## 1 Introduction  
Land cover (LC) data has great importance for different disciplines, such as biodiversity patterns [1], natural hazards studies (i.e., landslides [2] and wildfire [3]), and CO\({}_{2}\) emissions [4]. Additionally, there is a considerable need for current and precise information on LC and its changes for sustainable development and global warming studies [5]. The importance of the mentioned issues and the progress of Remote Sensing (RS) technologiestoward providing data with better temporal and spatial resolutions have motivated scholars and scientists to study LC mapping widely. Although the tremendous attempts exerted in LC mapping, examining the roles of balancing data, image integration, and performance of different machine learning algorithms in various landscapes has not yet received much attention from scholars. The advent of Sentinel-1 and Sentinel-2, providing images with high spatial resolution, global coverage, and ultimately their free access, brings excellent opportunities for LC mapping. As a result, many published papers have been conducted using these images. For example, [PERSON] [6] integrated these images for LC mapping complex boreal landscapes. In another study, those images were integrated for LC mapping in Colombia [7]. Integration of radar and optic RS data can deliver complementary information to improve LC mapping accuracy, taking advantage of both data [8]. More precisely, the geometrical characteristics of the classes are mainly examined by Sentinel-1, providing C-band. At the same time, Sentinel-2 Multi-Spectral Instrument (MSI) is sensitive to the manifest content of the LC classes [9]. It has been reported that incorporating time-series of these images can lead to more accurate and reliable LC maps compared to using them individually [10]. However, integrating these datasets in different landscapes for LC mapping has not yet been well documented. To boost LC mapping accuracy, adding some supplementary information (i.e., textural information and spectral indices) into the classification procedure has been endorsed as an efficient and practical approach [11; 12]. For example, the impact of complementary information (e.g., topographic data and spectral indices) has been investigated for LC mapping in mountainous areas [12]. Texture information provides some continuous measure of distribution in digital numbers of a satellite image within predefined local windows [13]. Using texture information and spectral bands can create high separation capability among different LC types, particularly in heterogeneous landscapes [13; 14]. Moreover, it has been reported that spectral indices can also improve LC mapping accuracy [15]. Since using a large set of features has some disadvantages, such as being time-consuming and highly computational complex [16], selecting the most critical features in LC classification using an appropriate feature selection method can lead to a more operative and reliable LC classification procedure [17]. In this regard, the RS community has widely employed Feature Selection (FS) methods to select the most appropriate features from a pool of available features. Among the different FS methods, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) as a powerful method has been successfully applied in different RS studies to eliminate redundant features [16]. It is generally accepted that Machine Learning (ML) algorithms can effectively improve LC classification accuracy. In this manner, although standard ML algorithms, in most cases, can obtain substantially reasonable accuracies for majority classes, they usually show poor accuracies for rare (minority) classes, mainly owing to the class imbalance problem [18]. Since LC classes are of various distribution and extent, gaining equal samples for all LC classes is very difficult [19], leading to the class imbalance problem and unacceptable accuracies for minority classes. To address this issue, several balancing data have been presented.