ABSTRACT Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soil-landscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification: medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area.

Técnicas de mapeamento digital podem contribuir para agilizar a realização de levantamentos pedológicos detalhados. Objetivou-se com este trabalho obter um mapa digital de solos (MDS) com uso de redes neurais artificiais (RNA), utilizando correlações entre unidades de mapeamento (UM) e covariáveis ambientais. A área utilizada compreendeu aproximadamente 12.000 ha localizados no município de Barra Bonita, SP. A partir do resultado de uma análise de agrupamento das covariáveis ambientais, foram escolhidas cinco áreas de referência para realizar o mapeamento convencional. As UM identificadas subsidiaram a aplicação da técnica de RNA. Utilizaram-se o simulador de redes neurais JavaNNS e o algoritmo de aprendizado backpropagation. Pontos de referência foram coletados para avaliar o desempenho do mapa digital produzido. A posição na paisagem e o material de origem subjacente foram determinantes para o reconhecimento dos delineamentos das UM. Houve boa concordância entre as UM delineadas pelo MDS e pelo método convencional. A comparação entre os pontos de referência e o mapa de solos digital evidenciou exatidão de 72 %. O uso da abordagem MDS utilizada pode contribuir para diminuir a falta de informações semidetalhadas de solos em locais ainda não mapeados, tomando-se como base informações pedológicas obtidas de áreas de referência adjacentes.

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.

O presente trabalho utilizou ferramentas de sensoriamento remoto e sistema de informação geográfica, aliadas às informações espectrais do solo e quantitativas e qualitativas do relevo, para a caracterização e discriminação das classes de solos desenvolvidos do Grupo Barreiras na região do município de Porto Grande, Amapá. Após o mapeamento semidetalhado de solos, realizou-se a caracterização pontual das amostras dos perfis por meio de espectroscopia de visível e infravermelho (400-2500 nm), e a análise espacial das redes de drenagem e relevo a partir de fotos aéreas e imagens de radar. As informações quantitativas do relevo foram mais eficientes na caracterização e discriminação dos solos estudados do que as qualitativas, enquanto as informações espectrais permitiram caracterizar os solos em nível pontual. Métodos espaciais utilizando redes de drenagem e relevo e métodos espectrais utilizando amostras pontuais complementam-se na caracterização e discriminação de solos na paisagem.

This study applied remote sensing and geographic information system tools, along with soil spectral data and quantitative and qualitative relief information, to characterize and separate soil classes developed over the Barreiras Group in the region of Porto Grande, state of Amapá, Brazil. After mapping soils at the semi-detailed level, soil samples from individual profiles were characterized using visible and infrared (400-2500 nm) spectroscopy, and the drainage system and relief were analyzed spatially based on aerial photographs and radar imagery. Quantitative relief information was more efficient than qualitative ones to characterize and separate soils, whereas the soil spectral data allowed the characterization of soil profiles individually. The spatial assessment of the drainage system and relief, and the spectral assessment of individual soil profiles are complementary to characterize and separate soils on the landscape.