不同土壤间的全氮光谱模型传递研究

doi:10.3964/j.issn.1000-0593(2018)10-3210-05

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Abstract Model transfer among different soils is the key point and obstacle and obstacle for rapidly determining soil nutrients by Vis-NIR spectroscopy. Here, we studied the methods and results of model transfer for total nitrogen(TN) between two types of soils in Qingdao, China. A main spectral model was firstly set up using soils sampled from Licun River. Then, by using piecewise direct standardization combined slope/bias algorithm (PDS-S/B), PDS combined linear intercept algorithm(LI), canonical correlation analysis (CCA) combined S/B (CCA-S/B), CCA-LI, direct standardization (DS) combined S/B (DS-S/B), and DS-LI, the concentrations of total N in soils sampled from Fushan Montain were predicted by the main spectral model with different accuracy. Results of model transfer by PDS-S/B was the best, whose root mean square error (RMSE), mean relative error, and maximum relative error were 0.04, 6.6%, and 19.0%, respectively. Pretreatment before building the spectral model could influence the transfer results. Here, the main spectral model built after extracting the diagnostic spectra genetric algorithm had better results than those built without any pretreatment. Transfer methods could also affect the transfer results. The transfer methods related to LI had a larger increase in accuracy than those related to S/B. The best model transfer was from PDS-S/B, suggesting that PDS-S/B was the better method for this study. This study resolved a specific model transfer for TN between two different types of soils under the same conditions by the same instrument, different from other studies which studied the model transfer of the same soil under different instrument and work conditions. This study explored the possibility that a spectrometer shared a same spectral model, which will improve the efficiency and promote the use of soil nutrient rapid determination by spectroscopy.

Key words：Model transfer; Calibration transfer；Visible and near infrared spectrum; Soil nutrients；Rapid detection

Received: 2017-04-13 Accepted: 2017-08-25

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O657.3

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)10-3210-05 OR https://www.gpxygpfx.com/EN/Y2018/V38/I10/3210

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