## 1 Introduction
One of the main challenges posed by spaceborne hyperspectral imagery is the availability of relevant information in high-dimensional data containing highly correlated spectral information. As a branch of artificial intelligence, machine learning (ML) refers to algorithms that can explore and derive meaningful information from data and use them within a self-learning approach to construct algorithms/models for accurate classification or prediction. ML has gradually gained popularity due to its accuracy and reliability. ML algorithms, e.g., artificial neural networks (ANNs), support vector machines (SVMs), and random forest (RF) have been widely applied in the last decade in crop type identification [16; 17; 29; 30; 31; 32]. Furthermore, the use of deep learning (DL) algorithms has allowed to explore several levels of distributed representations from the input training dataset. Among them, convolutional neural network (CNN) is the most common DL method applied to crop mapping [33]. This is because CNNs reduce the need for building handcrafted feature extractors, by demonstrating an exceptional ability to learn complex representations directly from image bands. Larger and more diverse training data lead to improved functionality of CNNs. A long running time, especially when working with big data, has been a challenging issue that is expected to be solved by improved hardware and software components of machine vision systems [34; 35]. In comparison with CNNs, the architectures with more layers and parameters, like Visual Geometry Group (VGG-16), AlexNet, and Res-Net, need more training data to fit the model parameters [36]. Classical ML algorithms process each pixel vector based on spectral features without considering spatial contextual information [37]. Keeping the spatial information could help crop type mapping because neighboring pixels have higher probabilities of belonging to the same crop class [38]. Hence, 2D-CNN can calculate spatial features in both directions, but it does not have spectral features. 2D-CNN has been used for crop mapping [39] to identify the phenology in Sentinel-2 images [40] and leaf age from a single image [41]. 3D-CNN, even if at a higher computational cost, is able to exploit high-dimensional spectral features [42]. The main issue in 3D methods is the increased dimensionality of samples that may affect the classification accuracy and efficiency [43]. Spectral/spatial pixel-wise hyperspectral image classification can be achieved by integrating spatial features into spectral information. Therefore, different feature extraction methods have been used to overcome this issue [18; 44; 45; 46]. Early-season crop mapping using supervised methods could be severely hampered by the absence of contemporary Earth observation (EO) data and ground truth acquisitions. Ideally, ground truth should be obtained from surveys that collect targeted first-hand information, but it is often impeded by the substantial cost of time and labor [47]. Most classifiers work well through training using similar ground truth data, while the spatial transferability of these methods is still a challenging issue. In addition, environmental conditions (rainfall, temperature, etc.) could change over the years, severely impacting crop growing stages. To overcome this limitation, several early-season mapping methodologies, e.g., sample migration [48] and temporal encoding [49], have been developed to effectively classify crop types, even in situations where there is limited ground truth data available for that specific growing season. These approaches leverage historical data to generate crop labels and prior information for early-season crop identification. Moreover, a transfer learning (TL) scheme based on DL approaches helps to gain crop mapping results in transfer sites.