How qualitative spectral information can improve soil profile classification?. Issue 2 (April 2019)

• Record Type: Journal Article
• Authors: Marques, Karina PP; Rizzo, Rodnei; Carnieletto Dotto, André; Souza, Arnaldo Barros e; Mello, Fellipe AO; Neto, Luiz GM; Anjos, Lúcia Helena C dos; Demattê, José AM
• Is Part Of: Journal of near infrared spectroscopy. Volume 27:Issue 2(2019)
• Page Start: 156
• Page End: 174
• Publication Date: 2019-04
• Subjects: NIR -- soil classification -- Munsell color system -- spectroradiometer -- colorimeter -- expert; Near infrared spectroscopy -- Periodicals; 543.5
• DOI: 10.1177/0967033518821965
• Languages: English
• ISSNs: 0967-0335

Abstract

Soil classification is important to organize the knowledge of soil characteristics. Spectroscopy has increased in the last years as a technique for descriptive and quantitative evaluation of soils. Thus, our objective was to assess qualitative and quantitative methods on soil classification, based on model profiles. Soils in different environments in the Roraima state, Brazil, were evaluated and represented by 16 profiles, providing 109 soil samples, which were analyzed for particle size distribution, chemical attributes and spectral measurement. Visible-near infrared spectra (350–2500 nm) of soil samples were interpreted in terms of intensity, shape and features. The soil color obtained using a spectroradiometer and a colorimeter, and by a soil expert was compared. Descriptive and qualitative analyses were performed for all spectra of the soil profile samples. The descriptive evaluations of the spectral curves from all horizons of the same profile were used to identify the diagnostic attributes and assign a profile to a taxonomic class. This was possible because spectra of samples had specific shapes, features and intensities that combined to present a specific signature. The Outil Statistique d'Aide à la Cartogénèse Automatique and cluster quantitative analyses could not correctly group similar soil classes and they still need to be improved in order to extract all the variability of the spectral data to discriminate soil classes. Soil color quantification by the Munsell system using both equipments showed greater R 2 and lower error than that achieved by a soil expert, due to influences of subjectivity inherent in human assessments. Based on this specific case, it was clear that the automatic system may be more consistent than the pedologist's visual method. Future studies should focus on the development of an online tool that integrates a descriptive approach and spectral information of a given soil profile to determine its probable taxonomic class.