Abordagens semiquantitativa e quantitativa na avaliação da textura do solo por espectroscopia de reflectância bidirecional no VIS-NIR-SWIR

Abstract

Resumo -O objetivo deste trabalho foi avaliar o potencial da espectroscopia de reflectância no VIS-NIR-SWIR, para a caracterização granulométrica de amostras de solos de diferentes classes texturais, e obter modelos de predição dos teores de argila, silte e areia no solo. Utilizou-se um conjunto de amostras representativas de Latossolos e Argissolo de cinco locais do Estado do Mato Grosso do Sul. Os espectros do visível e do infravermelho próximo ao infravermelho de ondas curtas (de 350 a 2.500 nm) das amostras foram obtidos e analisados. Empregaram-se a análise de componentes principais (ACP), agrupamento por "fuzzy c-means", regressão logística multinomial (RLM) e regressão por mínimos quadrados parciais. Espectros característicos para as diferentes classes texturais e a segregação de amostras de classes texturais e de locais de coleta com características distintas, por meio da ACP, "fuzzy c-means" e RLM, mostram o potencial semiquantitativo dos dados de reflectância no VIS-NIR-SWIR. Obteve-se quantificação satisfatória quanto à argila (R²=0,92, RPD=3,59), ao silte (R²=0,80, RPD=2,15) e à areia (R²=0,87, RPD=2,62). As técnicas de espectroscopia de reflectância podem auxiliar na determinação da textura e da variabilidade espacial do solo com metodologias semiquantitativas ou quantitativas.Termos para indexação: granulometria do solo, espectroscopia de reflectância, estatística multivariada, pedometria, sensoriamento próximo. Semiquantitative and quantitative approaches for soil texture evaluation through VIS-NIR-SWIR bidirectional reflectance spectroscopyAbstract -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.

Pedological mapping through integration of digital terrain models spectral sensing and photopedology

“…The medium texture was subdivided into medium clayey and medium sandy for evaluation purposes, a procedure that has been used in soil management ). Franceschini et al (2013) also reported significant differences among soils with different textures in laboratory and with hyperspectral sensing (FRANCESCHINI et al, 2015). Soil texture prediction through spectrum by terrestrial platform has shown to be more efficient (ARAÚJO et al, 2014) reaching R² 0.82.…”

Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

“…This program offers several options for data treatment to optimize the PCA. Various treatments were tested and the sequence that better featured the results in the transformation, in accordance to Franceschini et al [36], as follows: first, spectral transformation of reflectance data for absorbance; second, spectral data processing by SNV (Standard Normal Variate); third, transformation of spectral data by MSC (Multiple Scattering Correction); and, fourth, data smoothing by applying the Savitzky-Golay filter with second-order polynomial.…”

Evaluación De Textura Del Suelo Con Espectroscopía De Infrarrojo Cercano en Un Oxisol De Colombia

“…Franceschini et al (2013) señalan que para arcilla y arena se presentan picos en la región de 1200 nm, 1900 nm y 2200 nm debido a la presencia de filosilicatos (como caolinita) y gibsita para oxisoles y ultisoles en la región de Mato Grosso, Brasil. Para esta investigación, en esta región del espectro se manifiesta la caolinita.…”