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Soil type spatial prediction from Random Forest: different training datasets, transferability, accuracy and uncertainty assessment Scientia Agricola
Machado,Diego Fernandes Terra; Silva,Sérgio Henrique Godinho; Curi,Nilton; Menezes,Michele Duarte de.
ABSTRACT: Different uses of soil legacy data such as training dataset as well as the selection of soil environmental covariables could drive the accuracy of machine learning techniques. Thus, this study evaluated the ability of the Random Forest algorithm to predict soil classes from different training datasets and extrapolate such information to a similar area. The following training datasets were extracted from legacy data: a) point data composed of 53 soil samples; b) 30 m buffer around the soil samples, and soil map polygons excluding: c) 20 m; and d) 30 m from the boundaries of polygons. These four datasets were submitted to principal component analysis (PCA) to reduce multidimensionality. Each dataset derived a new one. Different combinations of...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Digital soil mapping; Soil survey; Legacy data.
Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019001300243
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