movements. Experiments suggest that even with better soil moisture data, these inaccuracies can lead to poor water movement predictions. Adjusting satellite data to fit these models isn't always effective and could sometimes worsen predictions. This highlights the need for more accurate models that can better link soil moisture with water movements.

Interpreting effective hydrologic depth estimates derived from soil moisture remote sensing: A Bayesian non-linear modeling approach

Science of The Total Environment

December 31, 2023

The Water Balance Equation (WBE) is like a budget for water in a specific area, tracking how much water comes in, goes out, and how much is stored over time. It's crucial in understanding water availability and the water cycle, especially on land. The WBE relates rainfall, evaporation, and runoff to changes in soil moisture.

Recently, there's been interest in using satellite data to guess some WBE parts, but this can be complex. Challenges include simplifications in the equation, missing water cycle elements, and limits of satellite data. Our study used advanced modeling to show these issues can affect the reliability of WBE estimates. We suggest treating these estimates as general guides, reflecting the satellite data used, rather than exact measurements. Better remote sensing and improved WBE will enhance our understanding of the water cycle.

Temporal Gap-filling of 12-hourly SMAP Soil Moisture over the CONUS using Water Balance Budgeting

Water Resources Research

December 31, 2023

The use of satellite-based soil moisture (SM) data in Earth system science can be hindered by gaps in observations. To address this, our study introduces a method to fill in these gaps in the Soil Moisture Active Passive (SMAP) dataset. We utilized a basic water balance equation, considering factors like precipitation and soil water loss, to estimate soil water content over 12-hour periods. This led to the development of a new, continuous SM product for the contiguous United States. This product, named the SMAP-based 12-hourly SM product, showed promising results, aligning well with on-the-ground measurements and effectively capturing soil moisture variations, especially those related to heavy rainfall events. This continuous dataset, with its detailed insights into soil water dynamics, offers valuable contributions to our understanding of land-surface hydrology.

True Global Error Maps for SMAP, SMOS, and ASCAT Soil Moisture Data Based on Machine Learning and Triple Collocation Analysis Remote Sensing of Environment

Remote Sensing of Environment

December 1, 2023

This study aims to address gaps in assessing the accuracy of satellite-based soil moisture data by utilizing machine learning techniques to generate spatially complete error maps for Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT) systems, and by examining the influence of environmental conditions on satellite-based soil moisture retrievals, revealing that a significant portion of missing error information from triple collocation analysis (TCA) can be reconstructed using ensemble prediction mean of machine learning models, contributing to a more comprehensive understanding of soil moisture dynamics across the three satellite missions.

A Bayesian Machine Learning Method to Explain the Error Characteristics of Global-Scale Soil Moisture Products

Remote Sensing of Environment

August 1, 2023

Accurately estimating soil moisture from satellite data is crucial for various Earth science disciplines. This study introduces a Bayesian approach to analyze error characteristics in widely used satellite-derived soil moisture data. By applying Bayesian hierarchical modeling and triple collocation analysis, the study examines the influence of environmental factors and human activities on data accuracy. The findings highlight the adaptability and potential of Bayesian modeling for sensitivity analysis in remote sensing research. The study also identifies factors like irrigation, vegetation, and retrieval algorithm assumptions as sources of errors. It emphasizes the need to consider multiple factors when assessing data quality. Overall, the research provides a valuable framework for investigating error characteristics in satellite-based soil moisture data.

* = 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
Under Preperation

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

Wolfgang Wagner, Roland Lindorfer, Sebastian Hahn, Hyunglok Kim, Mariette Vreugdenhil, Alexander Gruber, Milan Fischer, and Miroslav Trnka
IEEE Transactions on Geoscience and Remote Sensing
Major Revision

How Much 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
Nature Communications
Under Review

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

M.H. Le, H. Kim*, S. Adam, H. X. Do, P.A. Beling, V. Lakshmi
Advances in Water Resources
Recently Accepted

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

Yonghwan Kwon, Sanghee Jun, Eunkyu Kim, Kyung-Hee Seol, Seokmin Hong, In-Hyuk Kwon, Hyunglok Kim
Quarterly Journal of the Royal Meteorological Society
Major Revision

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
Under Preperation

A Novel Soil Moisture Validation Method Utilizing Brightness Temperature

Ziyue Zhu, Runze Zhang, Bin Fang, Hyunglok Kim, Venkataraman Lakshmi
Under Preperation

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

Spennemann Pablo, Gustavo Naumann, Mercedes Peretti, Carmelo Cammalleri, Mercedes Salvia, Alessio Bocco, Maria Elena Fernández Long, Martin Maas, Hyunglok Kim, Manh-Hung Le, John D. Bolten, Andrea Toreti and Venkataraman Lakshmi
Agricultural and Forest Meteorology
Under Review

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
Under Review

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
Under Review

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

Jean-Pierre Wigneron et al.
Under Preperation

Systematic Modeling Errors Strongly Undermine The Value Of Land Data Assimilation Systems And Microwave Remote Sensing For Water Flux Estimation

W. T. Crow, H. Kim , S. Kumar
IEEE International Geoscience and Remote Sensing Symposium
July 21, 2023

Utilizing Bayesian Machine Learning for Analyzing Error Patterns in Global-Scale Soil Moisture Data

H. Kim , W. T. Crow, W. Wagner, X. Li, V. Lakshmi
Hydrology Machine Learning (HydroML) Symposium, Phase 2 at Berkeley Lab
May 1, 2023

Uncertainty Analysis Framework in the Water Balance Equation Using Bayesian Statistical Modeling Approach

H. Kim, W. Crow
American Geophysical Union, Fall Meeting
December 1, 2022

Retrieving Runoff in Ungauged Basins using Satellite Observations of Rainfall and Soil Moisture

H. Kim, W. Crow
American Geophysical Union, Fall Meeting
December 1, 2022

Changes in Extreme Precipitation Patterns in the Meuse River Basin as a Driver of the July 2021 Flooding

B. Goffin, P. Kansara, H. Kim, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2022

Reconstruction of the SMAP-based 12-hourly soil moisture product over the CONUS through water balance budgeting

R. Zhang, S. Kim, H. Kim, B. Fang, A. Sharma, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2022

Hydrological flash drought forecasting using meteorological flash drought indices and machine learning approaches – A case study in the Mississippi River Basin

S. Bakar, D. Quintero, M. Le, H. Kim, S. S. Adams, P. Beling, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2022

Global downscaling and assimilation of soil moisture

V. Lakshmi, B. Fang, H Kim
March 1, 2022

Impact of Land Use Land Cover Changes on Carbon and Water Cycle Interactions: Using Data Driven Modeling and Satellite Products

M. Umair, S. Khan, H. Kim, M. Azmat, S. Atif
American Geophysical Union, Fall Meeting
December 1, 2021

Water Cycle in Different Time Scales: Analyzing the Impact of Human-driven Changes in Land Cover using Bayesian Inferences and Data Assimilation Methods

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

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.