Evaluation of ASTER images capability for identification of homogenous and heterogeneous soils in dry regions based on linear and non-linear relations Case Study: Khatam plain, Iran
Paper Details
Evaluation of ASTER images capability for identification of homogenous and heterogeneous soils in dry regions based on linear and non-linear relations Case Study: Khatam plain, Iran
Abstract
Degree of homogeneity of soil particles is one of the major factors effecting soli spectral reflectance which is calculated based on the Geometric standard deviation of soil particles. This study evaluates the application of ASTER imagery for identification of homogenous and heterogeneous soils in dry regions with special reference to linear and non-linear equations in Khatam plain of Yazd province, Iran. To do this, soil samplings (76 samples) from the soil surface has been done in 2007/08/23 and were measured values of the texture fragments by using of the hydrometer method. Finally, Geometric standard deviations of soil particles were calculated for each sample point. After doing the geometric and radiometric corrections and applying of the average filters on the satellite images, some processing operations were done. Correlation coefficient between soil texture data and soil spectral reflectance for homogenous (Geometric standard deviation <10) and heterogeneous soil samples (Geometric standard deviation ≥ 10) were calculated based on the linear and non- linear equations. The results showed that the Near Infra Red (NIR) band of ASTER sensor can be effective on the spectral reflectance of homogenous soils and identification of clay texture of heterogeneous soils. Also, the results of this study indicated that Short Wave Infra Red (SWIR) has a remarkable effect on the soil spectral of sand and silt particles in heterogeneous soils.
Bannari A, Morin D, Bonn F, Huete AR. 1995. A review of vegetation indices. Remote Sensing Reviews 13, 95-120. http://dx.doi.org/10.1080/02757259509532298
Ben-Dor E, Chabrillat S, Dematte JAM, Taylor GR, Hill J, Whiting ML, Sommer S. 2009.Using imaging spectroscopy to study soil properties. Remote Sensing of Environment 113, 38-55. http://dx.doi.org/10.1016/j.rse.2008.09.019
Bragato G. 2004. Fuzzy continuous classification and spatial interpolation in conventional soil survey for soil mapping of the lower Piave plain. Geoderma 118,1–16. http://dx.doi.org/10.1016/S0016-7061(03)00166-6
Brown DJ, Shephered K D, Walsh MG, Mays MD, Reinsch TG. 2006. Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma 132, 273-290. http://dx.doi.org/10.1016/j.geoderma.2005.04.025
Bybordi M. 2010. Soil Physics. Tehran University Publications, First edition, 9th Printing.
Casa R, Palombo A, Pignatti S. 2013. A comparison of sensor resolution and calibration strategies for soil texture estimation from hyperspectral remote sensing, Geoderma 197, 17-26. http://dx.doi.org/10.1016/j.geoderma.2012.12.016
Coleman TL, Agbu PA, Montgomery OL. 1993. Spectral differentiation of surface soils and soil properties: Is it possible from space platforms? Soil Science 155, 283-293. http://journals.lww.com/soilsci/Abstract/1993/04000/Spectral_Differentiation_of_Surface_Soils_and_Soil.7.aspx
Cozzolino D, Moron A. 2003. The potential of near-infrared reflectance spectroscopy to analyse soil chemical and physical characteristics. Journal of Agricultural Sciences 140, 65-71. https://doi.org/10.1017/S0021859602002836
Curcio D, Ciraolo G, Asaro FD, Minacapilli M. 2013. Prediction of soil texture distributions using VNIR-SWIR reflectance spectroscopy. Environmental Sciences 19, 494-503. https://doi.org/10.1016/j.proenv.2013.06.056
Dematte JAM, Fiorio PR, Ben-Dor E. 2009. Estimation of soil properties by orbital and laboratory reflectance means and its relation with soil classification.The Open Remote Sensing Journal 2, 12-23. https://doi.org/10.2174/1875413900902010012
DingY, Xu S, Zhu K. 1989. Application of remote sensing techniques on 1:500,000 soil mapping in Nanjing, Jiangsu Province, China. (In Chinese.) Turang 6, 304–306.
Dwived I RS, Ramana, K V, Thammappa S. 2001. The Utility of IRS-1C LISS-Ill and PAN-merged data for mapping salt affected soil.photogrammetric Engineering & Remote Sensing, Solute modeling, and geophysics. Geoderma 130, 191-206. http://info.asprs.org/publications/pers/2001journal/october/2001_oct_1167-1175.pdf
Ebrahimi Z, Vali AA, Ghazavi R, Haghparast H. 2013. Probe the impact of soil texture components and geometric mean of particles diameter on the soil spectral Response ,Case Study: The part of Khatam plain, Yazd Province. Iranian Journal of Quantitative Geomorphological Researches 3, 115-128.
Fox GA, Sabbagh GJ. 2003. Estimation of soil organic matter from red and near-infrared remotely sensed data using a soil line Euclidean distance technique. Soil Science Society of American Journal 66, 1922-1928. https://doi.org/10.2136/sssaj2002.1922
Feng L, Fraster W, Waddle A. 2012. Soil texture mapping over low relief areas using land surface feedback dynamic patterns extracted from MODIS, Geoderma 171, 44-52. http://dx.doi.org/10.1016/j.geoderma.2011.05.007
Hillel D. 1980. Applications of Soil Physics. Academic Press Int, 385 p.
Jenny H. 1941. Factors of soil formation. McGraw-Hill. New York.
Legacherie Ph, Baret F, Feret J, Netto JM, Robbez-Masson JM. 2008. Estimation of soil clay and calcium carbonate using laboratory, field and airborn hyperspectral measurements. Remote Sensing of Environment 112, 825-835. http://dx.doi.org/10.1016/j.rse.2007.06.014
McKenzie NJ, Ryan PJ. 1999. Spatial prediction of soil properties using environmental correlation. Geoderma 89, 67–94. http://dx.doi.org/10.1016/S0016-7061(98)00137-2
Shirazi MA, Boersma L. 1984. A unifying quantitative analysis of soil texture. Soil Science Society of America Journal 48, 142-147. https://doi.org/10.2136/sssaj1984.03615995004800010026x
Shirazi M, Matinfar M, Nematolahi MJ, Zehtabiyan GR. 2011.Comparison of Information Content of Aster and LISS-III Bands inArid Areas (Case study: Damghan Playa), Journal of Applied RS and GIS Techniques in Natural Resource Science 1, 31-49. http://en.journals.sid.ir/ViewPaper.aspx?ID=198845
Sullivan D.G, Shaw JN, Rickman D. 2005. IKONOS imagery to estimate surface soil property variability in two Alabama physiographies. Soil Science Society of America Journal 69, 1789-1798. https://doi.org/10.2136/sssaj2005.0071
Soil Survey Staff. 1996. Soil Survey Laboratory MethodsManual. Soil Survey Investigations Report No. 42, version 3.0, USDANRCS National Soil Survey Center, Washington, D.C. www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb1253872.pdf
Zhu AX, Liu F, Li B, Pei T, Qin CH, Liu G, Wang Y, Chen Y, Ma X, Qi F, Zhou CH. 2010. Differentiation of Soil Conditions over Low Relief Areas Using Feedback Dynamic Patterns. Soil Science Society of America Journal 74, 861-869. https://doi.org/10.2136/sssaj2008.0411
Ali Khadem, Zohre Ebrahimi, Hamed Haghparast (2017), Evaluation of ASTER images capability for identification of homogenous and heterogeneous soils in dry regions based on linear and non-linear relations Case Study: Khatam plain, Iran; JBES, V10, N5, May, P25-36
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