Hyunglok Kim bio photo

Hyunglok Kim

Terrestrial Water Cycle
Hydrology
Remote Sensing
Bayesian Inference
Machine Learning
Deep Learning

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Publications

Publications

A list is also available Google Scholar Page

Underlined = corresponding author.

Peer-Reviewed Journal Papers

(in review/revision)

2022 [1] Impact of Vegetation Gradient and Land Cover Condition on Soil Moisture Retrievals from Different Frequencies and Acquisition Times of Satellite Observations, IEEE Transactions on Geoscience and Remote Sensing

2022 [2] Changes in the speed of the subdaily global terrestrial water cycle due to human intervention

2022 [3] Performance assessment of SM2RAIN-NWF using ASCAT soil moisture via supervised land cover-soil-climate classification, Remote Sensing of Environment

Peer-Reviewed Papers

2022 [22] A comprehensive assessment of SM2RAIN-NWF using ASCAT and a combination of ASCAT and SMAP soil moisture products for rainfall estimation, M. Saeedi, H. Kim, S. Nabaeia, L. Brocca, V. Lakshmi, H. Mosaffac [ in-press ]
Science of The Total Environment

2022 [21] S. Lee, J. Qi, G. McCarty, M. Anderson, Y. Yang, X. Zhang, G. Moglen, D. Kwak, H. Kim, V. Lakshmi, S. Kim, Combined use of crop yield statistics and remotely sensed products for enhanced simulations of evapotranspiration within an agricultural watershed [link]
Agricultural Water Management

2021 [20] H. Kim, V. Lakshmi, Y. Kwon, and S. Kumar, First attempt of global-scale assimilation of subdaily scale soil moisture estimates from CYGNSS and SMAP into a land surface model [PDF]
Environmental Research Letters

2021 [19] S. Lee, J. Qi, H. Kim, G. McCarty, G. Moglen, M. Anderson, X. Zhang, and L. Du, Utility of Remotely Sensed Evapotranspiration Products to Assess an Improved Model Structure [PDF]
Sustainability

2020 [18] H. Kim, J. Wigneron, S. Kumar, J. Dong, W. Wagner, M. Cosh, D. Bosch, C. Collins, P. Starks, M. Seyfried, and V. Lakshmi, Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions [PDF]
Remote Sensing of Environment

2020 [17] H. Kim, S. Lee, M. Cosh, V. Lakshmi, Y. Kwon, and G. McCarty, Assessment and Combination of SMAP and Sentinel-1A/B-Derived Soil Moisture Estimates With Land Surface Model Outputs in the Mid-Atlantic Coastal Plain, USA [PDF]
IEEE Transactions on Geoscience and Remote Sensing

2020 [16] M. Le, H. Kim, H. Moon, R. Zhang, V. Lakshmi, and L. Nguyen, Assessment of drought conditions over Vietnam using standardized precipitation evapotranspiration index, MERRA-2 re-analysis, and dynamic land cover [PDF]
Journal of Hydrology: Regional Studies

2019 [15] H. Kim, M. Cosh, R. Bindlish, and V. Lakshmi, Field evaluation of portable soil water content sensors in a sandy loam [PDF]
Vadose Zone Journal

2019 [14] H. Kim and V. Lakshmi, Global dynamics of stored precipitation water in the topsoil layer from satellite and reanalysis data [PDF]
Water Resources Research

2019 [13] S. Parajuli, G. Stenchikov, A. Ukhov, H. Kim, Dust emission modeling using a new high‐resolution dust source function in WRF‐Chem with implications for air quality [PDF]
Journal of Geophysical Research: Atmospheres

2019 [12] M. Zohaib, H. Kim, M. Choi, Detecting global irrigated areas by using satellite and reanalysis products [PDF]
Science of The Total Environment

2018 [11] H. Kim and V. Lakshmi, Use of Cyclone Global Navigation Satellite System (CYGNSS) observations for estimation of soil moisture [PDF]
Geophysical Research Letters

2018 [10] H. Kim, R. Parinussa, A. Konings, W. Wagner, M. Cosh, V. Lakshmi, M. Zohaib, and M. Choi, Global-scale Assessment and Combination of SMAP with ASCAT (Active) and AMSR2 (Passive) Soil Moisture Products [PDF]
Remote Sensing of Environment

2018 [9] D. Kim, H. Moon, H. Kim, J. Im, and M. Choi, Intercomparison of Downscaling Techniques for Satellite Soil Moisture Products [PDF]
Advances in Meteorology

2017 [8] H. Kim, Z. Muhammad, E. Cho, Y. Kerr, and M. Choi, Development and Assessment of the Sand Dust Prediction Model by Utilizing Microwave Satellite Soil Moisture and Reanalysis Datasets in East Asian Desert Areas [PDF]
Advances in Meteorology

2017 [7] H. Nguyen, H. Kim, M. Choi, Evaluation of the soil water content using cosmic-ray neutron probe in a heterogeneous monsoon climate-dominated region [PDF]
Advances in Water Resources

2017 [6] M. Zohaib, H. Kim, and M. Choi, Evaluating the Patterns of Spatiotemporal Trends of Root Zone Soil Moisture in Major Climate Regions in East Asia [PDF]
Journal of Geophysical Research: Atmospheres

2017 [5] M. Choi, Q. Mu, H. Kim, K. Hwang, and J. Hur, Ecosystem-dynamics link to hydrologic variations for different land-cover types [PDF]
Terrestrial Atmospheric and Oceanic Sciences

2017 [4] E. Cho, C. Su, D. Ryu, H. Kim, and M. Choi, Does AMSR2 produce better soil moisture retrievals than AMSR-E over Australia? [PDF]
Remote Sensing of Environment

2016 [3] D. Kim, J. Lee, H. Kim, and M. Choi, Spatial composition of AMSR2 soil moisture products by conditional merging technique with ground soil moisture data [PDF]
Stochastic Environmental Research and Risk Assessment

2015 [2] H. Kim, and M. Choi. “Impact of soil moisture on dust outbreaks in East Asia: Using satellite and assimilation data [PDF]
Geophysical Research Letters

2014 [1] Y. Jung, H. Kim, J. Baek, and M. Choi, Rain Gauge Network Evaluations using Spatiotemporal Correlation Structures for Semi-mountainous Regions [PDF]
Terrestrial, Atmospheric and Oceanic Sciences

Conference Papers

2021 [13] R. Zhang, H. Kim, E. Lien, D. Zheng, L. Band, and V. Lakshmi, Deep Learning Approach to Predict Peak Floods and Evaluation of Socioeconomic Vulnerability to Flood Events: A Case Study in Baltimore, MD, IEEE SIEDS [PDF]

2020 [12] H. Kim and V. Lakshmi, Producing Satellite-based Diurnal Time-scale Soil Moisture Retrievals using Existing Microwave Satellites and GNSS-R Data (invited),
AGU Fall Meeting

2020 [11] H. Kim, J. Wigneron, S. Kumar, J. Dong, W. Wagner, M. Cosh, D. Bosch, C. Collins, P. Starks, M. Seyfried, and V. Lakshmi, Error Characteristic Assessments of Soil Moisture Estimates from Satellites and Land Surface Models: Focusing on Forested and Irrigated Regions,
AGU Fall Meeting

2020 [10] R. Zhang, H. Kim, L. Band, V. Lakshmi, 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,
AGU Fall Meeting

2020 [9] V. Sunkara, C. Doyle, H. Kim, B. Tellman, and V. Lakshmi, Leveraging Soil Moisture for Early Flood Detection,
AGU Fall Meeting

2020 [8] G. Pavur, H. Kim, and V. Lakshmi, Detecting Inland Waterbodies Using GNSS-R Data: Intercomparison of Previous Methods and a New Machine Learning Approach,
AGU Fall Meeting

2020 [7] H. Kim, V. Lakshmi, S. Kumar, and Y. Kwon, Assimilation of SMAP-enhanced and SMAP/Sentinel-1A/B soil moisture data into land surface models,
EGU General Assembly Conference

2019 [6] H. Kim, Y. Kwon, S. Kumar, and V. Lakshmi, Assimilation of GPS soil moisture data from CYGNSS into land surface models,
AGU Fall Meeting

2018 [5] H. Kim and V. Lakshmi, The Impact of Irrigation on the Water Cycle in the Continental United States (CONUS),
AGU Fall Meeting

2017 [4] H. Kim and V. Lakshmi, Evaluating the Long-term Water Cycle Trends at a Global-scale using Satellite and Assimilation Datasets,
AGU Fall Meeting

2016 [3] H. Kim, R. Parinussa, A. Konings, W. Wagner, M. Cosh, M. Choi, Assessment and Combination of SMAP with ASCAT (Active) and AMSR2 (Passive) Soil Moisture Products: A Case Study in Northeast Asia,
AGU Fall Meeting

2015 [2] H. Kim and M. Choi, Blending and Comparison of Passive and Active Satellite-Based Microwave Soil Moisture Retrievals (ASCAT, MIRAS, AMSR2, FY-3B, and SMAP) with Modeled Simulations (GLDAS) over Different Land Covers in East Asia,
AGU Fall Meeting

2015 [1] H. Kim, E. Cho, and M. Choi, Identifying Vulnerability Regions of Dust Outbreaks in East Asian Desert Areas: using ASCAT, MIRAS, AMSR2, MWRI, MODIS, and GLDAS,
AGU Fall Meeting

Theses

[3] Ph.D. Thesis (TBD)

[2] An Integrated Framework to Predict Peak Flood and Map Inundation Areas Using Machine Learning Methods with High-Resolution Lidar DEM and Satellite Data,
University of Virginia (M.S. in Data Science)

[1] Estimation and Application of Satellite-based Soil Moisture Retrievals: Data Inter-comparison, Fusion, and Application in Natural Disasters,
Sungkyunkwan University (M.S.E. in Water Resources)

Korean Peer-Reviewed Papers

2016 [7] H. Kim, S. Kim, J. Jeong, I. Shin, J. Shin, M. Choi, Revising Passive Satellite-based Soil Moisture Retrievals over East Asia using SMOS (MIRAS) and GCOM-W1 (AMSR2) satellite and GLDAS Assimilation Dataset,
Journal of Wetlands Research (link)

2016 [6] S. Kim, H. Kim, M. Choi, Evaluation of satellite-based soil moisture retrieval over the korean peninsula: using AMSR2 LPRM algorithm and ground measurement data,
Journal of Korea Water Resources Association (link)

2016 [5] L. Li, H. Kim, K. Jun, M. Choi, Estimation of River Discharge using Satellite-derived Flow Signals and Artificial Neural Network Model: Application to Imjin River, Korea Water Resources Association (link)

2016 [4] H. Kim, W. Sunwoo, S. Kim, M. Choi, Construction and estimation of soil moisture site with FDR and COSMIC-ray (SM-FC) sensors for calibration/validation of satellite-based and COSMIC-ray soil moisture products in Sungkyunkwan university, South Korea,
Korea Water Resources Association (link)

2016 [3] M. Choi, H. Kim, S. Kim, and M. Choi, Effects of Morbidity in Korean Peninsula due to Sand Dust using Satellite Aerosol Observations,
Korea Journal of Remote Sensing (link)

2015 [2] H. Kim and M. Choi, An Inter-comparison of Active and Passive Satellite Soil Moisture Products in East Asia for Dust-Outbreak Prediction,
Korean Society of Hazard Mitigation (link)

2015 [1] J. Lim, J. Baik, H. Kim, M. Choi, Estimation of Water Quality using Landsat 8 Images for Geum-river, Korea,
Journal of Korea Water Resources Association (link)

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