Document Type : Research Paper
Authors
Abstract
Spatial correlation is the first step in the interpolation of field data and mapping of soil properties.The aim of this research was to study the efficiency of two spatial statistics methods i.e., Kriging and inverse distance weighting for mapping of soil properties. Five sampling units were selected in the region, and the location of soil profiles was so determined to cover the whole area. In each unit, six profiles and totally 30 soil profiles were dug in the whole area. Soil samples were taken from two depths of 0-20 cm and 20-80cm. Soil variables including gravel, clay, silt, lime, organic matter, pH and EC were measured in both soil depths. In the GS+ software, the accuracy of two spatial statistics methods was tested using cross validation with the help of two statistical parameters: MAE and MBE. According to the results, MAE and MBE, related to the Kriging method, for the majority of soil parameters, are less than that of inverse distance weighting method; therefore, Kriging is a more accurate method to interpolate soil properties.
Keywords
-Abdollahi, J. and Rahimian, M. H., 2007. Determination of the best geo-statistical model for field data interpolation using remote sensing. Iranian Journal of Range and Desert Reseach,14 (2): 156-170.
-Allexandra, K. and Bollock, G., 1999. A comparative study of interpolation methods for mapping soil properties. Agronomy Journal, 91:393-400.
-Bosun, S. Z. and Qiguo, Z., 2003. Evaluation of spatial and temporal changes of soil quality based on geostatistical analysis in the hill region of subtropical, China. Geoderma 115:85-99.
-Cambardella, C. A., Moorman, T. B., Parkin, T. B., Karlen, D. L., Turco. R .F. and Konopka. A. E., 1994. Field scale variability of soil properties in central Iowa soils. Soil Science Society American Journal, 58: 1501-1511.
-Cressies, N., 1993. Statistics for spatial data, John Wiley and Sons press, 928p.
-Delfiner, P. and Delhomme, J. P., 1975. Optimum interpolation by kriging. 96-114. In: Davis J. D. and McCullagh M. J. (Eds), Display and Analysis ofSpatial Data. New York, John Wiley and Sons.
-Esfandiarpoor Borujeni, I., Mohammadi, J. Salehi, M. H., Toomanian, N. and Poch, R. M., 2010. Assessing geopedological soil mapping approach by statistical and geostatistical methods: A case study in the Borujen region, Central Iran. Catena, 82 , 1-14.
-Goovaerts, P. and Webster, R., 1994. Scale-dependent correlation between topsoil copper and cobalt concentrations in Scotland. European Journal of Soil Science, 45:79-95.
-Gotway, C. A., Hergert, G. W., Ferguson, R. B. and Peterson, T. A., 1996. Comparison of kriging and inverse-distance methods for mapping soilparameters. Soil Science Society of America Journal, 60 (4): 1237-1247.
-HasaniPak, A., 1998. Geostatistics. University of Tehranpress.314p.
-Hosseini, E., Gallichand, J. and Caron, J., 1993. Comparison of several interpolators for smoothing hydraulic conductivity data in south west Iran. Transactions of the ASAE, 37(6):1799-1807.
-Hosseini, V., Akhavan, R. and Tahmasebi, M., 2012. Effect of Pistachio (Pistaciaatlantica) canopy on the spatial distribution of soil chemical characteristics (Case study: Sarvabad, Kurdistan). Iranian Journal of Forest,4(1): 13-24.
-Jafarihaghighi, M., 2003. Methods of soil analysis: physical and chemical analysis and sampling, with emphasis on theory and practical. NedayeZohapress. 236p.
-Jiachun, Sh., Haizhen, W., Jianming, X., Jianjun. W., Xingmei, L., Haiping, Zh., Chunlan, Yu., 2007. Spatial distribution of heavy metals in soils: a case study of Changxing, China. Environmental Geology, 52:1–10.
-Mohammadi, J., 1999. Study of the spatial variability of soil salinity in Ramhormoz area (Khuzestan) using geostatistical theory Il. cokriging. JWSS - Isfahan University of Technology. 3(1) :1-8.
-Mohammadi, J., 2001. Review on fundamentals of geostatistics and its application to soil science. Journal of Water and Soil, 15:99-121.
-Neal, M., H., Khademi, M .A. and Hajabbasi, 2004. Response of Soil quality indicators and their spatial Variability to land degradation in central Iran. Applied Soil Ecology 27: 221-232.
-Pcerri, C. E., M .Bernoux, V. and Chaplot, 2004. Assessment of soil property spatial variation in an Amazon pasture. Geodrama, 123:51- 68.
-Robinson, T. P., Metternicht, G., 2006. Testing the performance of spatial interpolation techniques for mapping soil properties. Computer and Electronics in Agriculture, 50:97-108.
-Razack, M. and Lasm, T., 2006. Geostatistical estimation of the transmissivity in a highly fractured metamorphic and crystalline aquifer (Man-Danane Region, Western IvoryCoast). Journal of Hydrology, 325: 164-178.
-ToranjZar, H., Zarechahouki, M. A., 2014. Comparison of geostatistical methods for prediction of soil parameter in plant community of Nitrariaschoberi(arid regions). Journal of Range and Watershed Management, 66(4):509-519.
-Vieira, S. R. and Gonzalez. A. Paz., 2003. Analysis of the spatial variability of crop yield and soil properties in small agricultural plots. Bragantia Campinas, 62: 127-138.
-Voltz, M. and Goulard, M., 1994. Spatial interpolation of soil moisture retention curves. Geoderma, 62:109-123.
-Wakernagel, H., 2002. Multivariate geostatistics.Springer press, 387p.
-Weber, D. D. and Englund, E. J., 1994. Evaluation and comparison of spatial interpolators II. Mathematical Geology, 26(5): 589-603.
-Zarechahouki, M. A., ZareErnani, M., ZareChahouki, A. and KhalasiAhvazi, L., 2010.Application of spatial statistical methods in predictivemodels of plant species habitat. Arid Biom Scientific and Research Journal, 1(1):13-24.
-Zarechahouki, M. A., KhalasiAhvazi, L., Azarnivand. H. and ZareChahouki, A., 2013. Preparing the spatial distribution of soil factors by interpolation methods in east rangeland of Semnan. Journal of Range and Watershed Management, 66(3):387-399.
)