International network for natural sciences – research journal
  • mendeley icon
  • linkedin icon
  • google plus icon
  • twitter icon
  • google scholar icon
  • facebook icon

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

By: Ali Khadem, Zohre Ebrahimi, Hamed Haghparast

Key Words: Geometric standard deviation, Homogeneity, Remote sensing, Linear, Non-linear, Dry region, Iran

J. Bio. Env. Sci. 10(5), 25-36, May 2017.

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.

| Views 31 |

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

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.
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.
J. Bio. Env. Sci. 10(5), 25-36, May 2017.
http://www.innspub.net/jbes/evaluation-aster-images-capability-identification-homogenous-heterogeneous-soils-dry-regions-based-linear-non-linear-relations-case-study-khatam-plain-iran/
Copyright © 2017
By Authors and International Network for
Natural Sciences (INNSPUB)
http://innspub.net
brand
innspub logo
english language editing
  • CALL FOR PAPERS
    CALL FOR PAPERS
    Publish Your Article
  • CALL FOR PAPERS
    CALL FOR PAPERS
    Submit Your Article
INNSPUB on FB
Email Update