Soil salinity mapping using spatio-temporal kriging and Bayesian maximum entropy with interval soft data

The methods of prediction were compared quantitatively by mean error (ME) and mean squared error (MSE). The errors are the differences between the measured electrical conductivity in the laboratory on samples from March and June 2001 (which were not used in previous computations) and their cross-validation estimates. The MSE was divided further into three components revealing different aspects of the discrepancy between the observed and the estimated values of electrical conductivity. The BME predictions were less biased and more accurate than those from kriging. The MSE decomposition showed that the kriging with soft data (HSK) provided more biased estimates and failed to reproduce the magnitude of fluctuation in the observed soil salinity. The difference in incorporating soft data into the analysis was confirmed and was more acute when only the largest intervals from the soft data were used. In this situation BME produced very reliable estimates whereas HSK failed to predict soil salinity accurately.