Quantitative Evaluation and Uncertainty Assessment on Geostatistical Simulation of Soil Salinity Using Electromagnetic Induction Technique — Oak Academic Publishing
Research ArticleOpen AccessGoogle Scholar indexed
Quantitative Evaluation and Uncertainty Assessment on Geostatistical Simulation of Soil Salinity Using Electromagnetic Induction Technique
The Key Laboratory of Oasis Ecological Agriculture, Xinjiang Production and Construction Group/College of Agriculture, Shihezi University, Shihezi, China
,
The Key Laboratory of Oasis Ecological Agriculture, Xinjiang Production and Construction Group/College of Agriculture, Shihezi University, Shihezi, China
,
The Key Laboratory of Oasis Ecological Agriculture, Xinjiang Production and Construction Group/College of Agriculture, Shihezi University, Shihezi, China
,
The Key Laboratory of Oasis Ecological Agriculture, Xinjiang Production and Construction Group/College of Agriculture, Shihezi University, Shihezi, China
,
The Key Laboratory of Oasis Ecological Agriculture, Xinjiang Production and Construction Group/College of Agriculture, Shihezi University, Shihezi, China
,
The Key Laboratory of Oasis Ecological Agriculture, Xinjiang Production and Construction Group/College of Agriculture, Shihezi University, Shihezi, China
1 The Key Laboratory of Oasis Ecological Agriculture, Xinjiang Production and Construction Group/College of Agriculture, Shihezi University, Shihezi, China
2 The Key Laboratory of Oasis Ecological Agriculture, Xinjiang Production and Construction Group/College of Agriculture, Shihezi University, Shihezi, China
3 The Key Laboratory of Oasis Ecological Agriculture, Xinjiang Production and Construction Group/College of Agriculture, Shihezi University, Shihezi, China
4 The Key Laboratory of Oasis Ecological Agriculture, Xinjiang Production and Construction Group/College of Agriculture, Shihezi University, Shihezi, China
5 The Key Laboratory of Oasis Ecological Agriculture, Xinjiang Production and Construction Group/College of Agriculture, Shihezi University, Shihezi, China
6 The Key Laboratory of Oasis Ecological Agriculture, Xinjiang Production and Construction Group/College of Agriculture, Shihezi University, Shihezi, China
Diagnosis of soil salinity and characterizing its spatial variability both vertically and horizontally are needed to establish control measures in irrigated agriculture. In this regard, it is essential that salinity development in varying soil depths be known temporally and spatially. Apparent soil electrical conductivity, measured by electromagnetic induction instruments, has been widely used as an auxiliary variable to estimate spatial distribution of field soil salinity. The main objectives of this paper were adopted a mobile electromagnetic induction (EMI) system to perform field electromagnetic (EM) survey in different soil layers, to evaluate the uncertainty through Inverse Distance Weighted (IDW) and Ordinary Kriging (OK) methods, and to determine which algorithm is more reliable for the local and spatial uncertainty assessment. Results showed that EM38 data from apparent soil electrical conductivity are highly correlated with salinity, more accurate for estimating salinity from multiple linear regression models, which the correlation coefficient of 0 - 20, 20 - 40, 40 - 60 and 60 - 80 cm were 0.9090, 0.9228, 0.896 and 0.9085 respectively. The comparison showed that the prediction accuracy of two methods also displays good performance for soil salinity, the estimation precision of IDW method (with E = 0.8873, 0.9075, 0.8483 and 0.901, RPD = 9.64, 8.01, 8.17 and 11.23 in 0 - 20, 20 - 40. 40 - 60 and 60 - 80 cm soil layers, respectively) was superior to that of OK (with E = 0.8857, 0.872, 0.8744 and 0.8822, RPD = 9.44, 7.83, 8.52 and 10.88, respectively), but differences of two methods in predictions are not significant. The obtained salinity map was helpful to display the spatial patterns of soil salinity and monitor and evaluate the management of salinity.
Zhou, D., Xu, J.C., Wang, L., Lin, Z.L. and Liu, L.M. (2015) Identifying and Managing Risk Factors for Salt-Affected Soils: A Case Study in a Semi-Arid Region in China. Environmental Monitoring and Assessment, 187, 421. http://dx.doi.org/10.1007/s10661-015-4639-7
Bouarfa, S. and Kuper, M. (2012) Groundwater in Irrigation Systems: From Menace to Mainstay. Irrigation and Drainage, 61, 1-13. http://dx.doi.org/10.1002/ird.1651
Purkey, D.R. and Wallender, W.W. (2001) Drainage Reduction under Land Retirement over a Shallow Water Table. Journal of Irrigation and Drainage Engineering, 127, 1-7. http://dx.doi.org/10.1061/(ASCE)0733-9437(2001)127:1(1)
Wichelns, D. and Qadir, M. (2015) Achieving Sustainable Irrigation Requires Effective Management of Salts, Soil Salinity, and Shallow Groundwater. Agricultural Water Management, 157, 31-38. http://dx.doi.org/10.1016/j.agwat.2014.08.016
Alonso, M.F., Corwin, D.L., Oster, J.D., Maas, J. and Kaffka, S.R. (2013) Modeling a Sustainable Salt Tolerant Grass- Livestock Production System under Saline Conditions in the Western San Joaquin Valley of California. Sustainability- Basel, 5, 3839-3857. http://dx.doi.org/10.3390/su5093839
Berkal, I., Walter, C., Michot, D. and Djili, K. (2014) Seasonal Monitoring of Soil Salinity by Electromagnetic Conductivity in Irrigated Sandy Soils from a Saharan Oasis. Soil Research, 52, 769-780. http://dx.doi.org/10.1071/SR13305
Triantafilis, J., Kerridge, B. and Buchanan, S.M. (2009) Digital Soil-Class Mapping from Proximal and Remotely Sensed Data at the Field Level. Agronomy Journal, 101, 841-853. http://dx.doi.org/10.2134/agronj2008.0112
Urdanoz, V. and Aragues, R. (2012) Comparison of Geonics EM38 and Dualem 1S Electromagnetic Induction Sensors for the Measurement of Salinity and Other Soil Properties. Soil Use and Management, 28, 108-112. http://dx.doi.org/10.1111/j.1475-2743.2011.00386.x
Emadi, M., Shahriari, A.R., Sadegh-Zadeh, F., JaliliSeh-Bardan, B. and Dindarlou, A. (2016) Geostatistics-Based Spatial Distribution of Soil Moisture and Temperature Regime Classes in Mazandaran Province, Northern Iran. Archives of Agronomy and Soil Science, 62, 502-522. http://dx.doi.org/10.1080/03650340.2015.1065607
Qadir, M., Quillerou, E., Nangia, V., Murtaza, G., Singh, M., Thomas, R.J., et al. (2014) Economics of Salt-Induced Land Degradation and Restoration. Natural Resources Forum, 38, 282-295. http://dx.doi.org/10.1111/1477-8947.12054
Sun, Y.R., Cheng, Q., Lin, J.H., Schellberg, J. and Lammers, P.S. (2013) Investigating Soil Physical Properties and Yield Response in a Grassland Field Using a Dual-Sensor Penetrometer and EM38. Journal of Plant Nutrition and Soil Science, 176, 209-216. http://dx.doi.org/10.1002/jpln.201200104
Triantafilis, J., Huckel, A.I. and Odeh, I.O.A. (2001) Comparison of Statistical Prediction Methods for Estimating Field-Scale Clay Content Using Different Combinations of Ancillary Variables. Soil Science, 166, 415-427. http://dx.doi.org/10.1097/00010694-200106000-00007
Doolittle, J.A., Indorante, S.J., Potter, D.K., Hefner, S.G. and McCauley, W.M. (2002) Comparing Three Geophysical Tools for Locating Sand Blows in Alluvial Soils of Southeast Missonr. Journal of Soil and Water Conservation, 57, 175-182.
Triantafilis, J., Lau, K.L. and Buchanan, S.M. (2009) Field Level Digital Soil Mapping of Canon Exchange Capacity Using Electromagnetic Induction and a Hierarchical Spatial Regression Model in the Lower Namoi Valley. Australian Journal of Soil Research, 47, 651-663. http://dx.doi.org/10.1071/SR08240
Li, H.Y., Shi, Z., Webster, R. and Triantafilis, J. (2013) Mapping the Three-Dimensional Variation of Soil Salinity in a Rice-Paddy Soil. Geoderma, 195, 31-41. http://dx.doi.org/10.1016/j.geoderma.2012.11.005
Buchanan, S.M. and Triantafilis, J. (2009) Mapping Water Table Depth Using Geophysical and Environmental Variables. Ground Water, 47, 80-96. http://dx.doi.org/10.1111/j.1745-6584.2008.00490.x
Li, H.Y., Webster, R. and Shi, Z. (2015) Mapping Soil Salinity in the Yangtze Delta: REML and Universal Kriging (E-BLUP) Revisited. Geoderma, 237, 71-77. http://dx.doi.org/10.1016/j.geoderma.2014.08.008
Li, X.M., Yang, J.S., Liu, M.X., Liu, G.M. and Yu, M. (2012) Spatio-Temporal Changes of Soil Salinity in Arid Areas of South Xinjiang Using Electromagnetic Induction. Journal of Integrative Agriculture, 11, 1365-1376.
Bhat, S., Motz, L.H., Pathak, C. and Kuebler, L. (2015) Geostatistics-Based Groundwater-Level Monitoring Network Design and Its Application to the Upper Floridan Aquifer, USA. Environmental Monitoring and Assessment, 187, 4183. http://dx.doi.org/10.1007/s10661-014-4183-x
Silva, A.F., Barbosa, A.P., Zimback, C.R.L., Landim, P.M.B. and Soares, A. (2015) Estimation of Croplands Using Indicator Kriging and Fuzzy Classification. Computers and Electronics in Agriculture, 111, 1-11.
Huang, J., Subasinghe, R., Malik, R.S. and Triantafilis, J. (2015) Salinity Hazard and Risk Mapping of Point Source Salinisation Using Proximally Sensed Electromagnetic Instruments. Computers and Electronics in Agriculture, 113, 213-224.
Barbiéro, L., Cunnac, S., Mané, L., Laperrousaz, C., Hammecker, C. and Maeght, J.L. (2001) Salt Distribution in the Senegal Middle Valley: Analysis of a Saline Structure on Planned Irrigation Schemes from N’Galenka Creek. Agricultural Water Management, 46, 201-213. http://dx.doi.org/10.1016/S0378-3774(00)00088-3
Tripathi, R., Nayak, A.K., Shahid, M., Raja, R., Panda, B.B., Mohanty, S., et al. (2015) Characterizing Spatial Variability of Soil Properties in Salt Affected Coastal India Using Geostatistics and Kriging. Arabian Journal of Geosciences, 8, 10693-10703. http://dx.doi.org/10.1007/s12517-015-2003-4
Triantafilis, J., Odeh, I.O.A. and Mcbratney, A.B. (2001) Five Geostatistical Models to Predict Soil Salinity from Electromagnetic Induction Data across Irrigated Cotton. Soil Science Society of America Journal, 65, 869-878. http://dx.doi.org/10.2136/sssaj2001.653869x
Yang, J.S. and Yao, R.J. (2007) Comparison of Interpolation Approaches Based on Spatial Variability of Apparent Soil Electrical Conductivity with an Electromagnetic Induction. Transactions of CSAE, 23, 50-57.
Burrough, P.A. and McDonnell, R. (1998) Principles of Geographic Information Systems. Oxford University Press, New York.
Lloyd, C.D. (2010) Spatial Data Analysis. Oxford University Press, New York.
Matheron, G. (1963) Principles of Geostatistics. Economic Geology, 58, 1246-1266. http://dx.doi.org/10.2113/gsecongeo.58.8.1246
Del Monego, M., Ribeiro, P.J. and Ramos, P. (2015) Comparing the Performance of Geostatistical Models with Additional Information from Covariates for Sewage Plume Characterization. Environmental Science and Pollution Research, 22, 5850-5863. http://dx.doi.org/10.1007/s11356-014-3709-7
Robinson, T.P. and Metternicht, G. (2006) Testing the Performance of Spatial Interpolation Techniques for Mapping Soil Properties. Computers and Electronics in Agriculture, 50, 97-108. http://dx.doi.org/10.1016/j.compag.2005.07.003
Farifteh, J., Van der Meer, F., Atzberger, C., Carranza, E.J.M. (2007) Quantitative Analysis of Salt-Affected Soil Reflectance Spectra: A Comparison of Two Adaptive Methods (PLSR and ANN). Remote Sensing of Environment, 110, 59-78. http://dx.doi.org/10.1016/j.rse.2007.02.005
Triantafilis, J., Laslett, G.M. and 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. http://dx.doi.org/10.2136/sssaj2000.6431009x
Sudduth, K.A., Kitchen, N.R., Wiebold, W.J., Batchelor, W.D., Bollero, G.A., Bullock, D.G., Clay, D.E., Palm, H.L., Pierce, F.J., Schuler, R.T. and Thelen, K.D. (2005) Relating Apparent Electrical Conductivity to Soil Properties across the North-Central USA. Computers and Electronics in Agriculture, 46, 263-283. http://dx.doi.org/10.1016/j.compag.2004.11.010
Herrero, J., Netthisinghe, A., Hudnall, W.H. and Pérez-Covet, O. (2011) Electromagnetic Induction as a Basis for Soil Salinity Monitoring within a Mediterranean Irrigation District. Journal of Hydrology, 405, 427-438. http://dx.doi.org/10.1016/j.jhydrol.2011.05.041
Goovaerts, P. (1997) Geostatistics for Natural Resources Evaluation. Oxford University Press, New York.
McBratney, A.B. and Webster, R. (1981) Spatial Dependence and Classification of the Soil along a Transect in Northeast Scotland. Geoderma, 26, 63-82. http://dx.doi.org/10.1016/0016-7061(81)90076-8
Cambardella, C.A., Moorman, T.B., Novak, J.M., Parkin, T.B., Turco, R.F. and Konopka, A.E. (1994) Field-Scale Variability of Soil Properties in Central Iowa Soils. Soil Science Society of America Journal, 58, 1501-1511. http://dx.doi.org/10.2136/sssaj1994.03615995005800050033x