Exploring the temporal and spatial extent of image collections to deliver soil health indicators supporting sustainable agriculture.

Utilizing Earth Observation (EO) and Machine Learning (ML) to automate Soil Organic Carbon (SOC) monitoring marks a significant advancement for food security and Sustainable agriculture aligning with the United Nations’ Sustainable Development Goal 2 for zero hunger. In Flanders Belgium, a comprehensive methodology is developed to explore the spatial and temporal content of an extensive collection of over 8000 sentinel 2 images on 680000 hectares of farmland. The scope of the current study is to develop valuable soil health indicators, in support of The Common Agricultural Policy (CAP). This developed methodology combines satellite products from the Copernicus services with precise soil measurements to deploy EO-based ML models for predicting SOC levels through time. The large-scale soil quality data products developed, covered all Flanders, facilitate monitoring that underpins the CAP providing detailed insights at both pixel and parcel level. This approach simplifies the creation of soil quality maps showcasing SOC values relative to average conditions and taking into consideration soil-pedoclimatic factors thus enabling targeted soil health interventions. Such detailed classifications are crucial for the effective management of soil health in Flemish croplands. Current research is focused on improving soil health EO-based evaluation using advanced technologies like sensor data analysis edge computing and Federated AI while ensuring semantic interoperability for improvement. Current efforts are trying to tackle data-sharing challenges and, the ability to integrate IoT sensors and hyperspectral satellite images.
The presented methodological framework addresses the requirements and complexities inherent in soil health and agricultural sustainability and investigates how those research priorities can be aligned with the United Nations’ Sustainable Development Goal 2.