Prediction of Soil Clay Content and Cation Exchange Capacity Using Visible Near-Infrared Spectroscopy, Portable X-ray Fluorescence, and X-ray Diffraction Techniques

This article investigates a novel data fusion method to predict clay content and cation exchange capacity using visible near-infrared (visNIR) spectroscopy, portable X-ray fluorescence (pXRF), and X-ray diffraction (XRD) techniques. A total of 367 soil samples from two study areas in regional Australia were analyzed and intra- and interarea calibration options were explored. Cubist models were constructed using information from each device independently and in combination. pXRF produced the most accurate predictions of any individual device. Models based on fused data significantly improved the accuracy of predictions compared with those based on individual devices. The combination of pXRF and visNIR had the greatest performance. Overall, the relative increase in Lin's concordance correlation coefficient ranged from 1% to 12% and the corresponding decrease in root-meansquare error (RMSE) ranged from 10% to 46%. Provision of XRD data resulted in a decrease in observed RMSE values, although differences were not significant. Validation metrics were less promising when models were calibrated in one study area and then transferred to the other. Observed RMSE values were similar to 2 to 3 times larger under this model transfer scenario and independent use of XRD was found to have the best overall performance.

Automated summary

This study explores a novel data fusion method using visNIR, pXRF, and XRD techniques to predict clay content and cation exchange capacity, finding the combination of pXRF and visNIR to have the best performance. Fused data models significantly improved prediction accuracy compared to individual devices, with the addition of XRD data aiding in reducing errors. Models calibrated in one study area and transferred to another showed less promising validation metrics, with independent use of XRD showing the best overall performance.