Rapid estimation of soil cation exchange capacity through sensor data fusion of portable XRF spectrometry and Vis-NIR spectroscopy

Abstract

Soil cation exchange capacity (CEC) is a critical property of soil fertility. Conventionally, it is measured using laboratory chemical methods, which involve complex sample preparation and are time-consuming and expensive. Previous studies have investigated nondestructive and rapid methods for determining soil CEC using proximal soil sensors individually, including portable X-ray fluorescence (PXRF) spectrometry and visible near-infrared reflectance (Vis-NIR) spectroscopy. In this study, we examined the potential of the fusing data from PXRF and Vis-NIR to predict soil CEC for 572 soil samples from Yunnan Province, China. The CEC of the samples ranged from 5.42 to 50.25 cmol kg−1. Both partial least-squares regression (PLSR) and support vector machine regression (SVMR) were applied to predict soil CEC with individual sensor datasets and a fused sensor dataset for comparison. The root mean squared error (RMSE), coefficients of determination (R2), and ratios of performance to interquartile range (RPIQ) were used to evaluate the performance of the models. Results showed that: (1) SVMR performed better than PLSR on single sensor datasets and the fused sensor dataset, in terms of RMSE, R2, and RPIQ; and (2) both PLSR and SVMR based on the fused sensor dataset had better predictive power (RMSE = 4.02, R2 = 0.72, and RPIQ = 2.23 in PLSR model; RMSE = 3.02, R2 = 0.82, and RPIQ = 2.31 in SVMR model) than those based on any single sensor dataset. In summary, the fused sensor data and SVMR showed great potential for estimating soil CEC efficiently.