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✨ Research Overview


Sea surface salinity, Sea surface temperature, Sea fog, Red tide, Water quailty.

Sea fog detection

Improvement of SMAP SSS

and Detection

Red tide detection

Water quality mointoring

Severity mapping

Algorithm Development

Interpolation of SST

Interpolation of SSS

Sea fog prediction

SST prediction

📚 Representative Works

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SSS Monitoring and Prediction

    Sea Surface Salinity (SSS) is a key parameter of the ocean, and it serves as a proxy for climate change by interacting directly with the atmosphere. We are monitoring and predicting SSS in the Northeast Asia Sea region based on artificial intelligence (AI) methods. We are using the fusion of multiple satellite, reanalysis models, and in-situ data. From our studies, the SSS of the Northeast Asia Sea can be monitored more accurately, and it can reduce damage from low salinity water by predicting SSS in summer.

  • Fusion of remote sensing, reanalysis model, and in-situ data for SSS monitoring and prediction

  • Improvement of SMAP SSS

  • interpolation of spatial missing SSS data using timeseries data

  • Applying deep learning to SSS studies

Jang et al., 2021

In progress


📝 Publications

Last updated : 2021/02/10

Jang, E., Kim, Y., Im, J., & Park, Y. G. (2021). Improvement of SMAP sea surface salinity in river-dominated oceans using machine learning approaches. GIScience & Remote Sensing, 1-23.

Jung, S., Kim, Y., Park, S., & Im, J. (2020). Prediction of Sea Surface Temperature and Detection of Ocean Heat Wave in the South Sea of Korea Using Time-series Deep-learning Approaches. Korean Journal of Remote Sensing., 36(5), 1077-1093.

Jang, E., Im, J., Park, G. H., & Park, Y. G. (2017). Estimation of fugacity of carbon dioxide in the East Sea using in situ measurements and Geostationary Ocean Color Imager satellite data. Remote Sensing, 9(8), 821.

Kwon, Y. S., Jang, E., Im, J., Baek, S. H., Park, Y., & Cho, K. H. (2018). Developing data-driven models for quantifying Cochlodinium polykrikoides using the Geostationary Ocean Color Imager (GOCI). International Journal of Remote Sensing, 39(1), 68-83.

Pyo, J., Ha, S., Pachepsky, Y. A., Lee, H., Ha, R., Nam, G., ... & Cho, K. H. (2016). Chlorophyll-a concentration estimation using three difference bio-optical algorithms, including a correction for the low-concentration range: the case of the Yiam reservoir, Korea. Remote Sensing Letters, 7(5), 407-416.

Jang, E., Im, J., Ha, S., Lee, S., & Park, Y. G. (2016). Estimation of Water Quality Index for Coastal Areas in Korea Using GOCI Satellite Data Based on Machine Learning Approaches. Korean Journal of Remote Sensing, 32(3), 221-234.

Kim, Y. H., Im, J., Ha, H. K., Choi, J. K., & Ha, S. (2014). Machine learning approaches to coastal water quality monitoring using GOCI satellite data. GIScience & Remote Sensing, 51(2), 158-174.

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