## 5 Conclusions  
The present-day application of remote sensing in the field of crop phenology demands high-resolution spatio-temporal data. Most of the time, processing such massive amounts of data with a high resolution using conventional geospatial platforms or software is both expensive and time-consuming, as it requires a larger storage capacity and more processing power. The Google Earth Engine (GEE) offers a free cloud computing platform capable of managing and processing significant amounts of satellite image data sets in a relatively shorter time with higher precision. The present study incorporated high-resolution spatio-temporal Sentinel-2 data sets in the Google Earth Engine platform to develop cropping patterns and cropping intensity in association with GIS for 2020-2021 in the segment of Rajasthan. The use of multi-temporal datasets over single satellite images enables advantages like lesser coding, the opportunity of adding more data in the operation, and less time for execution of the entire operation. The method is relatively easy to implement, dependable, and efficient in terms of cost. According to our understanding, applying geospatial techniques in the study area to extract cropping patterns and cropping intensity using the Google Earth Engine is being done for the first time. An overall precision of 84% was achieved by the model that was proposed, with a kappa coefficient of 0.784. Both landowners and agricultural planners will find the current model useful for both the planning of agricultural land use in the present as well as for the future.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122412583/s1](https://www.mdpi.com/article/10.3390/app122412583/s1)]([https://www.mdpi.com/article/10.3390/app122412583/s1](https://www.mdpi.com/article/10.3390/app122412583/s1)).  
**Author Contributions:** Conceptualization, S., T.G., M.M.S.C.-P. and A.G.; Data Curation, S.; Formal analysis, S. and T.G.; Investigation, T.G., M.M.S.C.-P. and K.K.Y.; Methodology, S., T.G., A.G. and K.K.Y.; Project Administration, T.G., A.G., M.A., M.M.S.C.-P. and T.A.; Resources, T.G., T.A., M.A. and M.M.A.; Software, T.G., A.G., M.M.A. and M.M.S.C.-P.; Supervision, T.G. and M.M.S.C.-P.; Validation, S., T.G. and K.K.Y.; Visualization, T.G., K.K.Y. and S.; Funding Acquisition, T.A., M.M.A. and M.A.; writing--original draft, S., T.G. and M.M.S.C.-P.; writing--review and editing, S., T.G., K.K.Y., A.G., T.A., M.M.A. and M.M.S.C.-P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research work was funded by the Deanship of Research, King Khalid University, Saudi Arabia, for the Small Research Group under grant number R. G. P. 1/339/1443. The authors gratefully acknowledge Taif University Researchers Supporting Project, number (TURSP-2020/242), Taif University, Taif, Saudi Arabia. The first author is thankful to the University Grants Commission, India (UGC), for granting a fellowship during the completion of the present work as she is a UGCNET-JRF student.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.