  Monitoring organic matter is pivotal for maintaining soil health and can help
inform sustainable soil management practices. While sensor-based soil
information offers higher-fidelity and reliable insights into organic matter
changes, sampling and measuring sensor data is cost-prohibitive. We propose a
multi-modal, scalable framework that can estimate organic matter from remote
sensing data, a more readily available data source while leveraging sparse soil
information for improving generalization. Using the sensor data, we preserve
underlying causal relations among sensor attributes and organic matter.
Simultaneously we leverage inherent structure in the data and train the model
to discriminate among domains using contrastive learning. This causal and
contrastive constraint minimization ensures improved generalization and
adaptation to other domains. We also shed light on the interpretability of the
framework by identifying attributes that are important for improving
generalization. Identifying these key soil attributes that affect organic
matter will aid in efforts to standardize data collection efforts.
