Advancing Soil Health Monitoring: A Machine Learning Approach

Soil is fundamental to ecosystem services, agriculture, and climate regulation, serving as a medium for water and nutrient absorption, a habitat for biodiversity, and a major reservoir for organic carbon. Yet, soil faces increasing threats from degradation caused by intensive land use, deforestation, and climate change, which jeopardize food security and environmental sustainability.To address these challenges, this work harnesses recent advances in remote sensing and data analytics to create a comprehensive global soil dataset spanning from 1985 to 2023. This dataset covers five key properties: pH, salinity, nitrogen, phosphorus, and organic carbon content. By leveraging Google Earth Engine and machine learning algorithms, we generated global, high-resolution maps that enable researchers to monitor changes in soil health over time and predict future trends.The primary objective of this dataset is to support decision-making for sustainable land management, agriculture, and environmental conservation. It offers a critical tool for combating soil degradation and mitigating its impacts, empowering stakeholders with actionable insights to preserve and restore soil health on a global scale.