## 4 Discussion  
Earth observation satellites equipped with diverse sensors gather data with varying spatial, spectral, and temporal resolutions, along with different resistance to atmospheric conditions. Over the past decade, Earth observation (EO) imagery has become the primary data source for classifying land cover and mapping crops due to its efficiency and rapid data acquisition [52]. Furthermore, there has been a significant increase in the adoption of methods aimed at improving the classification accuracy of EO imagery for crop mapping [27]. These methods have evolved from relying solely on individual images [53] to incorporating time series imagery [54]. Integrating time series of EO satellite imagery enhances performance by capturing changes in crop phenology throughout the growing season [55; 56]. Traditional classification methods often overlook temporal connections within image time series [27], highlighting the need for more advanced methods capable of accounting for these relationships. Geospatial artificial intelligence (GeoAI), utilizing powerful learning methods such as ML and DL, has emerged as a dominant approach for classifying EO imagery by addressing temporal relationships within image time series. This study, aligning with the objectives of beekeepers, aimed to employ GeoAI models to capture temporal relationships within Sentinel-2 SITS, thereby enhancing crop mapping accuracy for predicting five essential crops in pollination services. Two main classification approaches are utilized for analyzing EO satellite imagery in crop mapping: pixel-based [57; 58] and object-based [59; 60]. In pixel-based classification, each pixel in the image is classified individually based on its spectral properties, operating at the pixel level. Conversely, object-based classification involves grouping adjacent pixels into homogeneous and meaningful objects or segments, considering higher level entities. These image objects are then assigned target classes using supervised or unsupervised classification techniques [61]. Both approaches have their strengths and weaknesses, and the choice between them depends on specific objectives, data characteristics, and available resources. In alignment with recent research focusing on crop mapping using DL methods [20; 26; 28], this study focused on applying the pixel-based classification approach. Given the ability of DL models to automatically extract high-level features in an expert-free end-to-end fashion, this study aimed to capture expert-free temporal and spectral features, specifically targeting temporal features using 1 DTempCNN and LSTM models, and spectral features using the 1 DSpecCNN model. Our findings indicated that applying Conv1D across the spectral domain proved more effective for distinguishing land covers and crop types than applying it across the temporal domain. Additionally, our findings, consistent with those of [PERSON] and [PERSON] [62], demonstrated that the LSTM model outperformed the CNN models, underscoring its effectiveness in capturing temporal features and highlighting its suitability for crop mapping using multi-temporal Sentinel-2 satellite imagery. Furthermore, our findings, consistent with the earlier studies [62; 63; 64], demonstrated the LSTM model's potential to provide a more accurate solution for crop recognition. DL models tend to favor majority classes, as every sample equally influences internal parameters during training. Previous research has addressed imbalanced data challenges through various strategies at both data and algorithm levels. At the data level, methods like oversampling [40] and undersampling [65] have been proposed, involving random sampling with or without replacement to effectively tackle class imbalances. At the algorithmic level, methods such as cost-sensitive learning have been employed to circumvent dataset manipulation by assigning penalties to samples based on their importance in the training set [66].