Contrastive learning methods have been applied to a range of domains and modalities by training models to identify similar" views" of data points. However, specialized scientific modalities pose a challenge for this paradigm, as identifying good views for each scientific instrument is complex and time-intensive. In this paper, we focus on applying contrastive learning approaches to a variety of remote sensing datasets. We show that Viewmaker networks, a recently proposed method for generating views without extensive domain knowledge, can produce useful views in this setting. We also present a Viewmaker variant called Divmaker, which achieves similar performance and does not require adversarial optimization. Applying both methods to four multispectral imaging problems, each with a different format, we find that Viewmaker and Divmaker can outperform cropping-and reflection-based methods for contrastive learning in every case when evaluated on downstream classification tasks. This provides additional evidence that domain-agnostic methods can empower contrastive learning to scale to real-world scientific domains. Open source code can be found at https://github. com/anonymous629/divmaker.