## 1 Introduction
In their study, a performance comparison was performed using area-to-point regression kriging rather than other deep learning-based algorithms. [PERSON] et al. [9] tested shallow and deep CNNs and confirmed that the deep CNN performed the same or better than the shallow CNN. The suggested algorithm demonstrated high-performance, but computational complexity and memory requirements could be problematic because the model is trained for each band. After analyzing the previous studies, we found three common points. The first is that the use of deep neural networks is superior [6; 8; 9]. [PERSON] et al. [8] analyzed the performance of each neural network by constructing shallow, deep, and very deep networks. They confirmed that the deeper the neural network, the higher the performance. Second, most neural networks have residual blocks and skip connections [6; 8; 10; 11]. Consequently, the vanishing gradient problem can be alleviated, and the learning speed improved, even though the neural network is deeper. Third, the size of the input image inside the neural network is maintained until the last stage of the output, in contrast to neural networks for object detection and segmentation. Accordingly, the enlargement function to create the HR is only located in the final stage of neural networks using upsampling convolution layers or pixel shuffle algorithms [11]. [PERSON] et al. [10] applied an enhanced deep super-resolution network (EDSR) to produce a 5 m resolution RapidEye RGB image with a 10 m resolution Sentinel-2 RGB image. They confirmed superior performance among super-resolution (SR) neural networks [11; 12]. Studies so far have used neural networks of increasing resolution between satellite images. In this study, we propose a context-based ResU-Net to increase the resolution of Sentinel-2 imagery using 2.5 and 5.0 m downsampled aerial orthoimage acquired during the same period. For completing the tasks, the aerial orthoimages were first simulated by reconstructing a residual U-Net, which has advantages not only in constructing a deep and dense neural network but also in identifying adjacent contexts and the position of objects. As a result of the experiments, we found that our neural network can express the aerial orthoimages' features and contexts well. Training datasets were newly generated by using Sentinel-2 and aerial orthoimage. Sentinel-2 images, providing 10 m, 20 m, and 60 m resolution of multispectral bands, were utilized in this research. Sentinel-2 has the highest resolution and shortest revisit date among free satellite images. Since the advantage of obtaining many repeat pass images is a factor that can satisfy the objectives of this study, it was selected as input data. SR research is key to securing a high-resolution ground truth (GT), and aerial orthoimages are one of the most reliable and high-quality data. Therefore, aerial orthoimages with a similar acquisition date were utilized as the GT. Two types of aerial orthoimages were produced, 2.5 m and 5.0 m, as ground truth data downsampled from the original aerial orthoimagery. The data were used for testing two-times magnification (5.0 m based on 10 m) and challenged four-time magnification (2.5 m based on 10 m). We tested the effect of using the lowest resolution 60 m image on the model and analyzed the model's influence when the feature dimensions are changed. In addition, the quality of our approach was investigated through the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root-mean-squared error (RMSE), a common approach in many SR studies. Finally, we found that our model's performance in most metrics turned out to be better than that of EDSR. We also identified that incorporating 60 m resolution with 10 m resolution Sentinel-2 images outperforms the combination 10 m and 20 m resolution images. In addition, we confirmed that the denser feature dimensions have better performance.