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

背景来自卫星遥感、数据分析和机器学习的地球观测是快速发展的研究领域。它们提供了解锁缩放挑战的潜力，以便跟踪再生农业实践在减少温室气体排放和改善土壤健康方面的影响。了解这些做法的影响需要测量的因素，包括但不限于，土壤有机质（SOM），土壤有机碳（SOC），土壤水力特性和土壤压实。

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.