Skip to main content
Log in

Hyperspectral remote sensing data derived spectral indices in characterizing salt-affected soils: a case study of Indo-Gangetic plains of India

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Hyperspectral remote sensing (Hyperion EO-1) data has emerged as most promising tool in quantifying severity of salt-affected soils. The study deals with identifying sensitive spectral bands (wavelength regions) for salinity parameters and thereafter used to compute spectral indices viz. Salinity index (SI), Brightness index (BI), Normalized Differential Salinity Index (NDSI), Combined Spectral Response Index (COSRI) and Coloration index (CI). Six sensitive hyperspectral bands (Band 9, 20, 22, 28, 29 and 46) of Hyperion-1 satellite data were identified to generate the spectral indices. The relationship between these spectral indices and salinity parameters of electrical conductivity (EC), sodium adsorption ratio (SAR) and exchangeable sodium percentage (ESP) were established to generate maps showing severity of salt-affected soils of the area. The severity maps were categorized into classes of normal, slight, moderate and highly showing the spatial distribution of severity of salt affected soils. Among these spectral indices, SI shown highest correlation coefficient (r 2) with the parameters of ECe (r 2 = 0.777), SAR (r 2 = 0.801) and ESP (r 2 = 0.804) followed by BI, COSRI and CI. The Hyperion data has shown the potential to assess severity of salt-affected soils for large area which may very useful for identifying the area for carring out reclamation measures and management planning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Abrol AP, Bhumbla DR (1971) Saline and alkali soils in India- their occurrence and management. World Soil Resour, Rep, pp 41–42

    Google Scholar 

  • Beck R (2003) EO-1 User Guide v 2. 3. Department of Geography, University of Cincinnati

  • Ben-Dor E, Patkin K, Banin A, Karnieli A (2002) Mapping of several soil properties using DAIS-7915 hyperspectral scanner data: a case study over clayey soils in Israel. Int J Remote Sensing 23(6):1043–1062

    Article  Google Scholar 

  • Clark RN, King TVV, Klejwa M, Swayze GA, Vergo N (1990) High spectral resolution reflectance spectroscopy of minerals. J Geophys Res 95(12):653–680

    Google Scholar 

  • Cloutis EA (1996) Hyperspectral geological remote sensing: evaluation of analytical techniques. Int J Remote Sens 17:2215–2242

    Article  Google Scholar 

  • Datt B, Mc Vicar TR, Niel TG, Jupp DL, Pearlman JS (2003) Preprocessing EO-1 Hyperian hyperspectral data to support the application of agricultural indexes. IEEE Trans Geosci Remote Sens 41:1246–1259

    Article  Google Scholar 

  • Dehaan RL, Taylor GR (2002) Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization. Remote Sens Environ 80:406–417

    Article  Google Scholar 

  • Douaoui AEK, Nicolas H, Walter C (2006) Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data. Geoderma 134(1–2):217–230

    Article  Google Scholar 

  • Eswaran H, Lal R, Reich PF (2001) Land degradation: An overview. In: Bridges ID, Hannam LR, Oldeman FWTP, de Vries SJS, Sombatpanit S (eds) Response to land degradation EM. Oxford and IBH Publishing, New Delhi

    Google Scholar 

  • Farifteh J, Van der Meer F (2007) Similarity measures for spectral discrimination of salt-affected soils. Int J Remote Sens 28(23):5273–5293

    Article  Google Scholar 

  • Farifteh J, Van der Meer F, Van der Meijde M, Atzberger CG (2008) Spectral characteristics of salt-affected soils: a laboratory experiment. Geoderma 145(3–4):196–206. doi:10.1016/j.geoderma.2008.03.011

    Article  Google Scholar 

  • Fernández-Buces N, Siebe C, Cram S, Palacio JL (2006) Mapping soil salinity using a combined spectral response index for bare soil and vegetation: a case study in the former lake Texcoco. J Arid Environ 65(4):644–667

    Article  Google Scholar 

  • Goodenough DG, Dyk A, Niemann O, Pearlman JS, Chen H, Han T, Murdoch M, West C (2003) Processing HYPERION and ALI for forest classification. IEEE Trans Geosci Remote Sens 41(2):1321–1331

    Article  Google Scholar 

  • Huberty CJ, Olejnik S (2006) Applied MANOVA and Discriminant Analysis, 2nd edn. Wiley, Hoboken

    Book  Google Scholar 

  • Lu N, Zhang Z, Gao Y (2005) Recognition and mapping of soil salinization in arid environment with hyperspectral data, vol 6. In: Proceedings of IEEE Int Geosci and Remote Sens symposium (IGARSS’05) New York, pp 4520–4523

  • Metternicht GI, Zinck JA (2003) Remote sensing of soil salinity: potentials and constraints. Remote Sens Environ 85(1):1–20

    Article  Google Scholar 

  • Mougenot B, Pouget M, Epema GF (1993) Remote sensing of salt affected soils. Remote Sens Rev 7(3–4):241–259

    Article  Google Scholar 

  • Rao BRM, Dwivedi RS, Venkataratnam L, Ravi Sankar T, Thammappa SS, Bhargawa GP, Singh AN (1991) Mapping the maginitude of sodicity in part of Indo-Gangetic Plains of Uttar Pradesh, using Landsat-TM data. Int J Remote Sens 12(3):419–425

    Article  Google Scholar 

  • Richards LA (1954) Diagnosis and improvement of saline and alkali soils. In: Agriculture handbook No. 60, US Department of Agriculture, Washington, DC

  • Shepherd KD, Walsh MG (2002) Development of reflectance spectral libraries for characterization of soil properties. Soil Sci Soc Am J 66:988–998

    Article  Google Scholar 

  • Shi Z, Huang MX (2007) Evaluating reclamation levels of coastal saline soil using laboratory hyperspectral data Eur. Soil Sci 40(10):1095–1101

    Google Scholar 

  • Szabolcs I (1989) Salt-affected soils. CRC Press, Boca Raton, FL

    Google Scholar 

  • Weng Y, Gong P, Zhu Z (2008) Soil salt content estimation in the Yellow River delta with satellite hyperspectral data. Can. J Remote Sens 34(3):259–270

    Google Scholar 

  • Weng Y, Gong P, Zhu Z (2010) A spectral index for estimating soil salinity in the Yellow River Delta region of China using EO-1 Hyperion data. Pedosphere 20(3):378–388

    Article  Google Scholar 

  • Wu J, Liu Y, Wang J, He T (2010) Application of Hyperion data to land degradation mapping in the Hengshan region of China. Int J Remote Sens 31(19):5145–5161

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Suresh Kumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, S., Gautam, G. & Saha, S.K. Hyperspectral remote sensing data derived spectral indices in characterizing salt-affected soils: a case study of Indo-Gangetic plains of India. Environ Earth Sci 73, 3299–3308 (2015). https://doi.org/10.1007/s12665-014-3613-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-014-3613-y

Keywords

Navigation