Mapping soil organic carbon and organic matter fractions by Geographically Weighted Regression

Elias Mendes Costa, Wagner de Souza Tassinari, Helena Saraiva Koenow Pinheiro, Sidinei Julio Beutler, Lúcia Helena Cunha dos Anjos
janeiro, 2018
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Image credit: Elias

Resumo

The soil organic matter (SOM) content and dynamic are related to vegetation cover, climate, relief, and geology; these factors have strong variation in space in the southeastern of Brazil. The objective of the study was to compare and evaluate performance of classical multiple linear regressions (MLR) and geographically weighted regression (GWR) models to predict soil organic carbon (SOC) and chemical fractions of organic matter in the Brazilian southeastern mountainous region. The regression models were fitted based on SOC and chemical fractions of SOM. The points (n = 89) were selected by pedologist’s experience along transects and toposequences. The covariates were also selected using the empirical knowledge of pedologists when choosing variables that drive soil.

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Publicação
In Journal of Environmental Quality, 18(1):1-8
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DSM SOC Geographically Weighted Regression
Elias Mendes Costa
Elias Mendes Costa
Professor EBTT

Meu principal interesse acadêmico-científico-profissional envolve gênese, morfologia, classificação, fertilidade e manejo de solos, levantamento e mapeamento digital de solos, pedologia quantitativa (pedometria), geotecnologias e análise ambiental, modelagem e programação. E mais recentemente experimentação agrícola e desenvolvimento técnico científico com as principais culturas agrícolas do país.