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Seohui Park
Seohui Park
Morgan State University (MSU) & NASA Goddard Space Flight Center (GSFC)
Preverjeni e-poštni naslov na nasa.gov - Domača stran
Naslov
Navedeno
Navedeno
Leto
Classification and mapping of paddy rice by combining Landsat and SAR time series data
S Park, J Im, S Park, C Yoo, H Han, J Rhee
Remote Sensing 10 (3), 447, 2018
1682018
Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia
Y Kang, H Choi, J Im, S Park, M Shin, CK Song, S Kim
Environmental Pollution 288, 117711, 2021
1312021
Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea
S Park, M Shin, J Im, CK Song, M Choi, J Kim, S Lee, R Park, J Kim, ...
Atmospheric Chemistry and Physics 19 (2), 1097-1113, 2019
982019
Estimating ground-level particulate matter concentrations using satellite-based data: a review
M Shin, Y Kang, S Park, J Im, C Yoo, LJ Quackenbush
GIScience & Remote Sensing 57 (2), 174-189, 2020
972020
Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models
S Park, J Lee, J Im, CK Song, M Choi, J Kim, S Lee, R Park, SM Kim, ...
Science of the Total Environment 713, 136516, 2020
622020
A new drought monitoring approach: Vector Projection Analysis (VPA)
B Son, S Park, J Im, S Park, Y Ke, LJ Quackenbush
Remote sensing of environment 252, 112145, 2021
352021
Air quality forecasts improved by combining data assimilation and machine learning with satellite AOD
S Lee, S Park, MI Lee, G Kim, J Im, CK Song
Geophysical Research Letters 49 (1), e2021GL096066, 2022
232022
Estimation of Ground-level PM10 and PM2.5 Concentrations Using Boosting-based Machine Learning from Satellite and Numerical Weather Prediction Data
S Park, M Kim, J Im
Korean Journal of Remote Sensing 37 (2), 321-335, 2021
152021
Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia
S Park, J Im, J Kim, SM Kim
Environmental Pollution 306, 119425, 2022
112022
Monitoring Ground-level SO2 Concentrations Based on a Stacking Ensemble Approach Using Satellite Data and Numerical Models
H Choi, Y Kang, J Im, M Shin, S Park, SM Kim
Korean Journal of Remote Sensing 36 (5_3), 1053-1066, 2020
62020
Synergistic combination of information from ground observations, geostationary satellite, and air quality modeling towards improved PM2.5 predictability
J Yu, CH Song, D Lee, S Lee, HS Kim, KM Han, S Park, J Im, SY Park, ...
NPJ climate and atmospheric science 6 (1), 41, 2023
32023
Estimation of Spatially Continuous Particulate Matter Concentrations under All Sky through the Synergistic Use of Satellite-based AOD and Numerical Models
S Park, J Lee, J Im, S Park
2019 Joint Satellite Conference, 2019
12019
Comprehensive 24-Hour Ground-Level Ozone Monitoring: Leveraging Machine Learning for Full-Coverage Estimation in East Asia
Y Kim, S Park, H Choi, J Im
Journal of Hazardous Materials, 137369, 2025
2025
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