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

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SummaryA 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.