###### Abstract
Brovey (BR) is a modulation-based method that builds a new synthetic image from the concept of RGB image compositing, which is based on the conformation of the intensity value from the original image bands [24]. High-frequency modulation (HFM) transfers the high frequencies of a high spatial resolution image containing image detail information to a low spatial resolution image, in this case, the multispectral image. To avoid fusion errors, mainly in areas of low radiometric correlation between the high-frequency image and the original image, a correction is applied using a low-intensity image [25]. The Gram-Schmidt (GS) process is a method patented by [26] that generates a synthetic band from the bands of the original image using the Gram-Schmidt transformation process. The SAR band replaces this generated band, and the inverse process of the transformation is performed. The principal components (PC) method is based on the concept of principal component transformation of the original multispectral image. The first principal component (PC1), which contains information on the intensity values of the image, is replaced by the SAR band. Subsequently, the inverse transformation process is performed to obtain the fused image [27]. Both images must be pre-processed to obtain a good fusion of the optical and SAR images. The \"salt and pepper\" speckle effect associated with SAR images must be reduced [28]. Usually, speckle reduction is performed using different methods based on the kernel matching of different sizes [29]; however, by employing the optical and SAR image fusion methods, the fused synthetic image still presents a lot of noise associated with the speckle effect. To obtain a more significant reduction in the speckle effect, and thus, to derive a better fused optical and SAR image, this research applies the multitemporal speckle reduction method proposed by [30], which allows a remarkable reduction in the speckle effect. In the bibliography, it is possible to find different methods for land cover classification on satellite images [31] based on machine learning (ML) algorithms, such as the support-vector machine, decision tree, random forest and k-nearest neighbour, and others based in deep learning (DL) algorithms, such as the fractional Gabor convolutional network,spatial-temporal invariant contrastive learning, subspace clustering for hyperspectral images and others. Deep learning (DL) algorithms have been highly studied recently and have shown great potential for classification accuracy. However, the computational cost for implementing these methods is extremely high, requiring supercomputers or high-performance servers to process the large volume of data needed. Meanwhile, studies show that machine learning (ML) algorithms can obtain high accuracies in land cover classification [32; 33] with meager computational costs, as opposed to DL. Among these is the random forest (RF) method, which is a machine learning algorithm widely used as a supervised classification technique [34] and proposed by [35]. This algorithm consists of a combination of tree predictors based on the principle of decision trees, which are analytical methods that explore inside a data set of different possibilities for a given decision [36]. The property that makes this model so robust in its prediction is the large number of uncorrelated trees that can be analyzed, and putting them together significantly reduces individual errors. This is why random forest obtains very high accuracies compared with other ML classification methods [33]. In the literature, it is possible to find research focused on determining different land covers through satellite images from various sensors.