Abstract
The objective of the present study was to evaluate a strategy for three-dimensional (3-D) digital soil mapping on two farms in southwest Sweden. Apparent electrical conductivity (ECa) and gamma radiation data from proximal sensors and laser-scanned elevation data were used as predictors. Depth-integrated ECa measurements from a non-invasive sensor were used directly, but also calibrated against probe sensor ECa measurements to obtain layer-specific values. This allowed the predictive powers of depth-integrated and layer-specific ECa to be compared. Clay and sand fractions, and organic matter content (OM) were modelled for three depth layers by multivariate adaptive regression splines (MARSplines). Clay and sand were consistently better predicted in the topsoil than in the subsoil. MARSplines models based on layer-specific ECa data rather than on depth-integrated ECa data yielded more successful estimations of these soil properties in both subsoil layers (0.4–0.6 and 0.6–0.8 m) on both the farms but this was not always the case in the topsoil. Topsoil OM was better predicted by spatial interpolation of the calibration data than by using MARSplines models with ancillary predictors. In the two subsoil layers, the mapping procedure could not be appropriately tested, because the OM was low and homogeneous. We concluded that a 3-D soil texture map of an agricultural field could be prepared using MARSplines models based on layer-specific ECa values, gamma radiation data and a digital elevation model.
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Abbreviations
- 2-D:
-
Two-dimensional
- 3-D:
-
Three-dimensional
- CI:
-
Confusion index
- E:
-
Modelling efficiency
- ECa:
-
Apparent electrical conductivity
- EMI:
-
Electromagnetic induction
- GPR:
-
Ground penetrating radar
- kNN:
-
k nearest neighbour prediction,
- lidar:
-
Light detection and ranging,
- MAE:
-
Mean absolute error,
- MARSplines:
-
Multivariate adaptive regression splines,
- OM:
-
Organic matter content,
- TC:
-
Total count of radioactive decays,
- vis–NIR:
-
Visible and near infrared
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The study was funded by The Swedish Farmers’ Foundation for Agricultural Research and the Swedish Research Council Formas.
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Piikki, K., Wetterlind, J., Söderström, M. et al. Three-dimensional digital soil mapping of agricultural fields by integration of multiple proximal sensor data obtained from different sensing methods. Precision Agric 16, 29–45 (2015). https://doi.org/10.1007/s11119-014-9381-6
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DOI: https://doi.org/10.1007/s11119-014-9381-6