Título:	Mapping soil cation exchange capacity in a semiarid region through predictive models and covariates from remote sensing data.
Autoria:	CHAGAS, C. da S. CARVALHO JUNIOR, W. de PINHEIRO, H. S. K. XAVIER, P. A. M. BHERING, S. B. PEREIRA, N. R. CALDERANO FILHO, B.
Afiliação:	CESAR DA SILVA CHAGAS, CNPS; WALDIR DE CARVALHO JUNIOR, CNPS; Helena Saraiva Koenow Pinheiro, Universidade Federal Rural do Rio de Janeiro; Pedro Armentano Mudado Xavier, Universidade Federal Rural do Rio de Janeiro; SILVIO BARGE BHERING, CNPS; NILSON RENDEIRO PEREIRA, CNPS; BRAZ CALDERANO FILHO, CNPS.
Ano de publicação:	2018
Referência:	Revista Brasileira de Ciência do Solo, v. 42, article e0170183, 2018.
Conteúdo:	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 covariates used to predict CEC consist of images from Landsat 5 TM and a legacy soil map (scale 1:10,000). The sample set comprises 499 samples from the topsoil layer (0.00-0.20 m), where 375 samples were used in training processes and 124 as validation samples. The cokriging model (R2= 0.57 and RMSE = 7.22 cmol c kg-1) performed better in predicting CEC than the random forest model (R2= 0.47 and RMSE = 7.89 cmol c kg-1). The approach used showed potential for estimating CEC content in the Brazilian semiarid region by using covariates obtained from orbital remote sensing and the legacy soil map.
Thesagro:	Solo Levantamento Reconhecimento do Solo
NAL Thesaurus:	Soil surveys
Palavras-chave:	Mineração de dados Geoestatística Landsat 5
Digital Object Identifier:	10.1590/18069657rbcs20170183
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPS)