Summary Sustainable agriculture practices are often hampered by the prohibitive costs associated with the generation of fine‐resolution soil maps. Recently, several papers have been published highlighting how visible and near infrared (vis–NIR) reflectance spectroscopy may offer an alternative to address this problem by increasing the density of soil sampling and by reducing the number of conventional laboratory analyses needed. However, for farm‐scale soil mapping, previous studies rarely focused on sample optimization for the calibration of vis–NIR models or on robust modelling of the spatial variation of soil properties predicted by vis–NIR spectroscopy. In the present study, we used soil vis–NIR spectroscopy models optimized in terms of both number of calibration samples and accuracy for high‐resolution robust farm‐scale soil mapping and addressed some of the most common pitfalls identified in previous research. We collected 910 samples from 458 locations at two depths (A, 0–0.20 m; B, 0.80–1.0 m) in the state of São Paulo, Brazil. All soil samples were analysed by conventional methods and scanned in the vis–NIR spectral range. With the vis–NIR spectra only, we inferred statistically the optimal set size and the best samples with which to calibrate vis–NIR models. The calibrated vis–NIR models were validated and used to predict soil properties for the rest of the samples. The prediction error of the spectroscopic model was propagated through the spatial analysis, in which robust block kriging was used to predict particle‐size fractions and exchangeable calcium content for each depth. The results indicated that statistical selection of the calibration samples based on vis–NIR spectra considerably decreased the need for conventional chemical analysis for a given level of mapping accuracy. The methods tested in this research were developed and implemented using open‐source software. All codes and data are provided for reproducible research purposes. Highlights Vis–NIR spectroscopy enables an increase in sampling density with little additional cost. Guided selection of vis–NIR calibration samples reduced the need for conventional soil analysis. Error of spectroscopic model prediction was propagated by spatial analysis. Maps from the vis–NIR augmented dataset were almost as accurate as those from conventional soil analysis.

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Soil classification is important to organize the knowledge of soil characteristics. Spectroscopy has increased in the last years as a technique for descriptive and quantitative evaluation of soils. Thus, our objective was to assess qualitative and quantitative methods on soil classification, based on model profiles. Soils in different environments in the Roraima state, Brazil, were evaluated and represented by 16 profiles, providing 109 soil samples, which were analyzed for particle size distribution, chemical attributes and spectral measurement. Visible-near infrared spectra (350–2500 nm) of soil samples were interpreted in terms of intensity, shape and features. The soil color obtained using a spectroradiometer and a colorimeter, and by a soil expert was compared. Descriptive and qualitative analyses were performed for all spectra of the soil profile samples. The descriptive evaluations of the spectral curves from all horizons of the same profile were used to identify the diagnostic attributes and assign a profile to a taxonomic class. This was possible because spectra of samples had specific shapes, features and intensities that combined to present a specific signature. The Outil Statistique d’Aide à la Cartogénèse Automatique and cluster quantitative analyses could not correctly group similar soil classes and they still need to be improved in order to extract all the variability of the spectral data to discriminate soil classes. Soil color quantification by the Munsell system using both equipments showed greater R2 and lower error than that achieved by a soil expert, due to influences of subjectivity inherent in human assessments. Based on this specific case, it was clear that the automatic system may be more consistent than the pedologist’s visual method. Future studies should focus on the development of an online tool that integrates a descriptive approach and spectral information of a given soil profile to determine its probable taxonomic class.