This paper argues that satellite data should be recognized as a distinct modality in machine learning (ML), highlighting its unique characteristics and challenges. Satellite data, characterized by large datasets, diverse spectral channels, and spatiotemporal scales, presents distinct challenges compared to traditional data modalities like natural images or language. The paper emphasizes the need for specialized methods and approaches tailored to satellite data, rather than adapting existing ML techniques. It outlines critical discussion questions and actionable suggestions to transform Satellite Machine Learning (SatML) into a dedicated research discipline. The paper also discusses the ethical concerns and deployment challenges of SatML, emphasizing the importance of responsible and ethical practices. Finally, it highlights how SatML can enrich other ML research areas, such as distribution shift, self-supervised learning, and multi-modal learning, and calls for community collaboration to address these challenges and advance the field.This paper argues that satellite data should be recognized as a distinct modality in machine learning (ML), highlighting its unique characteristics and challenges. Satellite data, characterized by large datasets, diverse spectral channels, and spatiotemporal scales, presents distinct challenges compared to traditional data modalities like natural images or language. The paper emphasizes the need for specialized methods and approaches tailored to satellite data, rather than adapting existing ML techniques. It outlines critical discussion questions and actionable suggestions to transform Satellite Machine Learning (SatML) into a dedicated research discipline. The paper also discusses the ethical concerns and deployment challenges of SatML, emphasizing the importance of responsible and ethical practices. Finally, it highlights how SatML can enrich other ML research areas, such as distribution shift, self-supervised learning, and multi-modal learning, and calls for community collaboration to address these challenges and advance the field.