Data fusion of vis–NIR and PXRF spectra to predict soil physical and chemical properties

Abstract

We tested one front-end data fusion method to combine visible near-infrared (vis-NIR) and portable X-ray fluorescence (PXRF) spectra for predicting different soil properties and investigated the contribution of different sensor data. A total of 197 soil samples were collected from 25 Alfisols and Mollisols in south-central Wisconsin, USA. Soils were analysed in the laboratory for clay, sand, silt content, total carbon (TC), total nitrogen (TN) and pH. Air-dried soil samples were scanned with vis-NIR and PXRF spectrometers. A principal component analysis (PCA) was applied to each of the vis-NIR and PXRF spectra to extract the first 10 principal components (PCs), which were used in the Cubist model. Five types of input data were compared for constructing the models using a front-end data fusion approach, including: (a) 10 PCs from vis-NIR spectra, (b) 10 PCs from spectra of PXRF beam 1 (XRF40), (c) 10 PCs from spectra of PXRF beam 2 (XRF10), (d) concatenating 20 PCs from two PXRF spectra (XRF40 + 10), and (e) concatenating 30 PCs from two PXRF spectra and vis-NIR spectra (XRF40 + 10 + NIR). In addition, the performances of the preprocessing methods (four smoothing methods with or without background removal) of PXRF spectra were compared. Multiple linear regression was also used to predict soil properties directly from 11 PXRF-estimated elements. Our results suggest that smoothing should be applied to the PXRF spectra prior to developing predictive models.PXRF spectra and elemental data can predict soil texture with validation R 2 > 0.85, better than using solely vis-NIR spectra, and combining vis-NIR and PXRF spectra improved the prediction. Combining PXRF and vis-NIR can also predict TC and TN moderately well. Soil pH cannot be predicted from vis-NIR or PXRF spectra in this dataset due to its weak correlations with other soil properties and elements. It is concluded that PXRF spectra can be solely used to estimate soil texture, whereas combining vis-NIR and PXRF spectra via PCA should be used to estimate TC and TN. Highlights• The use of front-end data fusion method to combine vis-NIR and PXRF spectra for predicting soil properties.

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

“…On the other hand, results by Xu et al [ 48 ] showed comparable prediction performances between model averaging approaches (i.e., GR approach) and the outer product analysis (OPA), which is also a front-end approach for the integration of vis-NIR and XRF sensor data. Further contrasting, recent research by Zhang and Hartemink [ 53 ], which fused XRF and vis-NIR data using front-end approaches, successfully explored the synergy between sensors and achieved accurate prediction performance for soil TN and TC. The results observed in the literature clearly show that there is still no consensus on an optimal data fusion approach for vis-NIR and XRF sensor data for soil analysis.…”

“…O’Rourke et al [ 26 ] reported RI ranging from 15 to 44% for clay, CEC, pH, ex-K, ex-Ca, and ex-Mg after model averaging data fusion procedures (e.g., GR and variance weighted averaging), using an Australian soil dataset. Evaluating the combined use of vis-NIR and XRF techniques through different data fusion approaches, Zhang and Hartemink [ 53 ] obtained RI values of 12, 3, and 20% for clay, pH, and total carbon, respectively, for samples collected from different agricultural fields in the USA. A similar study in Chinese soils, reported an RI of 26% for CEC [ 54 ].…”

“…In fact, though GR and LS are conventional data fusion approaches with promising results for fusing data of vis-NIR and XRF sensors for soil analysis [ 26 , 48 ], they may not take advantage of all the information contained in multi-source spectra [ 53 ]. On the other hand, front-end approaches (Front-end approaches are the fusion methods which consider all available information before subjecting them to any prediction model [ 48 , 53 ]), such as SF, attempt to make use of all available information by merging the full data. However, this trend was not endorsed by the results of our study that showed better prediction performances for the model averaging techniques compared to the tested front-end data fusion approaches (e.g., SF-PLS and SF-SVM).…”

“…A third approach is to combine spectral data (denoted here as spectra fusion (SF)) in one matrix, which is subjected to linear or non-linear analysis. Furthermore, the majority of papers reporting the fusion of vis-NIR and XRF data focused on the prediction of one or limited number of soil attributes, e.g., soil textural attributes [ 44 ], TN and TC [ 43 ], textural attributes, pH [ 53 ], CEC [ 54 ], and chromium [ 48 ]. Although O’Rourke et al [ 26 ] combined the vis-NIR and XRF data for the analysis of a wide range of soil attributes, they have explored the averaging data fusion methods only.…”

Effect of X-Ray Tube Configuration on Measurement of Key Soil Fertility Attributes with XRF

“…Sensors compatible with PSS approaches can be applied in laboratory and field conditions, and some of them can be used for on-line measurements, assembled on mobile platforms [1]. In order to create practical alternatives for soil analysis, efforts have been made worldwide to adapt and re-engineer sensor systems developed in other areas into soil sensing [6,7], as well as assessing different combinations of sensors and data fusion methods [8][9][10]. However, there is still no consensus on an effective technique that enables the development of a generic and robust methodology for soil fertility analysis via proximal sensors.…”