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

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 (R-2), 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 PISR on single sensor datasets and the fused sensor dataset, in terms of RMSE, R-2, and RPIQ; and (2) both PISR and SVMR based on the fused sensor dataset had better predictive power (RMSE = 4.02, R-2 = 0.72, and RPIQ = 2.23 in PLSR model; RMSE = 3.02, R-2 = 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.