Article Main

Binod K. Vimal Rajkishore Kumar C. D. Choudhary Sunil Kumar Rakesh Kumar Y. K. Singh Ragini Kumari

Abstract

Colour in soils as well as other object is the visual perceptual property which is perceived by human eye. They are governed by spectrum of light corresponding to wavelength or reflected energy of the material. Developed model for soil acidity is based on visual interpretation, principal component and spectral enhancement techniques by using of the satellite image (IRS LISS III, 2014). In this context, red soil patch is much sensitive in red spectral band comparison to green and blue spectral bands and perceived as red tone by human eyes but same soil patch appears green in false colour composite (FCC) image of NIR (0.70-0.80?m), Red (0.60-0.70 ?m) and Green (0.50-0.60?m) bands. The maximum coverage of red soil patches having low pH < 6.5 (1:2.5) was recognized in 44.07 per cent of the total geographical area (3019.56 sq.km) under Banka district. Maximum red soil patches having their acidity were recognised in Katoria (18.56%), Chanan (15.15%), Bounsi (10.44%) and Banka (9.92%) blocks. Overall results indicated that variation of tone in different bands helps for the separation of red soil patches.

Article Details

Article Details

Keywords

NIR band, RS-GIS, Satellite image, Spectral signature

References
Ben-Dor, E. (2002). Quantitative remote sensing of soil properties. Adv. Agron., 2002, 75, 173–243.
Bowers, S. and Hanks, R., (1965). Reflection of radiant energy from soils. Soil Sci., 100, 130–138.
Bricklemyer, R.S.; Brown, D.J. On-the-go visnir (2010). Potential and limitations for mapping soil clay and or-ganic carbon. Comput. Electron. Agric., 70, 209-216.
Buckman, H.O. and Brady, N.C. Weil, R.R. (2002). The nature and properties of soils, 13th Edition. Prentice Hall.
Crosta, A. P., and Moore, J. McM., (1989). Enhancement of Landsat Thematic Mapper imagery for residual soil mapping in SW Minas Gerais State, Brazil: a prospect-ing case history in Greenstone Belt terrain. Proceedings of the Seventh Thematic Conference on Remote Sensing for Exploration Geology, Calgary, Alberta, Canada, 2- 6 October, 1173-1187.
Demattê, J.A.M., Fiorio, P.R., Araújo, S.R. (2010). Variation of routine soil analysis when compared with hyperspec-tral narrow band sensing method. Remote Sens., 2010, 2, 1998–2016.
Engman, E.T. and Chauhan, N. (1995). Status of microwave soil moisture measurements with remote sensing. Re-mote Sensing Environ., 51 (1): 189–198.
Gary A. Shaw and Hsiao-hua K. Burke, (2003). Spectral Imaging for Remote Sensing, Lincon Laboratory Jour-nal Volume 14, Number1.
Ghosh, R., Padmanabhan, N., Patel, K.C. and Siyolkar, R., Soil fertility parameter retrieval and mapping using hyperion data (2012). In Investigations on Hyperspec-tral Remote Sensing Applications (eds Panigrahy, S. and Manjunath, K.R.), Space Applications Centre (ISRO), Ahmedabad, pp. 29–31.
Hussien, H.M., Karkush, M.O. and Zibbon, A.R.T. (2014). Studying the effects of contamination on soil properties using remote sensing. Journal of Engineering, 6(20), 78-90
Jackson, M.L. (1973). Soil Chemical Analysis. Prentice- hall of India Pvt. Ltd, New Delhi, pp.40.
Jenny, H. (1941). Factors of Soil Formation a System of Quantitative Pedology; McGraw-Hill Book Company, Inc.: New York, NY, USA.
Kadupitiya, H.K., Sahoo, R.N., Ray, S.S., Chakraborty, D. and Ahmed, N. (2010). Quantitative assessment of soil chemical properties using visible (VIS) and near-infrared (NIR) proximal hyperspectral data. Trop. Ag-ric., 158: 41–60.
Lillesand, Thomas M., Ralph W. Kiefer, and Jonathan W. Chipman (2005). Remote Sensing and Image Interpreta-tion, Fifth edition. Wiley, New York.
Manchanda, M.L. Kudrat, Mand Tiwari, A.K. (2002). Soil survey and mapping using remote sensing. Tropical Ecology, 43: 61-74.
Metternicht, G.I. and Zinck, J.A. (2003). Remote sensing of soil salinity: potentials and constraints. Remote Sensing Environ., 85 (1): 1–20.
Panda, B.C. (2009). Remote sensing: Principle and applica-tion. Viva Books Pvt Ltd.
Ray, S.S., Singh, J.P., Das, G. and Panigrahy, S. (2004). Use of high resolution remote sensing data for generating site-specific soil management plan. Int. Arch. Photogramm. Remote Sensing Spatial Inf. Syst. B., 35 (7): 127–131.
Ray, S.S., Singh, J.P., Dutta, S. and Panigrahy, S. (2002). Analysis of within field variability of crop and soil us-ing field data and spectral information as a pre-cursor to precision crop management. Int. Arch. Photogramm. Remote Sensing Spatial Inf. Syst. C, 34 (7): 302–307.
Saxena, R.K., Verma , K.S., Chary, G.R., Srivastava, R. and Bartwal, A.K. (2000). IRS-IC data application in water-shed characterization and management. International Journal of Remote Sensing, 21, 3197-3208.
Spatial Analyst (2012). Exelis Visual Information Solutions: Boulder, CO, USA, Available online: http://geol.hu/data/online_help/SpectralAnalyst.html.
Vinay, K., Vimal, B.K., K., Rakesh, K., Rakesh and K., Mukesh, (2013). Determination of soil pH by using digital image processing technique. Journal of Applied and Natural Science, 6 (1): 14-18
Viscarra Rossel RA, Cattle S., Ortega A., Fouad Y. (2009). In situ measurements of soil colour, mineral composi-tion and clay content by vis–NIR spectroscopy. Ge-oderma 150, 253–266.
Viscarra Rossel, R.A. and Behrens, T. (2010). Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma, 158, 46–54.
Section
Research Articles

How to Cite

Signature capture of red soil patches and their acidity-A case study of Banka district, Bihar, India. (2016). Journal of Applied and Natural Science, 8(2), 874-878. https://doi.org/10.31018/jans.v8i2.889