Airborne hyperspectral remote sensing applied to determine the texture of a Cambisol in the Chapada do Apodi, Ceará

• Autor(a) principal: Almeida,Eurileny Lucas de
• Data de Publicação: 2021
• Outros Autores: Oliveira,Marcio Regys Rabelo de, Rocha Neto,Odílio Coimbra da, Moreira,Luís Clenio Jário, Teixeira,Adunias dos Santos
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista ciência agronômica (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902021000200404
• topic: Precision agriculture; Reflectance spectroradiometry; SpecTIR-VS sensor

ABSTRACT Texture is of great importance in soil management, as it strongly influences the physical, hydraulic and chemical behaviour of soils. It is therefore necessary to determine texture, with the spectral band ratio being a fast and precise alternative method for this purpose. The aim of this study was to evaluate the use of spectral data, acquired by the ProSpecTIR-VS airborne sensor with a spatial resolution of 1 m, in selecting two bands for building a Normalised Difference Index that allows the textural attributes of the soil to be estimated, besides preparing a texture map of the soil in the image. Sixty-four samples were collected from several areas inserted in the Jaguaribe-Apodi irrigated perimeter, which is located in the Chapada do Apodi, in the Lower Jaguaribe Basin, where the predominant soil classes are Cambisols. The samples were collected from exposed soil, based on the hyperspectral images of the ProSpecTIR-VS airborne sensor. The Normalised Difference Index (NDI) was constructed, carrying out all possible normalised band ratios, with the best indices selected based on the coefficient of determination (R²). The most promising results for R² were obtained when estimating sand in the 1045 and 1323 nm bands, with an R² of 0.5. The low values ​​for R² can be explained by interference in the spectral response from materials on the soil surface, such as crop residue, gravel and vegetation. Preparing the sand map using the best model resulted in 82.1% of the pixels having values ​​between 20 and 60% sand, falling between the minimum and maximum sand content of the soil samples.