RESEARCH
📚 Representative Works
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Son et al., 2021
Drought Monitoring
Drought is one of the most complex natural disasters, making it difficult to clearly define its onset, intensity, and scope. Based on the impacts it causes, droughts are generally classified into four types: meteorological, agricultural, hydrological, and socioeconomic.
To represent drought information, we utilize satellite data, meteorological data, and other relevant sources to conduct large-scale drought monitoring. Through satellite observations, we monitor vegetation vitality, rainfall patterns, surface temperature, and other environmental factors. By analyzing this data, we quantify the stress caused by specific types of droughts (Park et al., 2017; Park et al., 2020, …).
Furthermore, to provide comprehensive drought information, we are developing new drought monitoring methods and indices tailored to user needs (Son et al., 2021). This method considers multiple interdependent variables on a continental scale, allowing users to focus on the type of drought most relevant to their interests.
Additionally, our research delves into the causes of drought and explores key indicators that represent drought phenomena, aiming to enhance understanding and response strategies.
Park et al., 2017
Park et al., 2020
📝 Publications
Last updated : 2026/03/08
Lee, J., Park, H., Im, J., Koyama, C., Tobias, R. R., & Tadono, T. (2026). ESCAPE: An ensemble-based self-calibrated autoencoder with physics-informed estimation of high-resolution soil moisture and surface roughness from ALOS-2/PALSAR-2 polarimetric observations. International Journal of Applied Earth Observation and Geoinformation, 147, 105206.
Lee, J., Jung, S., & Im, J. (2024). ASCAT2SMAP: image-to-image translation to obtain L-band-like soil moisture from C-band satellite data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
Lee, J., Im, J., Son, B., Cosio, E. G., & Salinas, N. (2024). Improved SMAP soil moisture retrieval using a deep neural network-based replacement of radiative transfer and roughness model. IEEE Transactions on Geoscience and Remote Sensing.
Lee, J., Park, S., Im, J., Yoo, C., & Seo, E. (2022). Improved soil moisture estimation: Synergistic use of satellite observations and land surface models over CONUS based on machine learning. Journal of Hydrology, 127749.
Son, B., Im, J., Park, S., & Lee, J. (2022). Satellite-based Drought Forecasting: Research Trends, Challenges, and Future Directions. Korean Journal of Remote Sensing, 37(4), 815-831.
Son, B., Park, S., Im, J., Park, S., Ke, Y., & Quackenbush, L. J. (2021). A new drought monitoring approach: Vector Projection Analysis (VPA). Remote Sensing of Environment, 252, 112145.
Park, S., Im, J., Han, D., & Rhee, J. (2020). Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output. Remote Sensing, 12(21), 3499.
Park, S., Kang, D., Yoo, C., Im, J., & Lee, M. I. (2020). Recent ENSO influence on East African drought during rainy seasons through the synergistic use of satellite and reanalysis data. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 17-26.
Park, S., Son, B., Im, J., Lee, J., Lee, B., & Kwon, C. (2019). Development of Satellite-Based Drought Indices for Assessing Wildfire Risk. Korean Journal of Remote Sensing, 35(6), 1285-1298.
Rhee, J., & Im, J. (2017). Meteorological drought forecasting for ungauged areas based on machine learning: Using long-range climate forecast and remote sensing data. Agricultural and Forest Meteorology, 237, 105-122.
Park, S., Im, J., Park, S., & Rhee, J. (2017). Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula. Agricultural and Forest Meteorology, 237, 257-269.
Park, S., Park, S., Im, J., Rhee, J., Shin, J., & Park, J. D. (2017). Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees. Water, 9(5), 332.
Im, J., Park, S., Rhee, J., Baik, J., & Choi, M. (2016). Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches. Environmental Earth Sciences, 75(15), 1120.
Park, S., Im, J., Jang, E., & Rhee, J. (2016). Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions. Agricultural and forest meteorology, 216, 157-169.