With GEE, users have convenient access to Java and Python Application Programming Interfaces (APIs), eliminating the need to download data for different tasks. As a result, harnessing the capabilities of GEE to develop novel classification approaches with improved accuracy can enable scientists to obtain more reliable results in near real-time earth observation purposes using RS data. Accurately mapping crop types using RS data is a challenging task due to the high intra- and inter-class variability resulting from crop diversity, environmental conditions, and farming practices [31; 33]. To tackle this challenge, most of the articles used pixel-based supervised Machine Learning (ML) classification algorithms which possess the ability to capture nonlinear relationships within the data [34; 35]. A significant portion of the literature in this field relies on a single classifier to predict the target class. The commonly utilized models encompass Support Vector Machines (SVM) [36], Random Forests (RF) [37], and Artificial Neural Networks (ANNs) [38]. However, some scholars proposed the structure of an ensemble of ML models, leveraging the complementary information from different classifiers to address the aforementioned challenges, and achieved improved accuracy levels [10; 39]. These proposed ensemble structures can be categorized into Parallel (Pa) and Cascaded (Ca) structures [40]. In Pa structures, there are multiple branches, each containing an ML model. To predict the final class of a sample, the predictions from each ML model are combined using simple techniques like majority voting [41]. In the existing literature, Pa structures have primarily been implemented either at the model level or the training data level [42]. At the model level, ML classifiers differ across branches, but input data are the same for each base model within each branch. However, it is important to note that some of the ML models employed in this approach may perform much better than others, resulting in serious uncertainties when they are combined in Pa structure. At the training data level, techniques such as the bagging algorithm (also known as bootstrap aggregation) are employed to train identical ML models using different training data in each branch [43]. While this approach aims to improve performance, the use of a subset of training samples/features prevents the ML models from fully utilizing all of the training data to learn the underlying patterns. Moreover, this strategy required hundreds of models to be trained. Previous scholars did not consider a Pa structure at input data level, in which not only are the entire training data used in each branch, but also the combination of different RS data sources can boost the classification accuracy. In the reviewed literature, the Pa structure relied on simple techniques like majority voting to predict the class of a sample. However, these simple approaches encounter challenges when dealing with complex classification tasks. To address this, some of the articles proposed a Ca structure, where the outputs of a Pa structure were directly used by another classifier, mainly referred to Meta-model [43; 44]. Since the Meta-model itself is an ML model, the process of feature engineering becomes crucial to enhance the final accuracy [45]. In the Ca structure, it is essential for the input features of the Meta-model to exhibit diversity [46]. However, a notable issue arises in which the generated Probability Maps (PM) within each branch exhibit a high correlation among the different classes. Consequently, the direct use of Pa structure's outputs in the Ca structure can lower the performance of the ensemble model. Previous articles utilized feature extraction algorithms to handle the highly correlated input features of ML models, among which the Principal Component Analysis (PCA) technique exhibited promising performance [47; 48]. However, these techniques were used in single-model methodologies and have not yet been employed in an ensemble model structure.