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.
Highlights
Agriculture is considered one of the foundations of the Brazilian economy, providing employment and increasing foreign exchange reserves
Soil profiles The profiles were placed in different landscape positions, with elevation ranging from 438 to 781 m (Figure 2)
Those located on the east side of the study area in the highest places are derived from basaltic rocks
Summary
Agriculture is considered one of the foundations of the Brazilian economy, providing employment and increasing foreign exchange reserves. Soil is one of the most important constituents of the environment. Knowing the soil properties and their spatial variability is essential for implementation of any management technique (Bhatti et al, 1991). Soil maps are one of the most used sources of information for evaluating soil spatial variability and, when available at an appropriate scale, they allow the user to identify physical, chemical and morphological properties. These maps aid in the decision-making process for agricultural planning, e.g., indicating sites with periodic flooding or variations in soil depth
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