RESEARCH
📚 Representative Works
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Hourly Aerosol Optical Depth Retrieval
Atmospheric aerosols are not only air pollutants that are harmful to human health, but they also play an important role in understanding climate change. As aerosol optical depth (AOD) provides a quantitative estimate of the aerosols in the atmosphere, it can be used as an indicator of air quality. Due to the limited spatial distribution of ground-based observation stations, continuous monitoring is challenging, prompting the widespread use of satellite-based AOD monitoring. Geostationary satellites are particularly advantageous for real-time and accurate monitoring of aerosols with high spatiotemporal variability. Monitoring aerosols with spatial variations in environmental factors can provide better understanding of the spatiotemporal patterns. Therefore, we aimed to accurately retrieve hourly AOD by integrating the use of geostationary satellites with environmental variables.
Artificial intelligence techniques were employed to retrieve hourly AOD using various input features, evaluating the effectiveness of spatial information and identifying key variables driving AOD variability. GOCI geostationary satellite data were used to analyze the influence of spatiotemporal weather variations on AOD fluctuations. Furthermore, an algorithm utilizing hyperspectral data from the GEMS satellite was developed to extend AOD monitoring across broader regions.
Kang et al., 2022
In progress
📝 Publications
Last updated : 2023/03/12
Choi, H., Park, S., Kang, Y., Im, J., & Song, S. (2023). Retrieval of hourly PM2. 5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and II. Environmental Pollution, 121169.
Kim, Y., Kang, E., Cho, D., Lee, S., & Im, J.. (2022). Improved Estimation of Hourly Surface Ozone Concentrations using Stacking Ensemble-based Spatial Interpolation. Journal of the Korean Association of Geographic Information Studies, 25(3), 74-99.
Park, S., Im, J., Kim J., & Kim, S.M. (2022). Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia. Environmental Pollution, v.306, 119425.
Lee, S., Park, S., Lee, M. I., Kim, G., Im, J., & Song, C. K. (2022). Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning with Satellite AOD. Geophysical Research Letters, 49(1), e2021GL096066.
Kang, Y., Kim, M., Kang, E., Cho, D., & Im, J. (2022). Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia. ISPRS Journal of Photogrammetry and Remote Sensing, 183, 253-268.
Park, S., Kim, M., Im, J. (2021). Estimation of Ground-level PM10 and PM2.5 Concentrations Using Boosting-based Machine Learning from Satellite and Numerical Weather Prediction Data. Korean Journal of Remote Sensing, 37(2), 321-335.
Choi, H., Kang, Y., & Im, J. (2021). Estimation of TROPOMI-derived Ground-level SO2 Concentration Using Machine Learning Over East Asia. Korean Journal of Remote Sensing, 37(2), 275-290.
Kang, E., Yoo, C., Shin, Y., Cho, D., & Im, J. (2021). Comparative Assessment of Linear Regression and Machine Learning for Analyzing the Spatial Distribution of Ground-level NO2 Concentrations: A Case Study for Seoul, Korea. Korean Journal of Remote Sensing, v.37, no.6-1, pp.1739-1756.
Kang, Y., Choi, H., Im, J., Park, S., Shin, M., Song, C.K., & Kim, S. (2021). Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia. Environmental Pollution, 288, 117711.
Choi, H., Kang, Y., Im, J., Shin, M., Park, S., & Kim, S. M. (2020). Monitoring Ground-level SO2 Concentrations Based on a Stacking Ensemble Approach Using Satellite Data and Numerical Models. Korean Journal of Remote Sensing, 36(5_3), 1053-1066.
Park, S., Lee, J., Im, J., Song, C. K., Choi, M., Kim, J., ... & Lee, D. W. (2020). Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models. Science of The Total Environment, 713, 136516.
Shin, M., Kang, Y., Park, S., Im, J., Yoo, C., & Quackenbush, L. J. (2020). Estimating ground-level particulate matter concentrations using satellite-based data: a review. GIScience & Remote Sensing, 57(2), 174-189.
Park, S., Shin, M., Im, J., Song, C. K., Choi, M., Kim, J., ... & Kim, S. K. (2019). Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea. ACP, 19(2), 1097-1113.