Multiscalar Geomorphometric Generalization to Delineate Soil Textural Patterns on Amazon Watersheds Landscapes

In the Amazon context, soil information needs to be on a scale compatible with small rural properties, despite the large extension of the region, so that it can be useful for family farmers and technical assistance. The multiscale influence of topography on soil distribution has a complex pattern that is related to the overlapping of pedological processes that occurred at different times and driving forces that are correlated with different scales. In this sense, we tested the hypothesis that generalized geomorphometric covariates at multiple scales, can improve digital soil mapping. In the mapping of textural classes, the Random Forest algorithm was applied in a multiscale geomorphometric database to predict the granulometric percentages on the soil surface. The generalized covariates improved the accuracy of the textural classification, with the Kappa Index increasing from 0.43 to 0.62. It can be argued that topography influences soil distribution at combined coarser spatial scales and is able to predict soil particle size contents in the studied watershed. Therefore, it is concluded that the use of generalized geomorphometric covariates in multiple scales results in greater accuracy of the models, being a flexible method for use in machine learning approaches and others methods based on relief covariables.