The objective of this work was to evaluate the potential of VIS-NIR-SWIR reflectance spectroscopy for the characterization of soil particle-size distribution of samples from different textural classes, and to obtain models to predict clay, silt, and sand contents in the soil. A representative sample set of Oxisols and Ultisols from five locations in Mato Grosso do Sul state, Brazil, were used. Visible and near-infrared to short-wave infrared (from 350 to 2,500 nm) spectra of the samples were obtained and analyzed. Principal component analysis (PCA), fuzzy c-means cluster analysis, multinomial logistic regression (MLR), and partial least squares regression were used. Characteristic spectra for the different soil texture classes and segregation of samples from texture classes and from sampling sites with distinct characteristics, through PCA, fuzzy c-means, and RLM, show the semiquantitative potential of the VIS-NIR-SWIR reflectance data. Satisfactory quantification was obtained for clay (R²=0.92, RPD=3.59), silt (R²=0.80, RPD=2.15), and sand (R²=0.87, RPD=2.62). The reflectance spectroscopy techniques can help to assess soil texture and soil spacial variability with semiquantitative or quantitative methodologies.
soil granulometric distribution; reflectance spectroscopy; multivariate statistics; pedometrics; proximal sensing
