Publications

Check our peer-reviewed journal papers and conference papers.

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.

Streamflow Estimation in Ungauged Regions using Machine Learning: Quantifying Uncertainties in Geographic Extrapolation

Hydrology and Earth System Sciences (Discussion)

May 1, 2023

In many protected areas and rivers with non-constant flow, there is limited ground data, making it hard to get streamflow information. This study looks at using streamflow data from regions with lots of information (North America, South America, and Western Europe) to help estimate streamflow in areas with less data (South Africa and Central Asia). By using machine learning algorithms trained on climate and catchment attributes from data-rich areas, we found they could effectively estimate monthly streamflow in data-poor regions. This study helps guide the selection of input data and machine learning methods for estimating streamflow in different geographic locations.

Impact of Vegetation Gradient and Land Cover Conditions on Soil Moisture Retrievals from Different Frequencies and Acquisition Times of AMSR2

IEEE Transactions on Geoscience and Remote Sensing

April 1, 2023

Estimating soil moisture from space using various microwave wavelengths is essential for predicting natural disasters and analyzing the Earth's water cycle. This study examines how well space-based technology can measure soil moisture (SM) and how it performs in different environments. It found that AMSR2 C-band products work better in areas with more vegetation, while X-band products are less effective. In areas with little vegetation, all AMSR2 products have weaker performance because of their limitations in detecting moisture in dry soil. The study also found that daytime measurements work better in areas with less vegetation, while nighttime measurements are more effective in densely vegetated areas. By using different products based on their strengths and weaknesses, researchers can improve the accuracy of soil moisture measurements, but this may result in reduced coverage of the area being studied.

Performance Assessment of SM2RAIN-NWF using ASCAT Soil Moisture via Supervised Land Cover-Soil-Climate Classification

Remote Sensing of Environment

February 1, 2023

Estimating precipitation from space using microwave satellite systems is essential for managing water resources, predicting natural disasters, and analyzing the Earth's water cycle. This study compares two algorithms, SM2RAIN and SM2RAIN-NWF, for estimating rainfall using soil moisture data. The newer SM2RAIN-NWF algorithm offers improved results by combining SM2RAIN with a net water flux model. We found that SM2RAIN-NWF performed better than SM2RAIN, especially in arid and semi-arid regions. The study also discovered that drainage played a crucial role in improving rainfall estimates, while evapotranspiration had a minimal impact.

* = 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

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
accepted

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

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
-
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
Under Review

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

Zanpin Xing, Xiaojun Li, Lei Fan, Frédéric Frappart, Hyunglok Kim, Karthikeyan Lanka, Preethi Konkathi, Yuqing Liu, Lin Zhao and Jean-Pierre Wigneron
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minor revision

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
IAHS2022
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.