## 4 Discussion  
It is possible to find several research studies referring to the common use of different sources of satellite data, both optical and SAR, since the information acquired by both sources is complementary [2] and allows for an increase in the accuracy of the classification of different land covers through the implementation of supervised classification methods such as the random forest (RF) algorithm. These studies have been implemented in other applications, such as in the improvement of cartography focused on the mapping of large-scale crops [42; 61], in improved water body visualization using optical and SAR image fusion techniques [19], and in applications oriented to oil palm crop mapping [14], among others. A high level of accuracy was found in the classification of coverages based on the SAR image, corroborating the research developed by [14], which evidences an increase in user accuracy (UA) compared with optical image classification. As mentioned in [57; 62], the specific classification of the cover associated with oil palm crops is highly reliable when using only SAR images. However, the classification of bare soil and vegetation coverage was confusing, corroborating the results obtained by [54], where the main confusions of the classifications in their research were in these two coverages. Regarding the identification of water bodies, the classification through the SAR image presented confusion with bare soils, which, as shown by [13; 63], is associated with the effect of the interaction of SAR waves with water bodies, given that, as mentioned above, these are highly influenced by the specular effect and the dielectric constant of the surface, thus contributing to the differentiation of this cover with others. Regarding the classification through optical imaging, as shown by [3; 63], the capacity to discriminate coverages based solely on SAR images does not attain the high accuracies that are possible to achieve using optical information of the study area. In addition to this, research such as those developed by [56; 64; 65] show that the overall accuracy (OA) using only the optical image is superior to that found using only the SAR image. However, in the  
Figure 9: Comparison of overall accuracies and kappa coefficient for each of the images analyzed. classification of oil palm coverage, a significant decrease in user accuracy was observed compared with that obtained in the SAR image only, which corroborates the result obtained by [14], where they showed that it is possible to find higher accuracies in the classification of oil palm areas using SAR images compared with optical images. In the image fusion methods implemented, an increase in the overall accuracy (OA) was evidenced for the high-frequency modulation (HFM) and Brovey (BR) methods, thus showing the potential of using complementary information sources to improve the land cover classification. In the case of crop mapping, studies developed by [10; 16; 42; 66] demonstrated the increase in classification accuracy of this coverage using Sentinel-2 optical and Sentinel-1 SAR imagery, which is associated with the significant increase of 5.72% obtained in this research when compared only with optical imagery. In the Brovey fusion method, a low user accuracy was evidenced for the coverage associated with bare soil. This is related to the results obtained by [6; 14; 63], where the bare soil and low vegetation coverages presented the lowest user accuracies (UA) compared with the other classified coverages. It was evidenced that the Sentinel-2 optical image classification showed high similarity with the high-frequency modulation (HFM) and Brovey (BR) methods, which corroborates the results found by [2; 11; 12; 56], where, using only optical images, the results were slightly low compared with those obtained by integrating optical and SAR images; however, a significant decrease was found comparing the classification using only SAR images, as evidenced in this study.