## 1 Introduction  
Traditionally, crop-type data are gathered through field surveys, which are time-intensive and costly, especially for large-scale mapping [1]. The growing availability of remote sensing (RS) data now allows for the automatic creation of crop maps by leveraging spectral differences between crop types, offering a more efficient and scalable approach [2]. Some studies use single-date RS data to identify crops by selecting imagery based on the crop growth calendar [3]. Although single-date methods involve fewer image-processing tasks, they present challenges in identifying the best time for crop classification [4]. Alternatively, a satellite image time series (SITS) enables continuous monitoring of crop dynamics, allowing for the distinction of crop types based on their distinct spectral signatures and providing detailed insights into the various phenological stages throughout the growing season [5, 6]. Effectively utilizing temporal sequences to capture fluctuations in crop growth is a significant challenge in crop mapping using SITS data. This has sparked interest in leveraging methods capable of using SITS data to extract features that provide insights into vegetation growth stages. Certain approaches derive phenological features from SITS for further analysis instead of directly using RS data. One approach is to use the original vegetation indice (VI) values during different periods of the year [7]. This approach could have the drawback of overlooking certain crucial information inherent in the time series inputs since the sequential relationship of VI is not expressly taken into account [8]. Accordingly, the temporal sequence of the images' presentation has no bearing on the results; in other words, rearranging the order of values in the series would not alter the model's outcomes or its level of accuracy. An alternative approach is to pre-compute temporal features from the VI time series [9; 10; 11]. This approach, due to taking into account the sequential relationship of Vis, may boost the accuracy of classification compared with the original VI values [12]. Employing or crafting temporal features from VIs such as maximum VI, approximations of crucial dates in the phenological stages of the vegetation classes such as the timing of peak VI, and beginning of green-up [13; 14] or more complex temporal feature extractors such as pre-defined mathematical equations/models [15; 16; 17] have been extensively employed for multi-temporal classification and vegetation phenology investigations. This approach also poses several challenges in extracting temporal features from vegetation dynamics, including (i) the requirement for manual model design and feature extraction, which depends on human expertise and domain knowledge and may not be well suited for specific tasks [8]; (ii) manual feature engineering is time-consuming and is influenced by environmental changes [18]; and (iii) the use of fixed pre-defined models and mathematical assumptions can restrict adaptability when dealing with various temporal patterns [19]. Accordingly, addressing this issue necessitates more adaptable and automated methods that can make the most of the temporal aspect [20]. Deep learning (DL) presents promising solutions for addressing this challenge and demonstrates considerable potential in capturing and representing features from time series data obtained through RS [18]. In recent years, DL models have shown outstanding performance in various computer vision tasks such as image classification, semantic segmentation, object tracking, and object detection [21; 22; 23]. Unlike machine learning (ML) models that require empirical feature design and manually crafted features [24], DL models can automatically extract high-level features from high-dimensional images in an expert-free end-to-end fashion. Considering the strengths of DL models, various attempts have been made to apply DL architectures to the classification of image time series [8; 25; 26], particularly for the aim of crop mapping [8; 27; 28].