📚 Representative Works
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Monitoring of Air Temperature
Urban air temperature is considered a significant variable for a variety of urban issues, and analyzing the spatial patterns of air temperature is important for urban planning and management. However, insufficient weather stations limit accurate spatial representation of temperature within a heterogeneous city. We aim to develop machine learning-based models to estimate urban maximum and minimum air temperature (Tmax and Tmin, respectively) using selected Land Surface Temperatures (LSTs) and other satellite-derived data.
The reliable and robust monitoring of air temperature distribution is essential for urban thermal environmental analysis. In this study, a stacking ensemble model consisting of multi-linear regression (MLR), support vector regression (SVR), and random forest (RF) optimized by the SVR is proposed to interpolate the Tmax during summertime in a mega urban area. This study demonstrates that the stacking ensemble method can improve the accuracy of spatial interpolation of environmental variables in various research fields.
Yoo et al., 2018
Cho et al., 2020
📝 Publications
Last updated : 2023/03/12
Cho, D., Bae, D., Yoo, C., Im, J., Lee, Y., & Lee, S. (2022). All-sky 1 km MODIS land surface temperature reconstruction considering cloud effects based on machine learning. Remote Sensing, 14(8), 1815.
Cho, D., Yoo, C., Son, B., Im, J., Yoon, D., & Cha, D. H. (2022). A novel ensemble learning for post-processing of NWP Model's next-day maximum air temperature forecast in summer using deep learning and statistical approaches. Weather and Climate Extremes, 100410.
Lee, Y., Lee, S., Im, J., & Yoo, C. (2021). Analysis of Surface Urban Heat Island and Land Surface Temperature Using Deep Learning Based Local Climate Zone Classification: A Case Study of Suwon and Daegu, Korea. Korean Journal of Remote Sensing 37(5-3), 1447-1460.
Cho, D., Yoo, C., Im, J., Lee, Y., & Lee, J. (2020). Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique. GIScience & Remote Sensing, 1-17.
Cho, D., Yoo, C., Im, J., & Cha, D. H. (2020). Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas. Earth and Space Science, 7(4), e2019EA000740.
Yoo, C., Lee, Y., Cho, D., Im, J., & Han, D. (2020). Improving Local Climate Zone Classification Using Incomplete Building Data and Sentinel 2 Images Based on Convolutional Neural Networks. Remote Sensing, 12(21), 3552.
Yoo, C., Im, J., Park, S., & Cho, D. (2020). Spatial Downscaling of MODIS Land Surface Temperature: Recent Research Trends, Challenges, and Future Directions. Korean Journal of Remote Sensing, 36(4), 609-626.
Yoo, C., Im, J., Cho, D., Yokoya, N., Xia, J., & Bechtel, B. (2020). Estimation of All-Weather 1 km MODIS Land Surface Temperature for Humid Summer Days. Remote Sensing, 12(9), 1398.
Yoo, C., Han, D., Im, J., & Bechtel, B. (2019). Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images. ISPRS Journal of Photogrammetry and Remote Sensing, 157, 155-170.
Yoo, C., Park, S., Kim, Y., & Cho, D. (2019). Analysis of Thermal Environment by Urban Expansion using KOMPSAT and Landsat 8: Sejong City. Korean Journal of Remote Sensing, 35(6_4), 1403-1415.
Yoo, C., Im, J., Park, S., & Quackenbush, L. J. (2018). Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data. ISPRS journal of photogrammetry and remote sensing, 137, 149-162.
Yoo, C., Im, J., Park, S., & Cho, D. (2017). Thermal characteristics of Daegu using land cover data and satellite-derived surface temperature downscaled based on machine learning. Korean Journal of Remote Sensing, 33(6_2), 1101-1118.