Modeling and Mapping of Salt-Affected Soils through Spectral Indices in Inland Plains of Semi-arid Agro-Ecological Region

Abstract

Mapping the salt-affected soils (SAS) through spectral indices derived from satellite imagery is essential for its monitoring at a frequent interval and for the management. Several salinity indices have been developed based on the combination of spectral bands for predicting soil salinity in the arid environment. Present study attempted to identify and map the salt-affected soils (SAS) in inland plains of semi-arid agro-ecological region of Tamil Nadu, India using satellite image (Resourcesat-1(IRS-P6) LISS IV). Vertisols and Inceptisols are the major soil orders cultivated under paddy, sugarcane and pulses. An integrated approach of spectral bands and indices along with analytical data of samples collected from the field was used to develop multiple regression equations for prediction. The best linear regression model was selected based on adjusted R2 and MSE values for the prediction of pH, EC, and ESP. The variation observed between salt-affected soils predicted by multiple regression equations and measured data were 15, 10, and 5 percent for pH, EC, and ESP, respectively, due to weak variation in soil properties, sample density, better vegetation cover by management practices.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Abbas, A., & Khan, S. (2007) Using remote sensing techniques for appraisal of irrigated soil salinity. In: Oxley L, Kulasiri D (eds) Advances and applications for management and decision making land, water and environmental management: integrated systems for sustainability MODSIM07, pp.2632–2638.

  2. Adcock, D., McNeill, A. M., McDonald, G. K., & Armstrong, R. D. (2007). Subsoil constraints to crop production on neutral and alkaline soils in south-eastern Australia: A review of current knowledge and management strategies. Australian Journal of Experimental Agriculture, 47, 1245–1261.

    Article  Google Scholar 

  3. Allbed, A., Kumar, L., & Sinha, P. (2014). Mapping and modelling spatial variation in soil salinity in the Al Hassa Oasis based on remote sensing indicators and regression techniques. Remote Sensing, 6, 1137–1157.

    Article  Google Scholar 

  4. Bannari, A., Guedon, A. M., El-Harti, A., Cherkaoui, F. Z., & El-Ghmari, A. (2008). Characterization of slightly and moderately saline and sodic soils in irrigated agricultural land using simulated data of advanced land imaging sensor. Communications in Soil Science and Plant Analysis, 39, 19–20.

    Article  Google Scholar 

  5. Berger, K. C., & Truog, E. (1939). Boron determination in soils and plants using the quinalizar in reaction. Industrial and Engineering Chemistry Analytical Edition, 11, 540–545.

    Article  Google Scholar 

  6. Bouman, B. A. M., & Tuong, T. P. (2001). Field water management to save water and increase its productivity in irrigated lowland rice. Agricultural Water Management, 49, 11–30.

    Article  Google Scholar 

  7. Broge, N. H., & Mortensen, J. V. (2002). Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sensing of Environment, 81, 45–57.

    Article  Google Scholar 

  8. Dehni, A., & Lounis, M. (2012). Remote sensing techniques for salt affected soil mapping: Application to the Oran region of Algeria. Procedia Engineering, 33, 188–198.

    Article  Google Scholar 

  9. Douaoui, A. E. K., 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 

  10. FAO. (2005) Global network on integrated soil management for sustainable use of salt affected soils. Rome, Italy. FAO Land and Plant Nutrition Management Service.

  11. Farifteh, F., Farshad, A., & George, R. G. (2006). Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma, 130, 191–206. https://doi.org/10.1016/j.geoderma.2005.02.003.

    Article  Google Scholar 

  12. Gracia, L., Eldeiry, A., & Elhaddad, A. (2005). Estimating soil salinity using remote sensing data. Proceedings of the 2005 Central Plains Irrigation Conference, 11, 223–288.

  13. ICAR-CSSRI. (2015). ICAR-Central Soil Salinity Research Institute Vision 2050. New Delhi: Indian Council of Agricultural Research.

    Google Scholar 

  14. Khan, N. M., Rastoskuev, V. V., Sato, Y., & Shiozawa, S. (2005). Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agricultural Water Management, 77, 96–109.

    Article  Google Scholar 

  15. Minhas, P. S., & Samra, J. S. (2003). Quality assessment of water resources in the Indo-Gangetic Basin part in India. India: Central Soil Salinity Research Institute.

    Google Scholar 

  16. Naidu, R., & Rengasamy, P. (1993). Ion interactions and constraints to plant nutrition in Australian sodic soils. Australian Journal of Soil Research, 31, 801–819.

    Article  Google Scholar 

  17. Piper, C. S. (1966). Soil and plant analysis. Bombay, India: Reprinted by Hans Publishers.

    Google Scholar 

  18. Qadir, M., Noble, A. D., Schubert, S., Thomas, R. J., & Arslan, A. (2006). Sodicity-induced land degradation and its sustainable management: Problems and prospects. Land Degradation and Development, 17, 661–676. https://doi.org/10.1002/ldr.751.

    Article  Google Scholar 

  19. Qadir, M., & Schubert, S. (2002). Degradation processes and nutrient constraints in sodic soils. Land Degradation and Development, 13, 275–294.

    Article  Google Scholar 

  20. Samra, J. S., Singh, G., & Ramakrishna, Y. S. (2006). Drought management strategies in India (p. 277). New Delhi: ICAR.

    Google Scholar 

  21. Saxena, R.K., Sharma, R. C., Verma, K. S., Pal, O. K.,&Mandai, A. K. (2004) Salt Affected Soils, Etah District (Uttar Pradesh). NBSS-CSSRI Publ.No 108, NBSS&LUP, Nagpur, pp–85.

  22. Sehgal, J., & Abrol, I. P. (1994). Soil degradation in India status and impact (p. 80). India: Oxford and IBH.

    Google Scholar 

  23. Soil Survey Staff. (2008) Keys to soil taxonomy. 8th Edison. SCS, USDA, Washigton, DC.

  24. Sumner, M. E., & Miller, W. P. (1996). Cation exchange capacity and exchange coefficients. In D. L. Sparks, A. L. Page, & P. A. Helmke (Eds.), Methods of Soil Analysis Part 3, Chemical Methods (pp. 1201–1229). Madison: Soil Science Society of America.

    Google Scholar 

  25. USDA. (2014) Saline, Saline-Sodic & Sodic Soils. Agro.Tech.Note.76. (http://www.nrcs.usda.gov/wps/portal/nrcs/detail/nm/technical/?cid=nrcs144p2_068965).

  26. Walkley, A., & Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sciences, 37, 29–38.

    Article  Google Scholar 

  27. Whitney, D. A. (1998) Soil salinity. In: Brown, J.R. (Ed.), Recommended Chemical Soil Test Procedures for the North Central Region 221. North Central Regional Publication, Missouri Agriculture, pp. 59–60 (revised). (Exp, Stn. Bull. SB1001).

  28. Wilding, L. P. (1985). Spatial Variability: its documentation, accommodation, and implication to soil surveys. In D. R. Nielsen & J. Bouma (Eds.), Soil Spatial Variability. Netherlands: Pudoc, Wageningen.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Lalitha Manickam.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Verify currency and authenticity via CrossMark

Cite this article

Manickam, L., Subramanian, D., Khandal, S. et al. Modeling and Mapping of Salt-Affected Soils through Spectral Indices in Inland Plains of Semi-arid Agro-Ecological Region. J Indian Soc Remote Sens (2021). https://doi.org/10.1007/s12524-021-01321-w

Download citation

Keywords

  • Salt-affected soils
  • Soil properties
  • Spectral indices
  • Multi-linear regression