Digital mapping techniques can help reduce the lack of soil information in areas where no 1st and 2nd order soil surveys were performed. The aim of this study was to obtain a digital soil map (DSM) by artificial neural networks (ANN) using the correlation between soil mapping units and environmental covariates. The study area of approximately 11,000 ha is located in Barra Bonita, SP, Brazil. Based on a cluster analysis of environmental covariates, five reference areas were chosen for conventional mapping. The selected soil mapping units supported the application of ANN. We used the neural network simulator JavaNNS and the backpropagation learning algorithm. Reference points were collected to evaluate the efficiency of the resulting digital map. The position in the landscape and the underlying parent material were fundamental to the recognition of the designs of the mapping units. There was good agreement between the mapping units delineated by DSM and the conventional method. The comparison between the reference points and the digital soil map showed an accuracy of 72 %. The use of the DSM approach can help reduce the lack of soil information in unmapped places, based on soil information obtained from adjacent reference areas.
artificial intelligence; environmental covariates; supervised classification
