Application of digital soil mapping methods for identifying salinity management classes based on a study on coastal central China

Soil Use and Management

Volumn 29, Issue 3, 2013, Pages 445-456

  Guo, Y ^a   Shi, Z ^a   Li, H Y ^b   Triantafilis, J ^c  

^a ZHEJIANG UNIVERSITY   (China)

^b JIANGXI UNIVERSITY OF FINANCE AND ECONOMICS   (China)

^c UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Digital soil mapping; Fuzzy k means; Management zones; Remote sensing; Soil salinity

Indexed keywords

ALGORITHM; COASTAL ZONE MANAGEMENT; DIGITAL MAPPING; FUZZY MATHEMATICS; MANAGEMENT PRACTICE; QUICKBIRD; REMOTE SENSING; SALINITY; SATELLITE IMAGERY; SOIL TYPE;

CHINA;

EID: 84884199045     PISSN: 02660032     EISSN: 14752743     Source Type: Journal    
DOI: 10.1111/sum.12059     Document Type: Article

References (37)

1. Altdorf, D. & Dietrich, P. 2012. Combination of electromagnetic induction and gamma spectrometry using k-means clustering: a study for evaluation of site partitioning. Journal of Plant Nutrition and Soil Science, 175, 345-354.
2. Bao, S.D. 2007. Soil and Agricultural Chemistry Analysis. P25-200.
3. Bezdek, J.C. 1981. Pattern recognition with Fuzzy objective function algorithms. Plenum Press, New York.
4. Boydell, B. & McBratney, A.B. 2002. Identifying potential within-field management zones from cotton yield estimates. Precision Agriculture, 3, 9-23.
5. Burrough, P.A. & McDonnell, R.A. 1998. Principles of geographical information systems. Oxford University Press, Oxford.
6. Burrough, P.A., Van Gaans, P.F.M. & MacMillan, R.A. 2000. High resolution landform classification using fuzzy k-means. Fuzzy Sets and Systems, 113, 37-52.
7. Davatgar, N., Neishabouri, M.R. & Sepaskhah, A.R. 2012. Delineation of site specific nutrient management zones for a paddy cultivated area based on soil fertility using fuzzy clustering. Geoderma, 173-174, 111-118.
8. Eldeiry, A.A. & Garcia, L.A. 2011. Using indicator kriging technique for soil salinity and yield management. Journal of Irrigation and Drainage Engineering, 137, 82-93.
9. Franzen, D., Long, D., Sims, A., Lamb, J., Casey, F., Staricka, J., Halvorson, M. & Hofman, V. 2011. Evaluation of methods to determine residual soil nitrate zones across the northern Great Plains of the USA. Precision Agriculture, 12, 594-606.
10. Fridgen, J.J., Kitchen, N.R., Sudduth, K.A., Drummond, S.T., Wiebold, W.J. & Fraisse, C.W. 2004. Management Zone Analyst (MZA): software for subfield management zone delineation. Agronomy Journal, 96, 100-108.
11. Gorsevski, P.V., Gessler, P.E. & Jankowski, P. 2003. Integrating a fuzzy k-means classification and a Bayesian approach for spatial prediction of landslide hazard. Journal of Geographical Systems, 5, 223-251.
12. Host, G.E., Polzer, P.L., Mladenoff, D.J., White, M.A. & Crow, T.R. 1996. A quantitative approach to developing regional ecosystem classifications. Ecological Applications, 6, 608-618.
13. Huang, M.X., Shi, Z. & Gong, J.H. 2008. Potential of multi-temporal ERS-2 SAR imagery for land use mapping in coastal zone of Shangyu city, China. Journal of Coastal Research, 24, 170-176.
14. Jaynes, D.B., Kaspar, T.C., Colvin, T.S. & James, D.E. 2003. Cluster analysis of spatiotemporal corn yield patterns in an Iowa field. Agronomy Journal, 95, 574-586.
15. Kitchen, N.R., Sudduth, K.A., Myers, D.B., Drummond, S.T. & Hong, S.Y. 2005. Delineating productivity zones on claypan soil fields using apparent soil electrical conductivity. Computers and Electronics in Agriculture, 46, 285-308.
16. Li, Y., Shi, Z., Li, F. & Li, H.Y. 2007. Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land. Computers and Electronics in Agriculture, 56, 174-186.
17. Li, H.Y., Shi, Z., Webster, R. & Triantafilis, J. 2013. Mapping the three-dimensional variation of soil salinity in a rice-paddy soil. Geoderma, 195-196, 31-41.
18. López-Lozano, R., Casterad, M.A. & Herrero, J. 2010. Site-specific management units in a commercial maize plot delineated using very high resolution remote sensing and soil properties mapping. Computer and Electronics in Agriculture, 73, 219-229.
19. Metternicht, G. 2003. Vegetation indices derived from high-resolution airborne videography for precision crop management. International Journal of Remote Sensing, 24, 2855-2877.
20. Minasny, B. & McBratney, A.B. 2002. FuzME version 3.0, Australian Centre for Precision Agriculture. McMillan Building A05, The University of Sydney, NSW 2006. Available at: http://www.usyd.edu.au/su/agric/acpa.
21. Moral, F.J., Terrόn, J.M. & Rebollo, F.J. 2011. Site-specific management zones based on the Rash model and geostatistical techniques. Computers and Electronics in Agriculture, 75, 223-230.
22. Odeh, I.O.A., McBratney, A.B. & Chittleborough, D.J. 1992. Soil pattern recognition with fuzzy c-means: application to classification and soil-landform interrelationship. Soil Science Society of America Journal, 56, 505-516.
23. Oldeland, J., Dorigo, W., Lieckfeld, L., Lucieer, A. & Jürgens, N. 2010. Combining vegetation indices, constrained ordination and fuzzy classification for mapping semi-natural vegetation units from hyperspectral imagery. Remote Sensing of Environment, 114, 1155-1166.
24. Paine, J.G. 2003. Determining salinization extent, identifying salinity sources, and estimating chloride mass using surface, borehole, and airborne electromagnetic induction methods. Water Resources Research, 39, 3-1-3-10.
25. Peñuelas, J., Isla, R., Filella, I. & Araus, J.L. 1997. Visible and near-infrared reflectance assessment of salinity effects on barley. Crop Science, 37, 198-202.
26. Pinter, P.J., Hatfield, J.L. Jr, Schepers, J.S., Barnes, E.M., Moran, M.S., Daughtry, C.S.T. & Upchurch, D.R. 2003. Remote sensing for crop management. Photogrammetric Engineering and Remote Sensing, 69, 647-664.
27. Salinas-Zavala, C.A., Douglas, A.V. & Diaz, H.F. 2002. Interannual variability of NDVI in northwest Mexico. Associated climatic mechanisms and ecological implications. Remote Sensing of Environment, 82, 417-430.
28. Song, X.Y., Wang, J.H., Huang, W.J., Liu, L.Y., Yan, G.J. & Pu, R.L. 2009. The delineation of agricultural management zones with high resolution remotely sensed data. Precision Agriculture, 10, 471-487.
29. Triantafilis, J. & Monteiro Santos, F.A. 2010. Resolving the spatial distribution of the true electrical conductivity with depth using EM38 and EM31 signal data and a laterally constrained inversion model. Australian Journal of Soil Research, 48, 434-446.
30. Triantafilis, J., Laslett, G.M. & McBratney, A.B. 2000. Calibrating an electromagnetic induction instrument to measure salinity in soil under irrigated cotton. Soil Science Society of America Journal, 64, 1009-1017.
31. Triantafilis, J., Odeh, I.O.A., Minasny, B. & McBratney, A.B. 2003. Elucidation of physiographic and hydrogeological features of the lower Namoi valley using fuzzy k-means classification of EM34 data. Environmental Modelling & Software, 18, 667-680.
32. Triantafilis, J., Kerridge, B. & Buchanan, S.M. 2009. Digital soil-class mapping from proximal and remotely sensed data at the field level. Agronomy Journal, 101, 841-853.
33. Triantafilis, J., Roe, J.A.E. & Monteiro Santos, F.A. 2011. Detecting a leachate-plume in an aeolian sand landscape using a DUALEM-421 induction probe to measure electrical conductivity followed by inversion modelling. Soil Use and Management, 27, 357-366.
34. Triantafilis, J., Gibbs, I. & Earl, N. 2013a. Digital soil pattern recognition in the lower Namoi valley using numerical clustering of gamma-ray spectrometry data. Geoderma, 192, 407-421.
35. Triantafilis, J., Terhune IV, C.H. & Monteiro Santos, F.A. 2013b. An inversion approach to generate electromagnetic conductivity images from signal data. Environmental Modelling & Software, 43, 88-95.
36. Victorian Resources Online. 2013. Salinity Class Ranges. http://www.dpi.vic.gov.au/dpi/vro/vrosite.nsf/pages/water_spotting_soil_salting_class_ranges#s.
37. Xiao, X., He, L., Salas, W., Li, C., Moore III, B., Zhao, R., Frolking, S. & Boles, S. 2002. Quantitative relationships between field-measured leaf area index and vegetation index derived from vegetation images for paddy rice fields. International Journal of Remote Sensing, 23, 3595-3604.