Digital soil mapping in the Eastern Amazon: algorithm performance and classification uncertainty in hydromorphic and well-drained landscapes

Context Pedological mapping in the Amazon is essential but highly challenging, particularly in hydromorphic environments where soil properties and boundaries are difficult to delineate. Improving mapping accuracy is critical for sustainable land-use planning and ecosystem management, yet conventional approaches are often time-consuming and costly. Digital soil mapping (DSM) offers a promising alternative by integrating machine learning algorithms with geospatial data to predict soil mapping units. Aims This study evaluates DSM approaches in the Eastern Amazon using legacy soil data, environmental covariates, and multiple machine learning algorithms, with emphasis on classification accuracy and spatial uncertainty. Methods We compared the performance of eight machine learning algorithms for predicting soil mapping units using predictors derived from a digital elevation model, Landsat-9 imagery, and legacy soil maps. A total of 244 spatial sampling units (30 m × 30 m pixels), corresponding to the centroids of soil mapping units and supported by field observations across nine soil classes, were used, with 70% allocated for training and 30% for validation. Recursive Feature Elimination was applied to identify the most relevant predictors. Model performance was assessed using overall accuracy, Kappa index, user’s accuracy, and producer’s accuracy. Key results The random forest implementation algorithm (ranger) achieved the best performance, with an overall accuracy of 0.74 and a Kappa index of 0.71. Other algorithms showed moderate agreement with the conventional pedological map (accuracy = 0.56; Kappa = 0.49), whereas the artificial neural network showed low performance (accuracy = 0.26; Kappa = 0.14). Conclusions Results indicate that the density and spatial configuration of soil mapping units influence the agreement between conventional and digital maps, particularly in hydromorphic environments where classification uncertainty is higher. Implications These findings highlight the potential of decision tree-based algorithms for DSM in complex tropical landscapes, while emphasizing the need for further research to address limitations related to data availability, spatial resolution, and the quality of reference soil maps.