Spatial soil data applications require sound geospatial data including coordinates and a coordinate reference system. However, when it comes to legacy soil data we frequently find them to be missing or incorrect. This paper assesses the quality of the geospatial data of legacy soil observations in Brazil, and evaluates geospatial data sources (survey reports, maps, spatial data infrastructures, web mapping services) and expert knowledge as a means to fix inconsistencies. The analyses included several consistency checks performed on 6,195 observations from the Brazilian Soil Information System. The positional accuracy of geospatial data sources was estimated so as to obtain an indication of the quality for fixing inconsistencies. The coordinates of 20 soil observations, estimated using the web mapping service, were validated with the true coordinates measured in the field. Overall, inconsistencies of different types and magnitudes were found in half of the observations, causing mild to severe misplacements. The involuntary substitution of symbols and numeric characters with similar appearance when recording geospatial data was the most common typing mistake. Among the geospatial data sources, the web mapping service was the most useful, due to operational advantages and lower positional error (~6 m). However, the quality of the description of the observation location controls the accuracy of estimated coordinates. Thus, the error of coordinates estimated using the web mapping service ranged between 30 and 1000 m. This is equivalent to coordinates measured from arc-seconds to arc-minutes, respectively. Under this scenario, the feedback from soil survey experts is crucial to improving the quality of geospatial data.

The objective of this work was to test the extrapolation of soil-landscape relationships in a reference area (RA) to a topographic map (scale 1:50,000), using digital soil mapping (DSM), and to compare these results to those obtained in similar studies previously conducted in Brazil. A soil survey in a 10 km² RA, using conventional mapping techniques (scale 1:10,000), was made in order to map a 678 km² physiographically similar area (scale 1:50,000) using DSM. The decision tree technique was employed to build a predictive extrapolation model based on soil classes and eight terrain attributes in the RA. The validation of DSM by application of field observation points resulted in a 66.1% global accuracy and in 0.36 kappa index. The most representative soils in the area were correctly predicted, whereas the less representative and less frequent soils in the landscape (and consequently with reduced sampling) had their prediction compromised. The RA proportion, which equals 1.5% of the total area, is a limiting factor in the formulation of soil-landscape relationships to precisely represent the mapped area by DSM.

O uso do solo fora de sua aptidão agrícola é o que norteia a degradação dos recursos naturais por meio das atividades antrópicas. As atividades agropecuárias estão entre as que mais perturbam o meio ambiente, expondo o solo à ação dos processos erosivos e acelerando a transferência de sedimentos aos corpos de água. Nos últimos anos, o método fingerprinting para identificação de fontes de sedimentos tem sido utilizado com sucesso no mundo, porém, trabalhos dessa natureza realizados no Brasil ainda estão em caráter incipiente. O objetivo deste trabalho foi estimar a proporção de contribuição das principais fontes de produção de sedimentos de uma bacia hidrográfica de encosta, onde predominam solos rasos e agricultura menos intensiva no sistema familiar. Essa bacia está localizada na região central do Estado do Rio Grande do Sul, tendo por ocupação do solo predominante áreas de floresta nativa seguida das de campo nativo, capoeira, lavouras anuais e, em menor expressão, silvicultura (eucalipto) e área urbanizada. Para a identificação das fontes, foi utilizado o método fingerprinting, que compara os solos de diferentes fontes, e os sedimentos, que são encontrados em suspensão no canal de drenagem, usando elementos traçadores. As maiores contribuições na produção de sedimentos são provenientes da erosão superficial do solo. Para o primeiro período de monitoramento, foi identificada uma produção relativa de sedimentos de 60 % das estradas, para o ponto T2; de 100 % da malha, para o T3; e de 77 % da malha, para o T4. Para o segundo período de monitoramento, as estradas contribuíram relativamente com 81 % do sedimento em suspensão no ponto T2 e com 76 %, no T3. Já no ponto T4, a maior contribuição foi da malha com 82 %. O método se apresentou sensível em detectar mudanças na contribuição de cada fonte em razão das características apresentadas por cada uma delas, para o tipo de bacia hidrográfica estudada.

Digital information generates the possibility of a high degree of redundancy in the data available for fitting predictive models used for Digital Soil Mapping (DSM). Among these models, the Decision Tree (DT) technique has been increasingly applied due to its capacity of dealing with large datasets. The purpose of this study was to evaluate the impact of the data volume used to generate the DT models on the quality of soil maps. An area of 889.33 km² was chosen in the Northern region of the State of Rio Grande do Sul. The soil-landscape relationship was obtained from reambulation of the studied area and the alignment of the units in the 1:50,000 scale topographic mapping. Six predictive covariates linked to the factors soil formation, relief and organisms, together with data sets of 1, 3, 5, 10, 15, 20 and 25 % of the total data volume, were used to generate the predictive DT models in the data mining program Waikato Environment for Knowledge Analysis (WEKA). In this study, sample densities below 5 % resulted in models with lower power of capturing the complexity of the spatial distribution of the soil in the study area. The relation between the data volume to be handled and the predictive capacity of the models was best for samples between 5 and 15 %. For the models based on these sample densities, the collected field data indicated an accuracy of predictive mapping close to 70 %.