The application of satellite remote sensing and machine learning for modelling impacts of regenerative farming practices

Background Earth observations from satellite remote sensing, data analytics and machine learning are rapidly evolving fields of research. They offer the potential to unlock scaling challenges to enable tracking of the impact of regenerative farming practices in terms of reducing greenhouse gas emissions and improving soil health. Understanding the impact of these practices requires measurements of factors including, but not limited to, soil organic matter (SOM), soil organic carbon (SOC), soil hydraulic properties and soil compaction.