Climate change can have a serious impact on human life and occurs due to the emission of greenhouse gases, such as carbon dioxide, into the atmosphere. Deforestation is one contributing factor to climate change. It is important to understand the drivers of deforestation for mitigation efforts, but there is a lack of data-driven studies that can predict these drivers. In this work, we propose a contrastive learning architecture, called Multimodal SuperCon, to classify the drivers of deforestation in Indonesia using composite images obtained from Landsat 8. Multimodal SuperCon is an architecture that combines contrastive learning and multimodal fusion to handle the available Indonesian deforestation dataset. As a means to yield better performance, Multimodal SuperCon upgrades ordinary SuperCon so that it is able to handle auxiliary spatial variables as additional inputs. Our proposed model outperforms previous work on driver classification, giving an 8% improvement in accuracy compared to a state-of-the-art rotation equivariant model for the same task.