Document Type : Research Article

Authors

1 University of Guilan

2 Associated Professor , Department of Soil Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

Abstract

Introduction: Soil texture is the average size of soil particles which depends on the relative proportion of sand, silt and clay contents. Soil texture is one of the most important features used by soil and environmental scientists to describe soils. Soil texture directly affects the soil porosity, which in turn, determines water-retention and flow characteristics, nutrient-holding capacity, internal drainage, sorption characteristics and long-term soil fertility. High-resolution soil maps are essential for land-use planning and other activities related to forestry, agriculture and environment protection. Given the soil texture roles in controlling the soil functions, it is necessary to understand the spatial distribution of this feature in regional scale. As soil texture is a staticproperty, regional scale soil texture maps can thus help environmental scientists to predict different soil-related processes. The objective of this study was to develop a soil textural class map using Terra satellite MODIS sensor images.
Material and Methods: To achieve this goal, the digital elevation model SRTM radar of the studied area for soil samples from different altitudes and slopes was prepared in foursen consecutive 30 meters time frame. The nearest neighbor method with an error of less than 0.5 pixels was used and the elevation layers were mosaicked and transmitted to the UTM ZON-38 coordinate system and GIS Ready Became. The normalized difference vegetation index of bands 1 and 2 of the matrix was obtained to isolate the reflection of the electromagnetic spectrum of vegetation and soil. This final mosaicked digital elevation model was then divided into different altitudes to accurately evaluate the surface texture. The 60 spatial points were selected to estimate the texture of surface soil in thestudied area with systematic randomized sampling. In the current study, soil texture was determined forthe air-dried samplesusing hydrometer. The SWIR bands of MODIS with resolution of 500 meters were selected for sampling dates. After corrections, DN values of the bands for sampling points were extracted. The Pearson correlation coefficient and step wise regression techniques were used to establish proper relationships between the DN values of the SWIR bands and the soil particles. Kriging and cokriging methods were also employed to create a spatially distributed map of the soil textural classes.
Results and Discussion: The results showed that there is a close correlation between the SWIR bands of the terra satellite and the MODIS sensor with band 3, and using this auxiliary variable significantly reduces the estimation error. The best model for fitting semivariogram for clay, silt and sand contents were spherical, spherical and exponential models and the best fitting Cross-semivariogram for clay, silt and sand contents were spherical, exponential and exponential models, respectively. The highest and lowest error estimation was, respectively, related to sand and clay content. The maximum and minimum decrease of estimation error by the auxiliary variables was found for sand and clay content, respectively. The nugget/sill ratio of the kriging semivariograms was greater than 25%for sand and claycontentand lower than 25%for sand and silt content. This indicates that sand and silt contents had a strong spatial dependency, and clay content hada moderate spatial dependency. These ratios for cokriging cross-semivariograms of sand, silt and clay contentsware less than 25%. The interpolation of estimated soil texture was also determined using the cokriging method with 70% of the soil texture measured in the laboratory.
Conclusions: Our results indicated thatcokriging method estimated the soil particles more accurately as compared with linear multi-variable stepwise regression and kriging methods. Application of cokriging method also reduces the number of sampling points and the estimation error of soil texture zoning. Therefore, cokriging method seems to be better suited in impact assessments for data-scarceregions such as Iran.

Keywords

1. Adhikari K., Kheir R.B., Greve M.B., Bocher P.K., Malone B.P., Minasny B., McBratney A.B., and Greve M.H. 2013. Soil Science Society of America Journal. 77, 860-876.
2. Akpa S.I., Odeh I.O., Bishop T.F., and Hartemink A.E. 2014. Digital mapping of soil particle-size fractions for Nigeria. Soil Science Society of America Journal 78(6), 1953-1966.
3. Alavipanah S.A. 2016. Aplication of remote secsing in the earth scinces (soil). university of Tehran press. Tehran (In Persian).
4. Bishop T., and McBratney A. 2001. A comparison of prediction methods for the creation of field-extent soil property maps. Geoderma 103(1-2), 149-160.
5. Broge N.H., Thomsen A.G., and Greve M.H. 2004. Prediction of topsoil organic matter and clay content from measurements of spectral reflectance and electrical conductivity. Acta Agriculturae Scandinavica, Section B-Soil & Plant Science 54(4), 232-240.
6. Brown D.J., Shepherd K.D., Walsh M.G., Mays M.D., and Reinsch T.G. 2006. Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma 132(3-4), 273-290.
7. Burgess T.M., and Webster R. 1980. Optimal interpolation., and isarithmic mapping of soil properties. European Journal of Soil Science 31(2), 315-331.
8. Cambardella C., Moorman T., Parkin T., Karlen D., Novak J., Turco R., and Konopka A. 1994. Field-scale variability of soil properties in central Iowa soils. Soil science society of America journal 58(5), 1501-1511.
9. Clark R.N. 1999. Spectroscopy of rocks and minerals, and principles of spectroscopy. Manual of remote sensing 3(3-58), 2.2-4.
10. Coleman T., Agbu P., Montgomery O., Gao T., and Prasad S. 1991. Spectral band selection for quantifying selected properties in highly weathered soils. Soil Science 151(5), 355-361.
11. D’acqui L., Pucci A., and Janik L. 2010. Soil properties prediction of western Mediterranean islands with similar climatic environments by means of mid‐infrared diffuse reflectance spectroscopy. European journal of soil science 61(6), 865-876.
12. Dematte J.A.M., and Garcia G.J. 1999. Alteration of soil properties through a weathering sequence as evaluated by spectral reflectance. Soil Science Society of America Journal 63(2), 327-342.
13. Gong Z., Kawamura K., Ishikawa N., Goto M., Wulan T., Alateng D., Yin T., and Ito Y. 2015. MODIS normalized difference vegetation index (NDVI) and vegetation phenology dynamics in the Inner Mongolia grassland. Solid Earth 6(4), 1185.
14. Goovaerts P. 1999 .Geostatistics in soil science: state-of-the-art and perspectives. Geoderma 89(1-2), 1-45.
15. Isaaks E., and Srivastava R. 1989. Applied geostatistics.,(Oxford University Press: New York). Google Scholar, 561.
16. Islam K., Singh B., and McBratney A. 2003. Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy. Soil Research 41(6), 1101-1114.
17. JHA C.S., and Unni N. 1994. Digital change detection of forest conversion of a dry tropical Indian forest region. International Journal of Remote Sensing 15(13), 2543-2552.
18. Journel A.G., and Huijbregts C.J. 1978. Mining geostatistics. Academic press.
19. Jensen J. R., and Lulla K. 1987. Introductory digital image processing: a remote sensing perspective.
20. Khosravi Y., and Esmaeil A. 2017. Spatial analysis of environmental data using geostatistics. Azarlak press. 282pp (In Persian).
21. Lagacherie P., Baret F., Feret J.-B., Netto J.M., and Robbez-Masson J.M. 2008. Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements. Remote Sensing of Environment 112(3), 825-835.
22. Lark R.M. 2010. Two contrasting spatial processes with a common variograms: inference about spatial models from higher-order statistics. European Journal of Soil Science, 61: 479-492.
23. Li J., and Heap A.D. 2011. A review of comparative studies of spatial interpolation methods in environmental sciences: performance and impact factors. Ecological Informatics 6(3-4), 228-241.
24. Liao K., Xu S., Wu J., and Zhu Q. 2013. Spatial estimation of surface soil texture using remote sensing data. Soil science and plant nutrition 59(4), 488-500.
25. Lieb M., Glaser B., and Huwe B. 2012. Uncertainty in the spatial prediction of soil texture: comparison of regression tree and random forest models. Geoderma 170(4), 70-79.
26. Lobell D.B., and Asner G.P. 2002. Moisture effects on soil reflectance. Soil Science Society of America Journal 66(3), ( 722-727).
27. Makabe S., KAKUDA K.i., Sasaki Y., Ando T., Fujii H., and Ando H. 2009. Relationship between mineral composition or soil texture and available silicon in alluvial paddy soils on the Shounai Plain, Japan. Soil Science & Plant Nutrition 55(5), (300-308).
28. McBratney A., and Webster R. 1983. Optimal interpolation and isarithmic mapping of soil properties. V. Co-regionalization and multiple sampling strategies J Soil Sci (34), 137-167.
29. Menut L., Perez C., Haustein K., Bessagnet B., Prigent C., and Alfaro S. 2013. Impact of surface roughness and soil texture on mineral dust emission fluxes modeling. Journal of Geophysical Research: Atmospheres 118(12), 6505-6520.
30. Minasny B., and Hartemink A.E. 2011. Predicting soil properties in the tropics. Earth-Science Reviews 106(1-2), 52-62.
31. Odeh I.O., and McBratney A.B. 2000. Using AVHRR images for spatial prediction of clay content in the lower Namoi Valley of eastern Australia. Geoderma 97(3-4), 237-254.
32. Oliver M.A., and Webster R. 1990. Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information System, 4: 313-332.
33. Page A.L. 1992. Methods of Soil Analysis. ASA and SSSA Publishers: Madison, WI.
34. Soil Survey Staff F. 2014. Keys to soil taxonomy (11th ed). Washington: USDA-NRCS.
35. Stenberg B., Rossel R.A.V., Mouazen A.M., and Wetterlind J. 2010. Visible and near infrared spectroscopy in soil science, Advances in agronomy. Elsevier, pp. 163-215.
36. Sullivan D.G., Shaw J., and Rickman D. 2005. IKONOS imagery to estimate surface soil property variability in two Alabama physiographies. Soil Science Society of America Journal 69(6), 1789-1798.
37. Tesfahunegn G.B., Tamene L., and Vlek P.L.G. 2011. Catchment-scale spatial variability of soil properties and implications on site-specific soil management in northern Ethiopia. Soil and Tillage Research, 117:124–139.
38. Triantafilis J., Odeh I., and McBratney A. 2001. Five geostatistical models to predict soil salinity from electromagnetic induction data across irrigated cotton. Soil Science Society of America Journal 65(3), 869-878.
39. Vieira S.R., and Paz Gonzalez A. 2003. Analysis of the spatial variability of crop yield and soil properties in small agricultural plots. Bragantia 62(1), (127-138).
40. Vincent R. K. (1997). Fundamentals of geological and environmental remote sensing (Vol. 366). Upper Saddle River, NJ: Prentice Hall.
41. Warrington D., Mamedov A., Bhardwaj A., and Levy G. 2009. Primary particle size distribution of eroded material affected by degree of aggregate slaking and seal development. Eur. J. Soil Sci 60, 84–93.
42. Western A.W., Zhou S.-L., Grayson R.B., McMahon T.A., Blöschl G., and Wilson D.J. 2004. Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes. Journal of Hydrology 286(1-4), 113-134.
43. Wetterlind J., and Stenberg B. 2010. Near‐infrared spectroscopy for within‐field soil characterization: small local calibrations compared with national libraries spiked with local samples. European Journal of Soil Science 61(6), 823-843.
44. Wu C., Wu J., Luo Y., Zhang L., and DeGloria S.D. 2009. Spatial prediction of soil organic matter content using cokriging with remotely sensed data. Soil Science Society of America Journal 73(4), 1202-1208.
45. Yates S., and Warrick A. 1987. Estimating Soil Water Content Using Cokriging 1. Soil Science Society of America Journal 51(1), 23-30.
46. Zhang R., Warrick A., and Myers D. 1992. Improvement of the prediction of soil particle size fractions using spectral properties. Geoderma 52(3-4), 223-234.
47. Zhao Z., Chow T.L., Rees H.W., Yang Q., Xing Z., and Meng F.R. 2009. Predict soil texture distributions using an artificial neural network model. Computers and electronics in agriculture 65(1), 36-48.
CAPTCHA Image