Satellite-based soil organic carbon mapping on European soils using available datasets and support sampling

Abstract

Soil organic carbon (SOC) plays a major role in the global carbon cycle and is an important factor for soil health and fertility. Accurate mapping of SOC and other influencing parameters are crucial to guide the optimization of agricultural land management to maintain and restore soil health, to increase soil fertility, and thus to quantify its potential for sequestering CO2. Remote sensing and machine learning techniques offer promising approaches for predicting SOC distribution. In this study, we used remote sensing data and machine learning algorithms to map SOC at regional to large scale, which we then combined with temporospatial and spectral signature-based soil sampling to integrate local ground measurements. A rigorous validation approach was performed where several independent unseen datasets with a high number of samples were used, which additionally involved densely sampled fields. We found that our approach could predict SOC with an average percentage error of less than 10 % with an R2 of 0.91 using support sampling on croplands located on mineral soils, demonstrating the potential of remote sensing, machine learning, and specific ground measurements for mapping SOC. Our results suggest that this approach could make small carbon differences measurable and inform carbon sequestration efforts and improve our understanding of the impacts of land use and field management practices on soil carbon cycling.

Keywords

• Remote sensing
• Machine learning
• Soil carbon mapping
• Precision agriculture
• Soil health
• Soil carbon sequestration