Diagnosis and Prognosis of Salt-Affected Soils and Poor-Quality Waters Using Remote Sensing and Proximal Techniques

  • Madhurama Sethi
  • D. S. Bundela
  • Rajkumar


Salinisation of soil and water resources has emerged as a major environmental predicament adversely impacting sustainability of agriculture and crop productivity in arid and semiarid regions. Diagnosis and prognosis of salinity is a challenging task due to extreme variability in space and time necessitating the application and use of smart modern tools and techniques for providing information on extent and spatial distribution of salt-affected land and water. Satellite remote sensing has been successfully used to diagnose and map moderate to heavy salt encrustation on bare soils and directly or indirectly through crop/vegetation growth patterns. Attempts have been made to resolve light-surface salt encrustation with crop cover and root-zone salinity as well as deeper groundwater salinisation using airborne remote sensing and proximal sensing-based geophysical tools. An integrated methodology combining proximal and remote-sensing data has provided the best solution for diagnosing various levels of salinisation of land and water. More recently data from aerial photographs, UAVs, airborne electromagnetic survey, satellite-based multispectral, hyperspectral and microwave, ground-penetrating radar and electromagnetic induction and geo-electrical resistivity devices along with improved data processing techniques in GIS have enhanced the capability for accurate identification and diagnosis of salty land and water. Nonetheless ground truth and laboratory analysis remain essentially and integrally important to achieve accuracy in providing a prognosis for management for sustainability of agricultural land and groundwater.


Geographic Information System Soil Salinity Synthetic Aperture Radar Vertical Electric Sound Landsat Thematic Mapper 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer India 2016

Authors and Affiliations

  1. 1.ICAR-Central Soil Salinity Research InstituteKarnalIndia

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