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| Titel |
Spatial interpolation of soil organic carbon using apparent electrical conductivity as secondary information |
| VerfasserIn |
G. Martinez, K. Vanderlinden, R. Ordóñez, J. L. Muriel |
| Konferenz |
EGU General Assembly 2009
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 11 (2009) |
| Datensatznummer |
250021691
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| Zusammenfassung |
| Soil organic carbon (SOC) spatial characterization is necessary to evaluate under what
circumstances soil acts as a source or sink of carbon dioxide. However, at the field or
catchment scale it is hard to accurately characterize its spatial distribution since large
numbers of soil samples are necessary. As an alternative, near-surface geophysical
sensor-based information can improve the spatial estimation of soil properties at these scales.
Electromagnetic induction (EMI) sensors provide non-invasive and non-destructive
measurements of the soil apparent electrical conductivity (ECa), which depends under
non-saline conditions on clay content, water content or SOC, among other properties that
determine the electromagnetic behavior of the soil. This study deals with the possible use of
ECa-derived maps to improve SOC spatial estimation by Simple Kriging with varying local
means (SKlm).
Field work was carried out in a vertisol in SW Spain. The field is part of a long-term tillage
experiment set up in 1982 with three replicates of conventional tillage (CT) and Direct
Drilling (DD) plots with unitary dimensions of 15x65m. Shallow and deep (up to 0.8m depth)
apparent electrical conductivity (ECas and ECad, respectively) was measured using the
EM38-DD EMI sensor. Soil samples were taken from the upper horizont and analyzed for
their SOC content.
Correlation coefficients of ECas and ECad with SOC were low (0.331 and 0.175) due to the
small range of SOC values and possibly also to the different support of the ECa and SOC
data. Especially the ECas values were higher in the DD plots. The normalized ECa difference
(ΔECa), calculated as the difference between the normalized ECas and ECad values,
distinguished clearly the CT and DD plots, with the DD plots showing positive ΔECa values
and CT plots ΔECa negative values. The field was stratified using fuzzy k-means (FKM)
classification of ΔECa (FKM1), and ECas and ECad (FKM2). The FKM1 map mainly
showed the difference between CT and DD plots, while the FKM2 map showed
both differences between CT and DD and topography-associated features. Using
the FKM1 and FKM2 maps as secondary information accounted for 30% of the
total SOC variability, whereas plot and management average SOC explained 44
and 41%, respectively. Cross validation of SKlm using FKM2 reduced the RMSE
by 8% and increased the efficiency index almost 70% as compared to Ordinary
Kriging. This work shows how ECa can improve the spatial characterization of
SOC, despite its low correlation and the small size of the plots used in this study. |
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