📚 Representative Works
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Sea Surface Temperature (SST)
Sea surface temperature is the most important parameter to represent an indicator and index for climate change monitoring. Interpretation of the trend of sea surface temperature is required for oceanography and climatology. Satellite-derived products are of key importance for the high-resolution monitoring of the ocean surface on a global scale. Due to the sensitivity of spaceborne sensors to the atmospheric conditions as well as the associated Spatio-temporal sampling, ocean remote sensing data may be subject to high-missing data rates. The Spatio-temporal interpolation of these data remains a key challenge.
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Machine learning approach to reconstruct sea surface temperature through Fusion of passive microwave and thermal infrared satellite data
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Daily SST composite fields (4 km) were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data
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Using time-series deep-learning approaches, prediction of SST and detection of ocean heat wave were conducted
Jung et al., 2020
Jung et al., 2022
Jung et al., 2022
📝 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.