Deforestation in tropical rainforests is a major source of carbon dioxide emissions, an important driver of climate change. For decades, the Brazilian government has maintained monitoring programs for deforestation detection in the Brazilian Legal Amazon area based on remotely sensed optical images in a protocol that involves considerable efforts of visual interpretation. However, the Amazon region is covered with clouds for most of the year, and deforestation assessment can rely only on images acquired in the dry season when cloud-free images are more likely to capture. One possibility to lessen that restriction and enable deforestation detection throughout the year is to synthesize cloud-free optical images from corresponding SAR images, which are only marginally influenced by atmospheric conditions. This work compares a set of such image synthesis methods, considering deforestation detection in the Amazon forest as the target application. Specifically, we evaluate three deep learning methods for cloud removal in Sentinel-2 images: a conditional Generative Adversarial Network (cGAN) based on the <i>pix2pix</i> architecture; an extension of that method, which uses atrous convolutions (<i>Atrous cGAN</i>) to enhance fine image details; and a non-generative method (<i>DSen2-CR</i>) based on residual networks. In the evaluation, we assess both the quality of the generated images and the accuracy obtained when performing deforestation detection from those images. We further compare those methods with an image aggregation tool available in Google Earth Engine (<i>GEE Tool</i>), which creates cloud-free mosaics from sequences of images acquired at nearby dates. In this study, we considered two sites in the Brazilian Amazon, characterized by distinct vegetation and deforestation patterns. In terms of the quality metrics and classification accuracy, the <i>Atrous cGAN</i> was the best performing deep learning method. The <i>GEE Tool</i> outperformed all those methods when dealing with images from the dry season but turned out to be the poorest performing method in the wet season.