PREDICTION RESULTS OF DIFFERENT MODELLING METHODS IN SOIL NUTRIENT CONCENTRATIONS BASED ON SPECTRAL TECHNOLOGY
Abstract
Spectroscopy has been applied in monitoring soil nutrient concentrations. Two types of soil samples, sandy loam and silty loam, were selected as the research objects. The UV-visible-near infrared reflectance spectroscopy data and total carbon (TC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (AN), available phosphorus (AP), available potassium (FK), and slowly available potassium (SK) concentrations were measured. We compared the prediction results within and between two different types of soil with regard to the soil nutrient concentrations using four modelling methods, which were principal component regression (PCR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM), and back propagation neural network (BPNN) models. In the prediction results within a given type of soil, LS-SVM and PLSR had better stability. In the prediction results of different types of soil, BPNN and LS-SVM had a high accuracy in most soil nutrient concentrations. By comparing different modelling methods, this study provides a basis for the subsequent selection of suitable models based on spectral technology to establish various soil nutrient models.
Keywords
UV-visible-near infrared reflectance spectroscopy,
soil nutrient,
least squares support vector machines,
back propagation neural network,
modelling methods
Supporting agencies: This work was supported by the National Natural Science Foundation of China (No. 31700447), the Youth Foundation of Shandong Academy of Sciences of China (No. 2017QN0013) and the Shandong Natural Science Foundation of China (No. ZR2018LD007).
About the Authors
X.-Y. Li
Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences); Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology; National Engineering and Technological Research Center of Marine Monitoring Equipment
China
China
P.-P. Fan
Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences); Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology; National Engineering and Technological Research Center of Marine Monitoring Equipment
China
China
Y. Liu
Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences); Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology; National Engineering and Technological Research Center of Marine Monitoring Equipment
China
China
G.-L. Hou
Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences); Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology; National Engineering and Technological Research Center of Marine Monitoring Equipment
China
China
Q. Wang
Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences); Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology; National Engineering and Technological Research Center of Marine Monitoring Equipment
China
China
M.-R. Lv
Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences); Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology; National Engineering and Technological Research Center of Marine Monitoring Equipment
China
China
References
1. D. Summers, M. Lewis, B. Ostendorf, Ecol. Indic., 11, No. 1, 123–131 (2011).
2. M. Vohland, M. Ludwig, S. Thiele-Bruhn, Geoderma, 223-225, No. 1, 88–96 (2014).
3. S. M. Tiquia, J. Lloyd, D. A. Herms, Appl. Soil Ecol., 21, No. 1, 31–48 (2002).
4. H. Q. Ding, Q. P. Lu, Spectrosc. Spectr. Anal., 32, No. 1, 88–91 (2012).
5. L. Luan, Y. Wang, X. Li, J. Near Infrared Spectrosc., 24, No. 4, 363–372 (2016).
6. A. M. Rady, D. E. Guyer, W. Kirk, J. Food Eng., 135, No. 2, 11–25 (2014).
7. W. Ng, B. P. Malone, B. Minasny, Geoderma, 289, 150–160 (2017).
8. A. Sakudo, Clin. Chim. Acta, 455, No. 3, 181–188 (2016).
9. S. Jia, H. Li, Y. Wang, Geoderma, 268, 92–99 (2016).
10. D. J. Brown, K. D. Shepherd, M. G. Walsh, Geoderma, 132, No. 3, 273–290 (2006).
11. A. V. Bilgili, H. M. V. Es, F. Akbas, J. Arid. Environ., 74, No. 2, 229–238 (2010).
12. F. Feyziyev, M. Babayev, S. Priori, Open J. Soil Sci., 06, No. 3, 52–58 (2016).
13. T. Naes, H. Martens, J. Chemom., 2, No. 2, 155–167 (1988).
14. S. Wold, M. Sjöström, L. Eriksson, Chemom. Intell. Lab. Syst., 58, No. 2, 109–130 (2001).
15. J. A. K. Suykens, J. Vandewalle, Neural Process. Lett., 9, No. 3, 293–300 (1999).
16. R. Hecht-Nielsen, Neural Networks, 1, No. 1, 65–93 (1988).
17. K. Kawamura, Y. Tsujimoto, M. Rabenarivo, Remote Sens., 9, No. 10, 1081 (2017).
18. M. Tatzber, F. Mutsch, A. Mentler, Appl. Spectrosc., 64, No. 10, 1167–1175 (2010).
19. Cheng Wen Chang, D. A. Laird, M. J. Mausbach, Soil Sci. Soc. Am. J., 65, No. 2, 480–490 (2001).
20. S. Wold, M. Sjöström, L. Eriksson, Chemom. Intell. Lab. Syst., 58, No. 2, 109–130 (2001).
21. R. M. Balabin, E. I. Lomakina, Analyst, 136, No. 8, 1703–1712 (2011).
22. D. K. Ghose, S. S. Panda, P. C. Swain, J. Hydrol., 394, No. 3-4, 296–304 (2010).
23. R. K. Douglas, S. Nawar, M. C. Alamar, Sci. Total Environ., 616–617, 147–155 (2017).
24. D. Summers, M. Lewis, B. Ostendorf, Ecol. Indic., 11, No. 1, 123–131 (2011).
25. L. J. Janik, S. T. Forrester, A. Rawson, Chemom. Intell. Lab. Syst., 97, No. 2, 179–188 (2009).
26. H. Wang, D. Hu, Brain Neural Networks, 1, 279–283 (2006).
27. Zhi-biao, Zhao, Yang, J. Bionic. Eng., 5, No. 3, 253–257 (2008).
28. Q. Zhang, Q. Li, G. Zhang, Anal. Methods, 4, No. 7, 2039–2047 (2012).
Review
For citations:
Li X., Fan P., Liu Y., Hou G., Wang Q., Lv M. PREDICTION RESULTS OF DIFFERENT MODELLING METHODS IN SOIL NUTRIENT CONCENTRATIONS BASED ON SPECTRAL TECHNOLOGY. Zhurnal Prikladnoii Spektroskopii. 2019;86(4):673(1)-673(7).
Views: 283
Email this article 