Spatial variety of soil properties by BP neural network ensemble

A 7.7 hectare silage field at Hayes, Northern Ireland, UK, was selected for the study, the samples were divided into training and validation dataum sets, several sampling distributions were designed based on the whole training sample distribution. Compared with the Root Mean Square Error (RMSE) achieved by Kriging, the accuracy achieved by BP neural network ensemble was very near or even better. While the interval between samples enlarged, the accuracy by BP neural network ensemble exceeded the accuracy achieved by Kriging. And one of the advantages was no statistical inference to samples for neural network ensemble. The potential ability of neural network ensemble was also discussed.