Bayesian Maximum Entropy prediction of soil categories using a traditional soil map as soft information.

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Bayesian Maximum Entropy was used to estimate the probabilities of occurrence of soil categories in the Netherlands, and to simulate realizations from the associated multi-point pdf. Besides the hard observations (H) of the categories at 8369 locations, the soil map of the Netherlands 1:50 000 was used as soft information (S). The category with the maximum estimated probability was used as the predicted category. The quality of the resulting BME(HS)-map was compared with that of the BME(H)-map obtained by using only the hard data in BME-estimation, and with the existing soil map. Validation with a probability sample showed that the use of the soft information in BME-estimation leads to a considerable and significant increase of map purity by 15%. This increase of map purity was due to the high purity of the existing soil map (71.3%). The purity of the BME(HS) was only slightly larger than that of the existing soil map. This was due to the small correlation length of the soil categories. The theoretical purity of the BME-maps overestimated the actual map purity, which can be partly explained by the biased estimates of the one-point bivariate probabilities of hard and soft categories of the same label. Part of the hard data is collected to describe characteristic soil profiles of the map units which explains the bias. Therefore, care must be taken when using the purposively selected data in soil information systems for calibrating the probability model. It is concluded that BME is a valuable method for spatial prediction and simulation of soil categories when the number of categories is rather small (say <10). For larger numbers of categories, the computational burden becomes prohibitive, and large samples are needed for calibration of the probability model.
Original languageEnglish
Pages (from-to)166-177
Number of pages12
JournalEuropean Journal of Soil Science
Issue number2
Publication statusPublished - 2008


  • spatial prediction
  • variables


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