Journal
GEODERMA
Volume 363, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.geoderma.2019.114163
Keywords
Partial least-squares regression; Support vector machine regression; Proximal sensing technique; Fused sensor data
Categories
Funding
- National Key Research and Development Program [2018YFC1802601]
- Key Frontier Project of Institute of Soil Science, Chinese Academy of Sciences [ISSASIP1629]
- National Science and Technology Basic Special Program [2014FY110200A10]
- Open Fund of Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education [GTYR201901]
- China Scholarship Council
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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.
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