## 3 Results
### Accuracy Evaluation of the Random Forest (RF) Classifier  
Figures 7 and 8 show the user and producer accuracies calculated for each land cover evaluation. After evaluating the SAR Sentinel-1 image, an OA of 70.48% with a KC of 0.65 was achieved. It was observed that the forest coverage achieved a high OE of 25%, indicating that many pixels were not correctly assigned to the category to which they belonged. A high degree of confusion was found between the coverages of water bodies and bare soils because the SAR image shows a very high similarity between both coverages. The highest CE found for water bodies was 54.71% and 54.29% for bare soil. Meanwhile, the coverage associated with oil palm obtained the highest UA, corresponding to 94.29%. As for Sentinel-2 optical image classification, an OA value of 74.29% with a KC of 0.69 was found. Comparing the KC to the SAR image, a difference of 0.05 was calculated. This value was similar to that obtained by [2] where the classification of their study areas, also evidenced higher values for the optical image. Low values were found in the UA for the bare soil cover, corresponding to 51.43%, presenting significant confusion with grassland and low vegetation. Confusion was found between the classification of low vegetation and grassland with water bodies. This may be because, as mentioned above, within the study area, the water bodies are lagoons with advanced eutrophication processes. The reflectance values in the infrared spectrum range resemble those of a vegetation cover. In the classification of oil palm coverage, there was confusion with some areas of forest and  
Figure 8: Producer accuracies obtained for each land cover. Figure 7: User accuracies obtained for each land cover. low vegetation, so its UA was only 83.33%, equivalent to a decrease of 10.96% with respect to the classification of this coverage using SAR imagery. BR method classification reached an OA of 77.38% with a KC of 0.73, with a 3.10% higher OA as compared to that achieved only with the optical image. A significant increase in UA was also found in oil palm and forest coverages being 91.43% and 84.29% respectively, close to those obtained in the SAR image classification. A high OE was found in the low vegetation cover, presenting more significant confusion with the coverages of water bodies and bare soils. There was a decrease equivalent to 21.43% in the classification accuracy for water bodies compared with the optical image. The classification of oil palm, bare soil, low vegetation and grassland coverages showed better results than using only the optical image. The highest value in OA was obtained using the HFM method, achieving the best OA of the four fusion methods implemented, reaching 78.10%, being 3.81% higher than in the case of the optical image, and a KC of 0.74. In this case, the oil palm and forest coverage obtained UA of 81.43% for both, slightly lower than those obtained with the BR method. As in the case of the BR method, the most significant error in the classification was obtained in the cover associated with bare soils, with a lower UA of 58.57%. Confusion was found in soils with grassland and low vegetation, as well as in some areas of water bodies and forests. Better accuracies were found in the classification of water bodies, equivalent to those found in the optical image. As in the previous case, a high percentage of OE 38.37% was obtained in the low vegetation cover, presenting confusion mainly due to the cover of bare soil and water bodies. In the case of classification by the GS method, an OA of 72.14% was found with a KC of 0.67, which is 2.14% below the OA achieved by the optical image. In this case, there is confusion between oil palm cover with some forest and low vegetation areas, with a UA of 84.29% for this cover, and this value being very close to the one found in the classification of the optical image. A good classification of the grassland coverage was evidenced, reaching 91.43%.