Papers

Summary

The demand for information on the soil resource to support the establishment of public policies for land use and management has grown exponentially in the last years. However, there are still difficulties to the proper use of already existing information for soil mapping. Here we aimed to establish a protocol for soil mapping using legacy data, magnetic signature and soil attributes evaluation. A total of 493 soil samples were collected at 0–0.20 m in the geological domain of Western Plateau of São Paulo State. This work has three parts: First, we performed a classification analysis using soil mapping units (SMU) extracted from conventional soil map and Support Vector Machines algorithm (SVM). As covariates, we used categorical information, such as geology, dissection and landform maps. Second, we used soil attributes to perform a cluster analysis using k-means as partitioning method. To choose the optimal number of clusters, the same number of SMU showed in the conventional soil map (e.g. 34 clusters) were used. The last step was to compare soil and clusters maps predicted by SVM with the conventional soil map. Results showed good performance of SVM for both classifications (clusters and SMU), with overall accuracy of 0.60 and 0.90 respectively. In addition, the distribution of soil attributes within each cluster was more homogeneous and well distributed than within SMU, showing that is very possible to use numerical classification for soil mapping. Future soil surveys could use cluster analysis as a preliminary evaluation for better understanding of tropical soil variations.