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	Provedor de dados Scientia Agricola (1) Rev. Bras. Ciênc. Solo (1) Autor Bhering,Silvio Barge (1) Calderano Filho,Braz (1) Carvalho Júnior,Waldir de (1) Chagas,César da Silva (1) Curi,Nilton (1) Machado,Diego Fernandes Terra (1) Menezes,Michele Duarte de (1) Pereira,Nilson Rendeiro (1) Pinheiro,Helena Saraiva Koenow (1) Silva,Sérgio Henrique Godinho (1) Xavier,Pedro Armentano Mudado (1) Mais... Palavra-chave Legacy data (2) Soil survey (2) Data mining (1) Digital soil mapping (1) Geostatistics (1) Landsat 5 (1) Mais... Tipo do documento Info:eu-repo/semantics/article (2) Ano 2019 (1) 2018 (1) País Brazil (2) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Data Rev. Bras. Ciênc. Solo Chagas,César da Silva; Carvalho Júnior,Waldir de; Pinheiro,Helena Saraiva Koenow; Xavier,Pedro Armentano Mudado; Bhering,Silvio Barge; Pereira,Nilson Rendeiro; Calderano Filho,Braz. ABSTRACT: Planning sustainable use of land resources requires reliable information about spatial distribution of soil physical and chemical properties related to environmental processes and ecosystemic functions. In this context, cation exchange capacity (CEC) is a fundamental soil quality indicator; however, it takes money and time to obtain this data. Although many studies have been conducted to spatially quantify soil properties on various scales and in different environments, not much is known about interactions between soil properties and environmental covariates in the Brazilian semiarid region. The goal of this study was to evaluate the efficiency of random forest and cokriging models applied to predict CEC in the Brazilian semiarid region. The... Tipo: Info:eu-repo/semantics/article Palavras-chave: Data mining; Geostatistics; Landsat 5; Legacy data; Soil survey. Ano: 2018 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100311 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 Registros recuperados: 2 Primeira ... 1 ... Última

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