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Afr. J. Agric. Res.


Vol. 6 No. 2
1



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Scientific Research and Essays

 

African Journal of Agricultural Research Vol. 6(21), pp. 4973-4982, 5 October, 2011

Available online at http://www.academicjournals.org/AJAR

DOI: 10.5897/AJAR11.634

ISSN 1991-637X ©2011 Academic Journals

 

 

Full Length Research Paper

 

Detection of salt affected soil in rice-wheat area using satellite image

 

Faheem Iqbal

 

Institute of Geographical Information Systems, National University of Sciences and Technology,

H-12 Campus, Islamabad, Pakistan. E-mail: faheem.engr@gmail.com.  Tel: 0092-0333-8971239, 0092-051-90854477.

 

Accepted 13 July, 2011

 

 Abstract

 

Pakistan’s agricultural system is predominately irrigated, which consumes 90% of fresh water resources and contributes 80% to the national production. It is ironic that the irrigated areas are the ones that are afflicted most by the twin menaces of waterlogging and salinity. Climate change is likely to exacerbate this soil degradation process by way of heavy rainfalls and by increased evaporation, respectively. Detection of soil salinity is usually done by laborious soil sampling. A study, to delineate surface soil salinity in the prime rice-wheat cropping area of Pakistan, was conducted. The study employed an index-based approach of using optical remote sensing data in combination with geographic information system. The effectiveness of different satellite imagery indicators was examined. Several combinations of the ratio of signals received in different spectral bands were used for development of this index. Near infrared and thermal IR spectral bands proved to be most effective as this combination helped easy detection of salt affected area from the non saline area. Results showed that 19% of the rice-wheat cropping area of Gujranwala district in Rachna Doab of central Punjab province of Pakistan is salt affected. These results are in agreement with the published survey data. Seasonal dependency of salinity was also analyzed so as to obtain correct classification.

 

Key words:  Salinity, remote sensing index, salinity index.

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