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M Rial, A Martínez Cortizas and L Rodríguez-Lado
Soil Organic Carbon (SOC) constitutes the largest terrestrial carbon pool. The understanding of its dynamics and the environmental factors that influence its behaviour as sink or source of atmospheric CO2 is crucial to quantify the carbon budget at the global scale. At the European scale, most of the existing studies to account for SOC stocks are centred in the fitting of predictive model to ascertain the distribution of SOC. However, the development of methodologies for monitoring and identifying the environmental factors that control SOC storage in Europe remains a key research challenge. Here we present a modelling procedure for mapping and monitoring SOC contents that uses Visible-Near Infrared (VNIR) spectroscopic measurements and a series of environmental covariates to ascertain the key environmental processes that have a major contribution into SOC sequestration processes. Our results show that it follows a geographically non-stationary process in which the influencing environmental factors have different weights depending on the spatial location. This implies that SOC stock modelling should not rely on a single model but on a combination of different statistical models depending on the environmental characteristics of each area. A cluster classification of European soils in relation to those factors resulted in the determination of four groups for which specific models have been obtained. Differences in climate, soil pH, content of coarse fragments or land cover type are the main factors explaining the differences in SOC in topsoil from Europe. We found that climatic conditions are the main driver of SOC storage at the continental scale, but we also found that parameters like land cover type influence SOC content found at the local scales in certain areas. Our methodology developed at continental scale could be used in future research aimed to improve the predictive performance of SOC assessments at European scale.
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Scientific method, Spectroscopy, Europe, Geography, Model, Climate, Carbon dioxide, Soil
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