## 1 Introduction
The ability of machine learning algorithms to automatically detect complicated spatiotemporal patterns in data without the requirement for expert descriptions [28]. Since machine learning algorithms rely on the dispersion and distribution of the training data without making any assumptions, automated burned area detection becomes possible [12]. Various machine learning techniques have been widely applied in wildfire monitoring, such as the Random Forest (RF), the Support Vector Machines (SVM), the Artificial Neural Networks (ANN), and the decision trees [27]. In [29], an automatic burned area mapping system was proposed employing paired Sentinel-2 images, using the SVM for an initial classification and a multiple spectral-spatial classification strategy for delineating the final burned area. Moreover, the RF and the seed-growing techniques were used to construct a worldwide burned area mapping approach [30]. [PERSON] et al. [31] proposed a study that examined large-scale wildfires in New South Wales, Australia. The researchers performed numerous tests by applying the RF method, which is trained on a variety of spectral indices, such as dNDVI and dNBR. Recently, deep learning-based techniques were developed to detect wildfire regions using satellite imagery due to their benefits of fusing contextual information and multiscale spatial details [12; 31; 32; 33]. [PERSON] et al. [32], for example, suggested a deep learning method for mapping burned areas using MODIS satellite imagery. [PERSON] et al. [33] employed a five-layer deep neural network (DNN) with MODIS derived variables (e.g., NDVI and surface reflectance (SR)) to map fires in Interior Alaska. Several machine learning algorithms, e.g., Light Gradient Boosting Machine (LightGBM), HRNet, U-Net, Fast-SCNN, and DeepLabv3+, were applied to Sentinel-2 and Landsat-8 imagery in wildfire regions [12]. Among the deep-learning approaches, U-Net-based semantic segmentation approaches were commonly used to classify the burned areas [7; 34; 35]. [PERSON] et al. [34] recently investigated the performance of U-Net and ResUnet using a bitemporal Landsat image pair. [PERSON] et al. [35] presented a processing chain based on U-Net for burned area detection using uni-temporal Sentinel-2 data. This work aims at detecting forest burned areas occurring in South Korea using satellite images. To exploit an advantage of applying machine learning methods, here, we employed three representative machine learning methods, LightGBM, RF, and U-Net, to detect forest burned areas with a combination of input variables including SR, NDVI, and NBR. Three different Schemes while varying the combination of input variables were suggested and compared. Two study sites recently occurring forest fire events in South Korea were selected, and Sentinel-2 satellite images were used by considering a small scale of the forest fires. The quantitative and qualitative evaluations according to the machine learning methods and input variables were carried out. Main contributions of this study can be summarized as follows. First, a detailed comparison of burned area detection performance between the deep learning-based machine learning model and ensemble learning-based machine learning models was performed. Based on the comparative results, advantages and disadvantages of each model for detecting the burned area were presented, allowing readers to choose the proper model for their detection cases. Second, the burned area detection performance was evaluated while varying the combination of input variables for training the model and obtaining the burned area detection map. It was also included to confirm the possibility of detecting the burned area by using only one satellite image acquired after the forest fire event.