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Forskningsartikel2014Vetenskapligt granskad

Constructing a layered electrical conductivity model using k nearest-neighbour predictions and a combination of two proximal sensors

Piikki, Kristin; Wetterlind, Johanna; Söderström, Mats; Stenberg, Bo


A strategy to produce layered high-resolution apparent electrical conductivity (ECa) models of agricultural fields by distance-weighted k nearest-neighbour prediction (kNN) was tested at three farms. Electromagnetic induction (EMI) measurements were combined with measurements made with a dipole probe. Depth-layer-specific ECa values from the probe measurements were interpolated in the attribute space of the EMI measurements with the distance-weighted kNN method. This analysis resulted in high-resolution ECa maps for depth intervals of 0-0.2 and 0.4-0.6 or 0.4-0.8 m. The ECa values measured with the dipole probe ranged between 6.1 and 40.2 mS m(-1), and at two of the three farms investigated it was possible to create ECa maps at two depths with mean absolute errors of 1.1-3.8 mS m(-1). At the third farm the predictions were less accurate. Combining data from two fundamentally different sensors of ECa for kNN predictions was deemed to be an efficient way to produce 3-D information on arable soil. However, it seems to be essential that the dipole probe and the EMI measurements are made under similar conditions.

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European Journal of Soil Science
2014, Volym: 65, nummer: 6, sidor: 816-826