Efficient soil management practices depend on the spatial distribution of soil properties which varies significantly even within the same field. Considering that it is impossible for any monitoring technique to provide spatially continuous data, spatial interpolation plays an indispensable role in estimating the missing values where no actual value was measured. The objective of this study was to evaluate various interpolation techniques for the estimation of selected soil chemical properties in a study area located about 85km to the north west of Cairo, Egypt. The studied soil properties included soil salinity, available phosphorus and nitrogen. The interpolation techniques included two commonly used techniques namely, Ordinary Kriging (OK) and Inverse Distance Weighting (IDW). The Artificial Neural Networks (ANN) method, which is considered a somewhat new approach was also evaluated. Soil samples were collected at approximately 200×200 m grids at 0-25 cm depth. The cross-validation method was used for evaluating the selected methods utilizing root mean square error (RMSE) and mean relative error (MRE). This study revealed that ANN had the highest accuracy followed by OK then IDW in terms of both RMSE and MRE when interpolating the studied soil properties. Nevertheless, these results are dependent on the accuracy of the designed network which must have an overall accuracy of coefficient of correlation (R) more than 0.80 between the predicted and the actual data. It also revealed that the best IDW with the highest accuracy must have a power of 2 for salinity and nitrogen and a power of 3 for phosphorus.
- Artificial neural networks
- Inverse distance weighting
- Ordinary kriging
- Soil properties interpolation