Soil texture prediction using portable X-ray fluorescence spectrometry and visible near-infrared diffuse reflectance spectroscopy

• Published in: Geoderma Vol. 376; p. 114553
• Main Authors: Benedet, Lucas, Faria, Wilson Missina, Silva, Sérgio Henrique Godinho, Mancini, Marcelo, Demattê, José Alexandre Melo, Guilherme, Luiz Roberto Guimarães, Curi, Nilton
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.10.2020
• Subjects: B horizons; Brazil; clay; fluorescence; normal distribution; Pedometrics; prediction; Proximal sensors; Random forest; reflectance spectroscopy; sand; silt; soil texture; spectral analysis; support vector machines; texture; Tropical soils; X-radiation; X-ray fluorescence spectroscopy; Brazil; Tropical soils; Random forest; Pedometrics; Proximal sensors
• DOI: 10.1016/j.geoderma.2020.114553
• ISSN: 0016-7061

•Prediction of soil texture via pXRF and Vis-NIR DRS data was evaluated.•Combination of A and B horizons data resulted in predictions with R2 above 0.80.•In general, the best predictions were achieved using pXRF data.•RF algorithm outperformed other algorithms for soil texture prediction.•The best prediction models were obtained with pXRF + RF using B horizon data. Portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared diffuse reflectance spectroscopy (Vis-NIR DRS), used separately or in tandem, have become important techniques for determination and prediction of soil attributes worldwide. However, there is little information available regarding the effectiveness of their combined use in tropical soils. This study aimed to predict soil texture using pXRF and Vis-NIR DRS, evaluating the efficiency of using these proximal sensors separately and in tandem. A total of 315 soil samples were collected from A and B horizons in Brazil. Soil samples were submitted to analyses of texture, pXRF and Vis-NIR DRS. Vis-NIR DRS spectral data pre-processing was evaluated by comparing results delivered by the derivative smoothing methods Savitzky-Golay (WT), Savitzky-Golay with Binning (WB), and data without the pre-processing treatment (WOT). Four algorithms were utilized for predictions: Gaussian Process (Gaussian), Support Vector Machine with linear (SVM-L) and radial (SVM-R) kernels, and Random Forest (RF). In general, models using only pXRF data slightly outperformed models using Vis-NIR DRS (WT, WB, WOT) data alone. Models combining data from both sensors achieved similar results to those obtained by pXRF alone. The best predictions of sand, silt, and clay contents were obtained via pXRF + RF using B horizon data, reaching R2 values of 0.91, 0.81, and 0.83, respectively. Although pXRF alone provided slightly better results, soil texture can be accurately predicted via both pXRF and Vis-NIR DRS data, separately and in tandem. These sensors can contribute to reduce costs and time required for tropical soil texture determination.