Combining Soil Databases for Topsoil Organic Carbon Mapping in Europe

Area of Science:

• Soil Science
• Environmental Science
• Geospatial Analysis

Background:

• Accurate soil organic carbon (SOC) assessment is crucial for ecosystem and agricultural functions.
• Existing studies focus on optimal SOC mapping methods for Europe.
• This research investigates the performance of aggregated soil sample datasets from diverse sources and land uses.

Purpose of the Study:

• To evaluate the effectiveness of combining soil sample databases from different European projects (LUCAS, BioSoil, SoilTrEC) for SOC distribution assessment.
• To identify key spatial predictors influencing SOC content across Europe.
• To analyze regional variations in SOC content.

Main Methods:

• Utilized 23,835 soil samples from agricultural, forest, and local soil datasets.
• Employed 15 spatial indicators including elevation, slope, climate data, land cover (CORINE), and soil properties (texture, WRB classification).
• Applied the Regression-Kriging (RK) geostatistical technique for SOC mapping and analysis.

Main Results:

• Regression-Kriging is suitable for SOC mapping, but combining diverse databases did not enhance statistical significance.
• Elevation, slope, compound topographic index, temperature, precipitation, texture, WRB, and CORINE land cover were significant predictors of SOC variation.
• Wetland areas showed the highest SOC; agricultural soils had lower SOC than forest and semi-natural areas. Significant regional differences in SOC content were identified across Europe.

Conclusions:

• Aggregating diverse soil datasets does not automatically improve SOC mapping accuracy using RK.
• Spatial predictors like topography, climate, and land cover are critical for understanding SOC distribution at the European scale.
• Targeted conservation and management strategies are needed, considering the identified regional disparities in soil organic carbon levels.