## 1 Introduction
Two main DL architectures used in the classification of SITS are convolutional neural networks (CNNs) [8; 15; 26] and recurrent neural networks (RNNs) [20; 25]. Numerous studies have highlighted the significant potential of these two DL models in the field of crop mapping [26; 28; 29; 30]. CNNs stand out as a prominent DL architecture extensively employed in RS applications, especially land cover classification [31]. CNNs have been employed in RS research for SITS classification to extract spatial and/or spectral features, accomplished through either 1D convolution along the spectral dimension [32], 2D convolution across spatial dimensions [33], or 3D convolution across both spectral and spatial dimensions [34]. Nonetheless, applying convolutions in the spectral and/or spatial dimensions leads to overlooking the temporal aspect and sequence of images [26]. Applying convolutions across the temporal domain has demonstrated effectiveness in addressing temporal aspects in the SITS classification [26; 35]. RNNs are another DL architecture that can link adjacent observations and capture dependencies across time steps to learn sequential relationships [36]. Due to their ability to process sequential data, RNNs are frequently viewed as a suitable choice for understanding the temporal dynamics in SITS and representing changes in land cover patterns [37]. The long short-term memory (LSTM), a popular variant of RNN, is created to overcome the shortcomings of traditional RNNs by enabling them to accurately capture long-term dependencies while circumventing the issue of vanishing gradients. The LSTM has been utilized in RS for various applications, including detecting changes in bi-temporalimages [37; 38], classifying time series data to distinguish crops [25; 30; 39], analyzing land use and land cover [40; 41], and studying vegetation dynamics [42]. Accurate crop prediction is crucial for beekeepers, aiding in strategic hive placement for optimal pollination efficiency and comprehensive coverage across diverse crop pollination needs. Accordingly, this study aimed to map five essential crops for pollination services across a region in Quebec, Canada, using Sentinel-2 SITS. To accomplish this aim, this study attempted to provide beekeepers with three DL-based models, namely one-dimensional temporal CNNs (1 DTempCNNs), one-dimensional spectral CNNs (1 DSpecCNNs), and LSTM. Furthermore, the motivations of this study were investigated to address the following research concerns: (i) assessing the performance of two DL architectures of CNNs and LSTM in crop mapping; (ii) investigating the potential of applying one-dimensional convolution (Conv1D) across the temporal domain (i.e., 1 DTempCNNs model) and its efficacy in crop type prediction compared with applying Conv1D across the spectral domain (i.e., 1 DSpecCNNs model), (iii) providing an approach for tuning models' architecture by assessing the impact of each hyperparameter in the model's architecture on its accuracy using the ANOVA (analysis of variance) method, and (iv) outlining the advantages and limitations of the provided DL-based models in predicting five essential crops for the beekeepers. The specific highlight of the presented study that differentiated it from preceding ones was its ability to address these concerns.