Identification of degraded soils by salinity in sugarcane farming through satellite images

In this work, we identify soil degradation by salinity, using images of HRG-2 (SPOT), TM and ETM+ (LANDSAT) of high spatial resolution in the sugarcane crops of Empresa Agroindustrial de Pomalca, located in the Lambayeque Region at the north coast of Peru. The reflectance of the surface soil, normali...

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Detalhes bibliográficos
Autores: Soca, R., Rojas, J., Willems, B. L., Ocola, L. C., Fernández, Ranulfo, Carlos Pérez, Juan Carlos
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2017
País:Perú
Recursos:Universidad Nacional Mayor de San Marcos
Repositório:Revistas - Universidad Nacional Mayor de San Marcos
Idioma:espanhol
OAI Identifier:oai:revistasinvestigacion.unmsm.edu.pe:article/13550
Acesso em linha:https://revistasinvestigacion.unmsm.edu.pe/index.php/fisica/article/view/13550
Access Level:Acceso aberto
Palavra-chave:Salinidad del suelo
NDVI
conductividad eléctrica del suelo
imágenes de satélite
Salinity
Electrical conductivity
satellite images
Descrição
Resumo:In this work, we identify soil degradation by salinity, using images of HRG-2 (SPOT), TM and ETM+ (LANDSAT) of high spatial resolution in the sugarcane crops of Empresa Agroindustrial de Pomalca, located in the Lambayeque Region at the north coast of Peru. The reflectance of the surface soil, normalized difference vegetation index, NDVI, and salinity index, IndSal, was estimated from the satellite images using the image processing software ENVI 4.5, and the programming language IDL.Also, the maximum compound values of the NDVI and IndSal was determined from the TM and ETM+ images to identify soils with low farming quality and degraded soils by salinity. To estimate the salinity in the sugarcane farming, we performed, by graphics, the correlation between the soil electrical conductivity, CE, and the spectral reflectance values extracted from the B1, B2, B3 and B4 (TM-20/04/2008) bands images. In this way, we applied the linear simple and multiple regression models. The correlation maximum value obtained was R=7.3 which was used to generate the topic map of the soil salinity spatial distribution.