Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Abstract

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

“…In subsurface layers, there is more evidence of absorption bands within 1400 -1900 nm because of the OH -molecule of soil hygroscopic water (Ben-Dor, 2002) and the presence of 2:1 minerals (Demattê et al, 2004a). Thus, our results showed types of curves that differentiate soil samples from surface and subsurface layers, corroborating Demattê et al (2004a), Rizzo et al (2014) and Vasques et al (2014).…”

Morphological Interpretation of Reflectance Spectrum (MIRS) using libraries looking towards soil classification

“…Sherman and Waite (1985) for example, stress the differences between goethite and hematite, while Madeira Netto (1996) delineates the shape of gibbsite. Despite the descriptive information as used by Bellinaso et al (2010), quantitative studies play an important role, together with spectral library patterns, as pointed out by Shepherd and Walsh (2002) and Rizzo et al (2014). But still, how can a user, with spectra in hand arrive at the classification of a soil?…”

How qualitative spectral information can improve soil profile classification?

“…The authors evaluated a spectrum of each horizon from the profile to reach a single soil classification. Although quantitative information attempts to express the spectra in numbers to facilitate interpretation, as performed by Rizzo et al., 11 this result loses some valuable information about soil characteristics.…”