###### Abstract
Among these, studies focused on the application of the random forest classifier to fusions of optical and SAR images to improve the detection of permeable and non-permeable surfaces stand out [19] and are oriented to change detection analysis [37], evidencing better overall crop mapping accuracies when using this classifier over other classifiers [38; 39] and demonstrating the potential of this algorithm for the classification of multitemporal (multi-data frequency) SAR images, such as the Sentinel-1 images. In addition, studies conducted by [40; 41] emphasize the efficiency of this algorithm in the Google Earth Engine (GEE) cloud computing environment when analyzing large territories using only optical imagery and research combining optical and SAR imagery to improve classifier accuracies [14; 42]. Globally, negative impacts on natural ecosystems due to the growth of the agricultural sector have been evidenced [11], where the oil palm agro-industry has shown a growing demand for oil, which has boosted the development of this sector [13]. The oil palm agro-industry in Colombia is positioned as the largest oil producer in the Americas and the fourth largest in the world [43]. The growth of the crop could impact mainly the savanna region of the country [44]. Therefore, it is essential that we find new methodologies based on geospatial Earth observation technologies that allow the agricultural sector to establish a basis and criteria for achieving productive sectoral organization and, at the same time, environmentally sustainable development [45; 46], as proposed in the elaboration of the agroecological zoning plans. Given that the official sourcing of information on land cover in Colombia is carried out at large scales (1:100,000) [47], the maps show errors in vegetation coverage interpretation, mainly when dealing with oil palm, and in forest areas and tall vegetation discrimination, so it is not possible to conduct detailed studies within a given area. In addition, the satellites used to produce land cover maps have low temporal resolutions, and the high percentage of cloudiness present in the national territory hinders the availability of good quality images, thus delaying the updating of land cover maps. This research evaluates the delimitation of land covers focused on oil palm crops using optical and SAR image fusion methods, which have been little studied until now. Optical and SAR image fusion methods allow for more accurate and detailed land cover classification [18; 19], enabling improved monitoring of the dynamics of change associated with oil palm cultivation in Colombia. In addition, in the literature, the fusion methods and the integration/synergism of optical and SAR images are usually evaluated separately, without comparing the two ways of linking this information. This study compares the evaluation of land cover classification in the optical image, the SAR image and in image fusion methods, and includes image integration/synergism. It is worth mentioning that remote sensing plays a vital role in carrying out studies on large extensions of territory and efficiently monitoring dynamic sectors, such as the agricultural sector [40]. Therefore, this research aims to improve land cover classification,especially oil palm crop cover, using optical/SAR image fusion methods by implementing a random forest (RF) algorithm in cloud computing platforms.