## 5 Conclusions  
EO provides large-scale high-resolution data for monitoring Earth's environment, which, combined with GeoAI modeling, enhances the analysis of geospatial big data for applications like land cover classification and crop mapping. This study focused on using EO imagery for crop mapping, aiding agricultural sustainability by assisting beekeepers in making informed decisions about five placement to ensure diverse diets and reduce pesticide exposure. This strategy improves bee pollination efficiency, increasing crop yields and reducing bee mortality. This research aimed to map thirteen land cover classes, with particular emphasis on five essential crops in the pollination services--apples, corn, soy, canola, and small fruits--in Quebec, Canada, using Sentinel-2 SITS. To accomplish this aim, this study employed three DL-based models, namely 1 DTempCNN, 1 DSpecCNN, and LSTM. In addressing the research concerns, the findings of this research indicated that (i) the LSTM model outperformed the 1 DTempCNN and 1 DSpecCNN models in land cover and crop type classification; (ii) applying Conv1D across the spectral domain was more effective for distinguishing land covers and crop types than across the temporal domain; (iii) using a systematic approach for the architecture tuning of models through assessing the impact of hyperparameters on models' accuracy helped in optimizing models' configurations; and (iv) although the developed DL-based models yielded satisfactory outcomes on all training, validation, and test datasets, our findings underscored the constrained generalizability of models attributed to real-world datasets not adhering to the IID assumption. This research contributes to evaluating various DL-based models for crop mapping, offering valuable insights for advancing future research and practical applications in agriculture and environmental management. Furthermore, this study underscores both the strengths and the limitations of using GeoAI modeling with Sentinel-2 SITS to address crop mapping challenges.  
**Author Contributions:** Conceptualization, N.M.G. and L.P.; methodology, N.M.G. and L.P.; software, N.M.G.; validation, N.M.G.; formal analysis, N.M.G. and L.P.; investigation, N.M.G. and L.P.; resources, N.M.G.; data curation, N.M.G.; writing--original draft preparation, N.M.G.; writing--review and editing, N.M.G., L.P. and N.C.; visualization, N.M.G.; supervision, L.P.; project administration, L.P.; funding acquisition, L.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially funded by the Institut de Valorisation des Donnees (IVADO): REGO 0006; the Mathematics of Information Technology and Complex Systems (Mitacs): IT34553; and the Natural Sciences and Engineering Research Council (NSERC): RGPIN/05396-2016 awarded to L.P.  
**Data Availability Statement:** Datasets used in this research were publicly accessible at the time of writing this paper. Furthermore, all the code to replicate our findings can be found in the following link: [[https://github.com/Nmg1994/Crop_mapping/tree/main](https://github.com/Nmg1994/Crop_mapping/tree/main)]([https://github.com/Nmg1994/Crop_mapping/tree/main](https://github.com/Nmg1994/Crop_mapping/tree/main)).  
**Acknowledgments:** The authors gratefully acknowledge the support of Compute Canada for providing advanced research computing (ARC) resources that facilitated the execution of the computational tasks in this research.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Nectar Technologies Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.