Bayesian data fusion for combining maps of predicted soil classes: A case study using legacy soil profiles and DEM covariates in Iran

This paper tackles the issue of spatially predicting soil classes by combining at best soil information coming directly from legacy soil profiles with soil information indirectly obtained from spatial covariates. Based on Multinomial Logistic Regression (MLR) and Bayesian Maximum Entropy (BME) models, we first show that prediction models easily lead to very different soil maps while having at the same time quite comparable global performances. By relying afterwards on a Bayesian data fusion (BDF) approach, we emphasize the benefit of combining the output of these two prediction models in order to get a single final map that combines the major spatial features of the MLR and BME maps while at the same time improving the quality of the predictions. The advocated methodology is illustrated with the mapping of World Reference Base (WRB) soil classes over a 10,480 km(2) area located in Iran. A set of 390 soil profiles allowed us to assign the WRB soil classes at these locations. In parallel, a set of potentially related covariates were computed from a 90 m resolution digital elevation model. Using MLR and BME models, predictions were obtained separately at the nodes of a 90 m resolution grid. Even if the performances of the MLR and BME models compare well, it is shown that a BDF procedure that combines both results yields improved performances, with spatial features that are a balanced combination of those found separately on the MLR and BME maps. These results emphasize the benefit of data fusion in order to improve the quality of the final map. Though the study was conducted here using MLR and BME models for predicting WRB soil classes, we believe this methodology and the corresponding findings are relevant when it comes to handle the results of spatial prediction models that are making use of distinct information sources in other soil science mapping contexts.