Abstract
Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg−1 for mineral soils and a root mean square error of 50 g C kg−1 for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation.
Highlights
Human pressure on the soil has reached the extent to which vital ecosystem services, such as food and fiber production or buffering against increases in greenhouse gas concentrations are at risk [1,2,3]
The Soil Organic Carbon (SOC) values of mineral samples of the Land Use/Cover Area frame Statistical Survey (LUCAS) library are relatively higher than in other large scale spectral library, mainly because many samples were collected in organic-rich soils of northern Europe (Figure 1)
The eigenvectors of the three first principal components (PC) show diagnostic variations across the Visible and Near InfraRed (Vis-NIR) spectrum that can be linked to soil properties (Figure 2)
Summary
Human pressure on the soil has reached the extent to which vital ecosystem services, such as food and fiber production or buffering against increases in greenhouse gas concentrations are at risk [1,2,3]. High-throughput and costeffective methods of SOC analysis should be developed to support the implementation of effective soil inventories and production of digital soil maps at the continental scale from which the state of the SOC can be determined in a consistent manner. Inference is based on multivariate calibration models developed from digital libraries linking Vis-NIR spectral data with reference laboratory measurements [12]. These empirical calibrations are only applicable to samples having similar soil composition and spectral characteristics as those in the library and generally and cannot be extrapolated to other soil types [13]
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