###### Abstract  
This paper presents an evaluation of land cover accuracy, particularly regarding oil palm crop cover, using optical/synthetic aperture radar (SAR) image fusion methods through the implementation of the random forest (RF) algorithm on cloud computing platforms using Sentinel-1 SAR and Sentinel-2 optical images. Among the fusion methods evaluated were Brovey (BR), high-frequency modulation (HFM), Gram-Schmidt (GS), and principal components (PC). This work was developed using a cloud computing environment employing R and Python for statistical analysis. It was found that an optical/SAR image stack resulted in the best overall accuracy with 82.14%, which was 11.66% higher than that of the SAR image, and 7.85% higher than that of the optical image. The high-frequency modulation (HFM) and Brovey (BR) image fusion methods showed overall accuracies higher than the Sentinel-2 optical image classification by 3.8% and 3.09%, respectively. This demonstrates the potential of integrating optical imagery with Sentinel SAR imagery to increase land cover classification accuracy. On the other hand, the SAR images obtained very high accuracy results in classifying oil palm crops and forests, reaching 94.29% and 90%, respectively. This demonstrates the ability of synthetic aperture radar (SAR) to provide more information when fused with an optical image to improve land cover classification. image fusion; random forest (RF); land cover classification; cloud computing; synthetic aperture radar (SAR); optical images +
Footnote †: journal: _Agriculture_  
0000-0002-2300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-3300-33000  
area classification studies, especially for seasonal crops [2]. The Copernicus Program, sponsored by the European Space Agency (ESA), nowadays provides a global monitoring satellite platform named Sentinel, which captures images of the entire Earth's surface every six days in different regions of the electromagnetic spectrum. The Sentinel-2 satellite [3], mounting an MSI instrument, and Sentinel-1 [4], mounting a SAR-C sensor, both measure in the optical and the microwave ranges of the spectrum. In [5; 6; 7], the authors mention the importance of using satellite information captured in different electromagnetic spectrum ranges, including those associated with the optical and microwave ranges. The joint use of both sources can provide more information on a given area than using these sources separately [8; 9; 10; 11]. By using these two sources, it is possible to link reflectance information captured by optical satellites with information on texture/roughness, moisture content, geometry, and the orientation of the different coverages present in the study area [12]; particularly, on oil palm coverage [13; 14; 15]. In addition, research has been carried out that shows an increase in the accuracy of classification algorithms when both sources are integrated, demonstrating that they are complementary to each other [2; 16]. By implementing optical and synthetic aperture radar (SAR) image fusion methods, it is possible to visually improve the differentiation of land cover, especially land cover with the presence of water bodies [17], and increase classification accuracy by using supervised classification algorithms [18; 19]. Studies have been conducted to map oil palm plantations using a combination of optical and SAR information [20] and using SAR images only [21], demonstrating the potential for the microwave range to discriminate the areas associated with this coverage [13; 22; 23]. In particular, SAR images have been used in areas with high cloud cover since the information captured in the microwave range is not affected by weather conditions. Some commonly used image fusion methods are Brovey (BR), high-frequency modulation (HFM), Gram-Schmidt (GS) and principal components (PC).