Spatial modeling of soil salinity using kriging interpolation techniques: A study case in the Great Hungarian Plain

Abstract

The world’s current task is to ensure food security for an ever-growing population of 7.674 billion in 2019. Soil degradation threatens sustainable agriculture in arid and semi-arid climates, where evaporation rates outweigh precipitation. Soluble salts concentrated in the subsoil under certain climatic conditions influence soil physicochemical properties, leading to soil fertility and biodiversity losses. Hence, understanding salinity behavior and its spatial variation are crucial for natural resources management to achieve and maintain sustainability. This study aims to model soil salinity spatial distribution using four kriging interpolation methods, i.e., ordinary kriging (OK), empirical Bayesian kriging (EBK), co-kriging (CK), and indicator kriging (IK). Two hundred twenty-two soil samples were collected for this purpose during a field campaign conducted in the Hungarian Soil Monitoring System framework in 2016. The performance of kriging methods was assessed and compared using two cross-validations, i.e., leave-one-out cross-validation (LOOCV) and the holdout method. The Pearson correlation analysis has been used to expose a significant moderate correlation between salt content and cation exchange capacity (CEC) with a correlation coefficient of 0.4 and a p-value of 0.003. Thus, the spatial relationship between soil salinity content (SSC) and CEC was integrated into the model to enhance predictions in areas where no measurements were accessible. The study demonstrated co-kriging efficiency by reducing the mean squared error (MSE) of ordinary kriging (OK) from 0.8 g/kg and 0.85 g/kg for LOOCV and the holdout cross-validation to 0.3 g/kg.

Keywords

• Geostatistics
• interpolation
• kriging
• soil salinity
• spatial modeling.

References

1. Abdel-Fattah, M.K., 2020. A GIS-based approach to identify the spatial variability of salt affected soil properties and delineation of site-specific management zones: A case study from Egypt. Soil Science Annual 71(1): 76-85.
2. Abdennour, M.A., Douaoui, A., Bradaï, A., Bennacer, A., Fernández, M.P., 2019. Application of kriging techniques for assessing the salinity of irrigated soils: the case of El Ghrous perimeter, Biskra, Algeria. Spanish Journal of Soil Science 9(2): 105-124.
3. Abdennour, M.A., Douaoui, A., Piccini, C., Pulido, M., Bennacer, A., Bradaï, A., Barrena, J., Yahiaoui, I., 2020. Predictive mapping of soil electrical conductivity as a Proxy of soil salinity in south-east of Algeria. Environmental and Sustainability Indicators 8: 100087.
4. Babiker, S., Abulgasim, E., Hamid H.S., 2018. Enhancing the spatial variability of soil salinity ındicators by remote sensing indices and geo-statistical approach. Journal of Earth Science & Climatic Change 9: 1-7.
5. Benslama, A., Khanchoul, K., Benbrahim, F., Boubehziz, S., Chikhi, F., Navarro-Pedreño, J., 2020, Monitoring the variations of soil salinity in a palm grove in Southern Algeria. Sustainability 12(15): 6117.
6. Bhunia, G.S., Shit, P.K., Maiti, R., 2016. Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC). Journal of the Saudi Society of Agricultural Sciences 17(2): 114-126.
7. Biswas, A., Si, B.C., 2013. Model averaging for semivariogram model parameters. In: Advances in agrophysical research. Grundas, S., Stepniewski, A. (Eds.). IntechOpen. Available at [access date: 21.04.2021]: https://www.intechopen.com/chapters/39857
8. Cambardella, C., Moorman, T., Parkin, T., Karlen, D., Novak, J., Turco, R., Konopka, A., 1994. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal 58: 1501–1511.

9. Csillag, F., Pásztor, L., Biehl L.L., 1993. Spectral band selection for the characterization of salinity status of soils. Remote Sensing of Environment 43(3): 231–242.
10. Delbari, M., Afrasiab, P., Loiskandl, W., 2011. Geostatistical analysis of soil texture fractions on the field scale. Soil and Water Research 6: 173-189.
11. Deutsch, C., Journel, A., 1998. Geostatistical software library and user’s guide. Oxford University Press, Oxford. 369p.
12. El hafyani, M., Essahlaoui, A., El Baghdadi, M., Teodoro, A.C., Mohajane, M., El hmaidi, A., El ouali , A.,2019. Modeling and mapping of soil salinity in Tafilalet plain (Morocco). Arabian Journal of Geosciences 12: 35.
13. Emadi, M., Baghernejad, M., 2014. Comparison of spatial interpolation techniques for mapping soil pH and salinity in agricultural coastal areas, northern Iran. Archives of Agronomy and Soil Science 60(9): 1315-1327.
14. ESDAC, 2021. European Soil Data Centre (ESDAC). European Commission, Joint Research Centre. Available at [Access date: 21.04.2021]: https://esdac.jrc.ec.europa.eu/
15. Gallichand, J., Buckland, G.D., Marcotte, D., Hendry, M.J., 1992. Spatial interpolation of soil salinity and sodicity for a saline soil in Southern Alberta. Canadian Journal of Soil Science 72(4): 503-516.
16. Gräler, B., 2011. Cokriging and indicator kriging. Seminar Spatio-temporal dependence. University of Münster. Available at [Access date: 21.04.2021]: http://www.graeler.org/copulaIntro/02_Co-Kriging_Indicator-Kriging.pdf
17. Gribov, A., Krivoruchko, K., 2020. Empirical Bayesian kriging implementation and usage. The Science of The Total Environment 722: 137290.
18. Guedes, L.P., Bach, R.T., Uribe-Opazo, M.A., 2020. Nugget effect influence on spatial variability of agricultural data. Engenharia Agrícola 40(1): 96-104.
19. Hamzehpour, N., Eghbal, M.K., Bogaert, P., Toomanian, N., Sokouti, R.S., 2013. Spatial prediction of soil salinity using kriging with measurement errors and probabilistic soft data. Arid Land Research and Management 27(2): 128-139.
20. Hartmann, K., Krois, J., Waske, B., 2018. E-Learning Project SOGA: Statistics and Geospatial Data Analysis. Freie Universitaet Berlin. Available at [Access date: 21.04.2021]: https://www.geo.fu-berlin.de/en/v/soga/index.html
21. Hoa, P.V., Giang, N.V., Binh, N.A., Hai, L.V.H., Pham, T.D., Hasanlou, M., Bui, D.T, 2019. Soil salinity mapping using SAR Sentinel-1 data and advanced machine learning algorithms: A case study at Ben Tre Province of the Mekong River Delta (Vietnam). Remote Sensing 11(2): 128.
22. Hungarian Meteorological Service, 2018. Precipitation conditions of Hungary. Available at [Access date: 21.04.2021]: https://www.met.hu/en/eghajlat/magyarorszag_eghajlata/altalanos_eghajlati_jellemzes/csapadek/
23. Ivushkin, K., Bartholomeus, H., Bregt, A.K., Pulatov, A., 2017. Satellite thermography for soil salinity assessment of cropped areas in Uzbekistan. Land Degradation and Development 28(3): 870–877.
24. Kiš, M.I., 2016. Comparison of ordinary and universal kriging interpolation techniques on a depth variable (a case of linear spatial trend), case study of the Šandrovac field. The Mining-Geological-Petroleum Bulletin 31(2): 41–58.
25. Krige, D., 1985. The use of geostatistics in defining and reducing the uncertainty of grade estimates. South African Journal of Geology 88(1): 69-72.
26. Mádl-Szőnyi, J., Tóth, J., Pogácsás, G., 2008. Soil and wetland salinization in the framework of the Danube-Tisza Interfluve hydrogeologic type section. Central European Geology 51(3): 203-217.
27. MSZ 08-0206-2, 1978. Evaluation of some chemical properties of the soil. Laboratory tests. (pH value, phenolphtaleine alkalinity expressed in soda, all water-soluble salts, hydrolite (yˇ1^-value), and exchanging acidity (yˇ2^- value). Hungarian Standards Institution.
28. Naseem, I., Bhatti, H.N., 2000. Organic matter and salt concentration effect on cation exchange equilibria in non-calcareous soils. Pakistan Journal of Biological Sciences 3: 1110-1112.
29. Nawar, S., Reda, M., Farag, F., El Nahry, A.H., 2011. Mapping soil salinity in El-Tina plain in Egypt using geostatistical approach. Proceedings of the Geoinformatics Forum Salzburg, 5–8 July 2011, Salzburg, Austria.
30. Negreiros, J., Painho, M., Aguilar, F., Aguilar, M., 2010. Geographical information systems principles of ordinary kriging interpolator. Journal of Applied Sciences 10: 852-867.
31. Nezami, M.T., Alipour, Z.T., 2012. Preparing of the soil salinity map using geostatistics method in the Qazvin Plain. Journal of Soil Science and Environmental Management 3(2): 36-41.
32. Nie, S., Bian, J., Zhou, Y., 2021. Estimating the spatial distribution of soil salinity with geographically weighted regression kriging and ıts relationship to groundwater in the western Jilin Irrigation Area, Northeast China. Polish Journal of Environmental Studies 30(1): 283-294.
33. Panagos, P., Van Liedekerke, M., Jones, A., Montanarella, L., 2012. European soil data centre: Response to European policy support and public data requirements. Land Use Policy 29 (2): 329-338.
34. Panday, D., Maharjan, B., Chalise, D., Shrestha, R.K., Twanabasu, B., 2018. Digital soil mapping in the Bara district of Nepal using kriging tool in ArcGIS. PloS one 13(10): e0206350.
35. Pásztor, L., Laborczi, A., Bakacsi, Z., Szabó, J., Illés, G., 2018. Compilation of a national soil-type map for Hungary by sequential classification methods. Geoderma 311: 93–108.
36. Pásztor, L., Laborczi, A., Takács, K., Szatmári, G., Dobos, E., Illés, G., Bakacsi, Z., Szabó, J., 2015. Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools. Hungarian Geographical Bulletin 64(1): 49-64.
37. Pulatov, A., Khamidov, A., Akhmatov, D., Pulatov, B., Vasenev, V., 2020. Soil salinity mapping by different interpolation methods in Mirzaabad district, Syrdarya Province. IOP Conference Series: Materials Science and Engineering, International Scientific Conference Construction Mechanics, Hydraulics and Water Resources Engineering (CONMECHYDRO – 2020) 23-25 April 2020, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, Tashkent, Uzbekistan, Volume 883, 012089
38. Ronai, A., 1986. The Quaternary of the Great Hungarian Plain. Available at [Access date: 21.04.2021]: http://epa.oszk.hu/02900/02986/00025/pdf/EPA02986_geologica_hungarica_ser_geol_1985_21_413-445.pdf
39. Sahbeni, G., 2021. A PLSR model to predict soil salinity using Sentinel-2 MSI data. Open Geosciences 13(1): 977-987.
40. Sahbeni, G., 2021. Soil salinity mapping using Landsat 8 OLI data and regression modeling in the Great Hungarian Plain. SN Applied Sciences 3: 587.
41. Samsonova, V.P., Blagoveshchenskii, Y.N., Meshalkina, Y.L., 2017. Use of empirical Bayesian kriging for revealing heterogeneities in the distribution of organic carbon on agricultural lands. Eurasian Soil Science 50: 305–311.
42. Sangani, M.F., Khojasteh, D.N., Owens, G., 2019. Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping. Environmental Monitoring and Assessment 191: 684.
43. Schofield, R., Thomas, D., Kirkby, M., 2001. Causal processes of soil salinization in Tunisia, Spain, and Hungary. Land Degradation and Development 12(2): 163-181.
44. Scudiero, E., Corwin, D.L., Anderson, R.G., Yemoto, K., Clary, W., Wang, Z., Skaggs, T., 2017. Remote sensing is a viable tool for mapping soil salinity in agricultural lands. California Agriculture 71(4):231–238.
45. Shahid, S.A., Zaman, M., Heng, L., 2018. Soil salinity: Historical perspectives and a world overview of the problem. In: Guideline for salinity assessment, mitigation, and adaptation using nuclear and related techniques. Zaman, M., Shahid, S.A., Heng, L. (Eds.). Springer, Cham. pp 43-53.
46. Shainberg, I., Rhoades, J.D., Prather, R.J., 1980. Effect of exchangeable sodium percentage, cation exchange capacity, and soil solution concentration on soil electrical conductivity. Soil Science Society of America Journal 44(3): 469-473.
47. Silva, B.B., Braga, A.C., Braga, C.C., Oliveira, L.M., Montenegro, S., Junior, B.B., 2016. Procedures for calculation of the albedo with OLI-Landsat 8 images: Application to the Brazilian semi-arid. Revista Brasileira de Engenharia Agricola e Ambiental 20:3-8.
48. Smith, T.E., 2011. Notebook for spatial data analysis: Part II. Continuous Spatial Data Analysis: 4- Variograms. ESE 502, Available at [Access date: 21.04.2021]: https://www.seas.upenn.edu/~tesmith/NOTEBOOK/Part_II/4_Variograms.pdf
49. Szabó, J., Pirkó, B., 2017. The Hungarian monitoring system – results weaknesses, development opportunities. The soil information and monitoring system (TIM). Available at [Access date: 21.04.2021]: http://eagri.cz/public/web/file/519224/_2_7_Bela_Pirko.pdf
50. Szatmári, G., Bakacsi, Z., Laborczi, A., Petrik, O., Pataki, R., Tóth, T., Pásztor, L., 2020. Elaborating Hungarian segment of the global map of salt-affected soils (GSSmap): national contribution to an international initiative. Remote Sensing 12(24): 4073.
51. Taghadosi, M., Hasanlou, M., Eftekhari, K., 2019. Soil salinity mapping using dual-polarized SAR Sentinel-1 imagery. International Journal of Remote Sensing 40(1): 237-252.
52. Tajgardan, T., Ayoubi, S., Shataee, S., Sahrawat, K., 2010. Soil surface salinity prediction using ASTER data: Comparing statistical and geostatistical models. Australian Journal of Basic and Applied Sciences 4(3): 457-467.
53. Thompson, J.A., Roecker, S., Grunwald, S., Owens, P.R., 2012. Digital Soil Mapping: Interactions with and Applications for Hydropedology. In: Hydropedology. Lin, H. (Ed.). Elsevier. pp.665-709.
54. Tóth, G., Adhikari, K., Várallyay, Gy., Tóth, T., Bódis, K., Stolbovoy, V., 2008. Updated map of salt-affected soils in the European Union. In: Threats to Soil Quality in Europe. EUR 23438 EN. Tóth, G., Montanarella, L., Rusco, E. (Eds). European Commission, Joint Research Centre, Institute for Environment and Sustainability Office of Official Publications of the European Communities. Luxembourg, pp. 65–77.
55. Tóth, T., 2009. Monitoring, predicting and quantifying soil salinity, sodicity and alkalinity in Hungary at different scales: Past experiences, current achievements and outlook with special regard to European Union initiatives, Advances in the assessment and monitoring of salinization and status of biosaline agriculture. Report of an expert consultation held in Dubai, United Arab Emirates, 26-29 November 2007. World Soil Resources Report. No 104. FAO, Rome, Italy.
56. Tóth, T., Balog, K., Szabo, A., Pásztor, L., Jobbágy, E.G., Nosetto, M.D., Gribovszki, Z., 2014. Influence of lowland forests on subsurface salt accumulation in shallow groundwater areas. AoB PLANTS 6: plu054.
57. Tóth, T., Pásztor, L., Kabos, S., Kuti, L., 2002. Statistical prediction of the presence of salt-affected soils by using digitalized hydrogeological maps. Arid Land Research and Management 16(1):55–68.
58. Triantafilis, J., Odeh, I., McBratney, A., 2001. Five geostatistical models to predict soil salinity from electromagnetic ınduction data across ırrigated cotton. Soil Science Society of America Journal 65(3): 869-878.
59. Tziachris, P., Metaxa, E., Papadopoulos, F., & Papadopoulou, M., 2017. Spatial modelling and prediction assessment of soil iron using kriging interpolation with pH as auxiliary iformation. International Journal of Geo-Information 6(9): 283.
60. Uri, N., 2018. Cropland soil salinization and associated hydrology: trends, processes, and examples. Water 10(8): 1030.
61. Wackernagel, H., 1995. Ordinary Kriging. In: Multivariate Geostatistics. Wackernagel, H. (Ed.). Springer, Berlin, Heidelberg. pp. 74-81.
62. Wackernagel, H., 2013. Basics in Geostatistics 2 Geostatistical interpolation/estimation: Kriging methods. MINES ParisTech. Available at [Access date:21.04.2021]: https://www.nersc.no/sites/www.nersc.no/files/Basics2kriging.pdf
63. Xiao, Y., Gu, X., Yin, S., et al., 2016. Geostatistical interpolation model selection based on ArcGIS and Spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China. SpringerPlus 5: 425.
64. Zhang, Y., 2011. Introduction to Geostatistics-Course Notes. University of Wyoming. 31p. Available at [Access date: 21.04.2021]: http://www.uwyo.edu/geolgeophys/people/faculty/yzhang/_files/docs/geosta1.pdf
65. Zheng, Z., & Zhang, F., & Chai, X., Zhu, Z., Ma, F., 2009. Spatial estimation of soil moisture and salinity with neural kriging. International Conference on Computer and Computing Technologies in Agriculture. CCTA 2008: Computer and Computing Technologies in Agriculture II, IFIP Advances in Information and Communication Technology, Vol 294. Springer, Boston, MA. pp. 1227-1237.