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

• Autores: Soca, R., Rojas, J., Willems, B. L., Ocola, L. C., Fernández, Ranulfo, Carlos Pérez, Juan Carlos
• Formato: artículo
• Fecha de Publicación: 2017
• Institución: Universidad Nacional Mayor de San Marcos
• Repositorio: Revistas - Universidad Nacional Mayor de San Marcos
• Lenguaje: español
• OAI Identifier: oai:ojs.csi.unmsm:article/13550
• Enlace del recurso: https://revistasinvestigacion.unmsm.edu.pe/index.php/fisica/article/view/13550
• Nivel de acceso: acceso abierto
• Materia: Salinidad del suelo; NDVI; conductividad eléctrica del suelo; imágenes de satélite; Salinity; Electrical conductivity; 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, 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.