## 1 Introduction
This classification was based on the analysis of the Normalized Difference Vegetation Index (NDVI) and Sentinel-2 remotely sensed data. Findings from the study showed that random forest and support vector machine classification methods can be used to sort data from Sentinel-2 remote sensing into different groups. [PERSON] et al. [11] exploredthe viability of utilizing texture information derived from the Gray Level Co-occurrence Matrix (GLCM) in the classification of crops. This investigation involved the analysis of time-series photos captured by unmanned aerial vehicles (UAVs) and the application of machine learning classifiers. The findings indicate that using texture information can result in a 7.72% enhancement in the accuracy of categorization. [PERSON] et al. [12] employed an unmanned aerial vehicle (UAV) platform to capture uncalibrated, small-scale photos of agricultural land in the Swiss Plateau region. These images were subsequently identified using textural characteristics and random forest classification techniques. The findings indicated that the total accuracy reached a value of 66.7% while employing pixel-level crop classification. In the scenario involving the consolidation of a combined group of crops, the overall average (OA) experienced a 7% increase. [PERSON] et al. [13] employed support vector machine and random forest classification algorithms to categorize UAV remote sensing photos. The findings indicated that the support vector machine achieved a producer accuracy of 81.68%, while the random forest algorithm achieved a higher producer accuracy of 96.58%. Previous research has demonstrated that random forests exhibit strong performance in the classification of crops [14; 10; 15; 16]. Google Earth Engine (GEE) is a web-based platform that facilitates geospatial analysis on a global level. It offers a wide range of remote sensing datasets and robust computing capabilities, allowing for users to effectively store, process, and analyze Earth observation data. The GEE platform has the capability to store data at the Pb-level, which is a unit of computer storage. This enables researchers to process many images simultaneously in parallel activities, resulting in a notable enhancement in the efficiency of image processing [17]. GEE has been applied to geospatial mapping at various scales, such as rice distribution mapping [18], follow land mapping [19], tidal flats mapping [14], and land cover mapping [20]. C-band Sentinel-2 is considered the most promising radar data for crop classification because of its medium temporal and spatial resolution and is freely available to the public [21]. Choosing the right method depends on a number of factors, such as the size of the training set, the number of dimensions in the feature space, the presence of correlated features, and the chance of overfitting [22]. Once these concerns have been resolved, the algorithm can be determined. Therefore, in the majority of classification studies, random forest (RF) and support vector machine (SVM) are commonly acknowledged as the leading classifiers due to their ability to achieve a high accuracy [23]. One great thing about the support vector machine (SVM) method for classification is that it can work well with datasets that have a lot of attributes, even if only a few examples are used for training [24]. Support vector machine (SVM) classification methods do have some problems, though. The training and testing phases of the algorithm are not very good, and there are limits on speed and size when choosing kernel function parameters [25]. Random forests are a new and beneficial classification method that only needs two parameters to be set when making a predictive model: the number of decision trees that are formed (t) and the number of input features that are taken into account when each node of the decision tree is split up (m) [26]. Empirical evidence suggests that the model's generalization error approaches a limit as the number of trees in the random forest increases indefinitely.