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 daily maximum air temperature (T_max) during summertime in a mega urban area. A total of 10 geographic variables, including the clear-sky averaged land surface temperature and the normalized difference vegetation index, were used as input variables. The stacking model was compared to Cokriging, three individual data-driven methods, and a simple average ensemble model, all through leave-one-station-out cross validation. The stacking model showed the best performance by improving the generalizability of the individual models and mitigating the sensitivity to the extreme daily T_max. This study demonstrates that the stacking ensemble method can improve the accuracy of spatial interpolation of environmental variables in various research fields.

Recent studies have enhanced the mapping performance of the local climate zone (LCZ), a standard framework for evaluating urban form and function for urban heat island research, through remote sensing (RS) images and deep learning classifiers such as convolutional neural networks (CNNs). The accuracy in the urban-type LCZ (LCZ1-10), however, remains relatively low because RS data cannot provide vertical or horizontal building components in detail. Geographic information system (GIS)-based building datasets can be used as primary sources in LCZ classification, but there is a limit to using them as input data for CNN due to their incompleteness. This study proposes novel methods to classify LCZ using Sentinel 2 images and incomplete building data based on a CNN classifier. We designed three schemes (S1, S2, and a scheme fusion; SF) for mapping 50 m LCZs in two megacities: Berlin and Seoul. S1 used only RS images, and S2 used RS and building components such as area and height (or the number of stories). SF combined two schemes (S1 and S2) based on three conditions, mainly focusing on the confidence level of the CNN classifier. When compared to S1, the overall accuracies for all LCZ classes (OA) and the urban-type LCZ (OA_urb) of SF increased by about 4% and 7–9%, respectively, for the two study areas. This study shows that SF can compensate for the imperfections in the building data, which causes misclassifications in S2. The suggested approach can be excellent guidance to produce a high accuracy LCZ map for cities where building databases can be obtained, even if they are incomplete.

A high spatio-temporal resolution land surface temperature (LST) is necessary for various research fields because LST plays a crucial role in the energy exchange between the atmosphere and the ground surface. The moderate-resolution imaging spectroradiometer (MODIS) LST has been widely used, but it is not available under cloudy conditions. This study proposed a novel approach for reconstructing all-sky 1 km MODIS LST in South Korea during the summer seasons using various data sources, considering the cloud effects on LST. In South Korea, a Local Data Assimilation and Prediction System (LDAPS) with a relatively high spatial resolution of 1.5 km has been operated since 2013. The LDAPS model’s analysis data, binary MODIS cloud cover, and auxiliary data were used as input variables, while MODIS LST and cloudy-sky in situ LST were used together as target variables based on the light gradient boosting machine (LightGBM) approach. As a result of spatial five-fold cross-validation using MODIS LST, the proposed model had a coefficient of determination (R²) of 0.89–0.91 with a root mean square error (RMSE) of 1.11–1.39 °C during the daytime, and an R² of 0.96–0.97 with an RMSE of 0.59–0.60 °C at nighttime. In addition, the reconstructed LST under the cloud was evaluated using leave-one-station-out cross-validation (LOSOCV) using 22 weather stations. From the LOSOCV results under cloudy conditions, the proposed LightGBM model had an R² of 0.55–0.63 with an RMSE of 2.41–3.00 °C during the daytime, and an R² of 0.70–0.74 with an RMSE of 1.31–1.36 °C at nighttime. These results indicated that the reconstructed LST has higher accuracy than the LDAPS model. This study also demonstrated that cloud cover information improved the cloudy-sky LST estimation accuracy by adequately reflecting the heterogeneity of the relationship between LST and input variables under clear and cloudy skies. The reconstructed all-sky LST can be used in a variety of research applications including weather monitoring and forecasting.

Increasing heatwave intensity and mortality demand timely and accurate heatwave prediction. The present study focused on teleconnection, the influence of distant land and ocean variability on local weather events, to drive long-term heatwave predictions. The complexity of teleconnection poses challenges for physical-based prediction models. In this study, we employed a machine learning model and explainable artificial intelligence to identify the teleconnection drivers for heatwaves in South Korea. Drivers were selected based on their statistical significance with annual heatwave frequency ( | R | > 0.3, p < 0.05). Our analysis revealed that two snow depth (SD) variabilities—a decrease in the Gobi Desert and increase in the Tianshan Mountains—are the most important and predictive teleconnection drivers. These drivers exhibit a high correlation with summer climate conditions conducive to heatwaves. Our study lays the groundwork for further research into understanding land–atmosphere interactions over these two SD regions and their significant impact on heatwave patterns in South Korea.

Yeonsu Lee, Sun Jegal, Siwoo Lee, Bokyung Son, Jungho Im. Korean J. Remote Sens. 2025;41:843-57. https://doi.org/10.7780/kjrs.2025.41.5.11

The transformation of natural land cover into impermeable materials is a common definition of urbanization. However, it should be noted that vertical growth caused by urban renewal has been disregarded, while the horizontal expansion of urban areas has been widely studied. Understanding dynamic land cover change and its impact focusing on the thermal environment is essential for the sustainable management of urban areas. Therefore, the objective of this study is to propose a simple yet effective urban thermal environment investigation strategy that responds to dynamic land cover transformation. The study area is Suwon, Republic of Korea, where the city has recently been experiencing the most extreme urbanization along with explosive population growth. We designed a three-step approach: 1) extraction of temporal local climate zone (LCZ) to monitor the change in land cover due to urban growth over the period 2004 and 2021, 2) retrieval of high spatial-resolution land surface temperature (LST) using artificial intelligence to observe surface energy flux in heterogeneous urban area in detail, and 3) apply the filtering analysis method, which only uses the pixels classified with a higher confidence level, based on deep learning probabilistic approach to reduce the uncertainty of LCZ classification. To construct the temporal LCZ maps with a 30 m spatial resolution and retrieve the downscaled LSTs (DLSTs) with a corresponding spatial resolution, Landsat series satellites, Shuttle Radar Topography Mission (SRTM), and land cover map produced by Ministry of Environment were used. We obtained the following results: First, the overall accuracy (OA) of the LCZ classification was higher than 90% in both 2004 and 2021. Second, the average coefficient of determination (R2) and root mean square error (RMSE) of the DLSTs were greater than 0.9 and less than 1 °C, respectively. Third, through our suggested urban thermal environment investigation strategy, this study could find that the results of LST changes were clearly varied by building height and compactness changes. Especially, change of low-rise building to mid-rise building increased LST significantly. With these findings, we solidly believe the suggested strategy facilitates advanced urban climate studies.