Conservation Agriculture Enhances Soil Organic Carbon: A 5‐Year Assessment Using Sentinel‐2 and Machine Learning

ABSTRACT Monitoring soil organic carbon (SOC) dynamics is crucial for sustainable agriculture, soil health, and climate change mitigation. In this study, we assessed 5‐year SOC changes in conventional (CO) and conservation agriculture (CA) fields across France and Italy using a combination of Sentinel‐2 satellite imagery and machine learning (ML) models. A total of 922 topsoil samples (0–20 cm) were collected to build a training dataset representing the main soil types and climatic regions. Satellite‐derived spectral reflectance, obtained through a pixelwise temporal mosaicking approach (median and 90th percentile composites), was paired with laboratory‐measured SOC to train soil‐type‐specific Cubist models, which were validated both internally and against the independent LUCAS dataset. The models demonstrated high predictive performance (normalized Root Mean Square error = 0.18–0.21, Ratio of Performance to InterQuantile range ≈2.0) and were then applied to 48 CA fields where conservation practices had been in place for at least 5 years, allowing the mapping of SOC values before and after the implementation of these practices. To provide a benchmark, SOC mapping was also conducted for 48 CO fields, covering the same time window (2018–2023). Results indicate that CA practices led to significant SOC increases in most fields, particularly in Cambisols and Luvisols, with an average ΔSOC of +0.95 g kg −1 over 5 years, whereas CO fields showed negligible changes. While Italy and France differed in baseline SOC levels, CA fields in both countries exhibited SOC gains tied to higher SOC: clay ratio and improved soil health, demonstrating the tangible benefits of crop rotation, permanent soil cover, and reduced tillage. This study demonstrates that combining Sentinel‐2 imagery with validated ML models enables robust monitoring of SOC dynamics across contrasting management systems, soil types, and climatic conditions. The approach provides a scalable and cost‐effective tool to evaluate agricultural practices and to guide strategies for soil health and carbon sequestration.