作者： Wan, MX (Wan, Mengxue) ; Hu, WY (Hu, Wenyou) ; Qu, MK (Qu, Mingkai); Li, WD (Li, Weidong); Zhang, CR (Zhang, Chuanrong) ; Kang, JF (Kang, Junfeng) ; Hong, YS (Hong, Yongsheng) ; Chen, Y (Chen, Yong); Huang, B (Huang, Biao)

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

刊物：GEODERMA，卷: 363    文献号: 114163

DOI: 10.1016/j.geoderma.2019.114163

出版年: APR 1 2020

全文链接：https://www.sciencedirect.com/science/article/pii/S0016706119314739?via%3Dihub

摘要：

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.