Publications

Check our peer-reviewed journal papers and conference papers.

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

November 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.

A comprehensive Assessment of SM2RAIN-NWF using ASCAT and A Combination of ASCAT and SMAP Soil Moisture Products for Rainfall Estimation

Science of The Total Environment

September 1, 2022

Rainfall estimation using remote sensing technology offers a more accurate alternative to traditional measurement methods due to its high resolution in both time and space. The SMA2RAIN-NWF algorithm, an improved version of the original SM2RAIN algorithm, uses satellite soil moisture data to estimate rainfall. This study aims to evaluate the effectiveness of SMA2RAIN-NWF using multiple soil moisture products and different aggregation periods. The results show that the algorithm performs better as the aggregation levels increase and that it is more effective in urban areas. Overall, the SMA2RAIN-NWF algorithm demonstrates improved performance compared to the original SM2RAIN algorithm.

* = mentored by Dr. Kim

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

G. Pavur, H. Kim*, B. Fang, V. Lakshmi
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Major Revision

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

M.H. Le, H. Kim*, S. Adam, H. X. Do, P.A. Beling, V. Lakshmi
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Major Revision

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

R. Zhang, S. Kim, H. Kim, A. Sharma, B. Fang, V. Lakshmi
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Major Revision

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

Wade T. Crow, Hyunglok Kim, and Sujay Kumar
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Under Review

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

Hyunglok Kim and Wade T. Crow
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Under Review

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
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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.