###### Abstract
Scholars also used Sentinel-1 data with Google Earth Engine to classify croplands [21]. Using the random forest technique, 76.88% accuracy was obtained with a kappa score of 0.728. The study concluded that the Sentinel-1 images are accurate enough for cropland classification and identifying paddy field cropping patterns. Rice area, cropping patterns, and growth stages in Southeast Asia were attempted using Sentinel-1 Time Series on a Google Earth Engine platform [22]. Overall, the rice extent was measured, and a kappa coefficient of 0.92 was achieved. The study concluded that Support Vector Machine (SVM) and Artificial Neural Networks (ANN) outperform the random forest. The authors also suggested that simple and robust technology might be used to replace time-consuming, expensive field surveys across Southeast Asia. A method was developed to use Google Earth Engine and the random forest classifier to create a map of irrigated land [23]. During 2018/19 and 2019/20 winter growing seasons, the Normalized Difference Vegetation Index (NDVI) was subsequently employed to distinguish between irrigated and rain-fed lands. The study incorporated ground-based training samples and Very High-Resolution Images (VHRI). The overall accuracy of classification was 88%. Using deep learning and the Google Earth Engine, a few studies focused on the Sentinel SAR-optical fusion for crop-type mapping [24]. The results demonstrated that integrating multi-temporal SAR and optical data gives overall greater training accuracy for crop-type mapping than either multi-temporal SAR or optical data alone. Application of the Google Earth Engine using Sentinel imagery and neural networks was attempted for crop mapping in Canada [25]. In this study, the Google Earth Engine cloud computing platform was employed for the first time in conjunction with an Artificial Neural Networks (ANN) algorithm using Sentinel-1 and Sentinel-2 images to generate an object-based annual space-based crop inventory map for 2018. The study revealed that further research should be conducted on the proposed cloud computing technology since it is more efficient in terms of cost, time, calculation, and automation. Google Earth Engine cloud computing was also adopted to automate the mapping of Africa's croplands using satellite imagery [26]. The researchers used a 16-day time series of MODIS NDVI data at a resolution of 250 meters over the whole African continent. The study concluded that MODIS 250-m time-series data could rapidly map cropped areas; however, there are currently inadequate reference data to report trends from these results in a systematic manner. For understanding the temporal dynamics of spatial heterogeneity over agriculture using time-series NDVI, near-infrared and red reflectance of Landsat 8 OLI images were used [27]. The results showed that before planting, the NIR and red variables detect a similar degree of spatial heterogeneity in the cropland, with comparable values of the variability in mean length. The study concluded that the NDVI and NIR values capture a similar spatial heterogeneity, which is modest in comparison to the red band due to the homogeneity of soil and the mean length variability of NDVI, NIR, and red variables tend to be the same as the image pixel size increases. An artificial neural network was developed for the purpose of crop classification by utilizing datasets from the C-band RISAT-1 satellite [28]. The classification results were validated on the ground. The overall accuracy for satellite data acquired in the month of August and September was 74.21 and 77.36 percent, respectively. A scalable parcel-based crop identification technique employing Sentinel-2 data time-series for monitoring the common agricultural policy was developed in segments of Spain [29]. The application of the technique to a Lansat-8 OLI-based equivalent variable space yielded a Cohen's kappa coefficient of 0.70 for the SVM classification, demonstrating the improved performance of Sentinel-2 for this type of application.