Three-dimensional digital soil mapping of agricultural fields by integration of multiple proximal sensor data obtained from different sensing methods

Three-dimensional digital soil mapping of agricultural fields by integration of multiple proximal... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Three-dimensional digital soil mapping of agricultural fields by integration of multiple proximal sensor data obtained from different sensing methods

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Publisher
Springer US
Copyright
Copyright © 2014 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-014-9381-6
Publisher site
See Article on Publisher Site

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.

Journal

Precision AgricultureSpringer Journals

Published: Oct 7, 2014

References

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