## 1 Introduction  
Accurate estimations of crop yields are important for many agronomic issues, including agricultural management, national food policies, and international crop trade. For this reason, a variety of methods are employed for crop yield prediction, and the application of satellite images is becoming increasingly important. Satellite remote sensing techniques, which continuously cover large areas, can help provide more accurate estimations of crop yields. [PERSON] et al. [1] predicted corn and soybean yields in Iowa, and [PERSON] et al. [2] estimated the yield of winter wheat in Shandong, China, using a regression model with vegetation index and weather data. Most studies have focused on statistical analysis based on linear relationships between crop yields and vegetation indices obtained from optical satellite sensors [1; 2; 3; 4; 5; 6; 7]. In addition to vegetation indices, various land surface variables, such as weather elements, soil moisture (SM), hydrological conditions, soil properties, and fertilizer application, have also been used in crop yield estimation [8; 9]. Empirical models using vegetation indices and weather elements have been widely applied due to their convenient data acquisition and relatively high prediction accuracy. [PERSON] [10; 11] presented a new mathematical optimization model to compensate for the lack of high resolution remote sensing images, and estimated potato yield using the biomass calculated by the model. Many studies have achieved meaningful results, but the relationships between explanatory variables and crop yields have not always been clarified by linear equations; therefore, artificial intelligence (AI) approaches have become necessary to solve nonlinear and complex problems. AI is a computer technology that simulates human intelligence through learning processes [12]. AI could be an alternative to statistical modeling, because it can efficiently handle the problems of nonlinearity and complexity. Since crop yield is a nonlinear process, AI can be regarded as a suitable prediction approach. AI includes machine learning models, such as the random forest (RF) and support vector machine (SVM), and neural network models, such as the artificial neural network (ANN) and deep neural network (DNN). RF generates a large number of decision trees with slightly different characteristics through repetitive random sampling of the training data. It includes a bootstrap that determines the suitability of the sampling distribution and conducts resampling as needed. Bagging processes, which aggregate the results of the decision trees derived from bootstrapping, are also incorporated in the RF for ensemble modeling [13]. SVM conducts an optimal grouping of data using maximum margin hyperplanes (MMHs) with nonlinear kernel functions, and then builds a statistical model appropriate for each group [14]. ANN is a network model that can produce an approximation by optimizing a weight and bias set for a node-link structure consisting of input, hidden, and output layers [15; 16]. DNN, a part of deep learning, is a more advanced technique that combines the advantages of traditional neural networks and machine learning models through an intensive learning process in a deep network. DNN can overcome the problem of local minima found in classic ANN approaches, and the problem of overfitting seen in traditional machine learning models. This is because the DNN can optimize a deep network structure via the back-propagation algorithm, which integrates forward and backward optimization processes, and by the activation function, which prevents the problem of vanishing gradients of loss functions during the back-propagation process. In addition, unexpected outliers can be handled efficiently by the drop-out method in a DNN model. Recently, AI models have been used in research to improve the accuracy of crop yield prediction. [PERSON] et al.