Soil salinity detection from satellite image analysis: an integrated approach of salinity indices and field data

• Published in: Environmental monitoring and assessment Vol. 188; no. 2; p. 119
• Main Authors: Morshed, Md. Manjur, Islam, Md. Tazmul, Jamil, Raihan
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2016; Springer Nature B.V
• Subjects: Accuracy; Atmospheric Protection/Air Quality Control/Air Pollution; Coastal zone; coasts; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Environmental Monitoring - methods; equations; Geographic Information Systems; Geography; image analysis; Integrated approach; Models, Chemical; monitoring; Monitoring/Environmental Analysis; Principal components analysis; regression analysis; Remote sensing; Salinity; Salt; Satellite Imagery; Satellites; Soil - chemistry; Soil - standards; Soil salinity; surveys; Salinity indices; Regression; Bangladesh; Landsat image; Remote sensing
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-015-5045-x

This paper attempts to detect soil salinity from satellite image analysis using remote sensing and geographic information system. Salinity intrusion is a common problem for the coastal regions of the world. Traditional salinity detection techniques by field survey and sampling are time-consuming and expensive. Remote sensing and geographic information system offer economic and efficient salinity detection, monitoring, and mapping. To predict soil salinity, an integrated approach of salinity indices and field data was used to develop a multiple regression equation. The correlations between different indices and field data of soil salinity were calculated to find out the highly correlated indices. The best regression model was selected considering the high R ² value, low P value, and low Akaike’s Information Criterion. About 20 % variation was observed between the field data and predicted EC from the satellite image analysis. The precision of this salinity detection technique depends on the accuracy and uniform distribution of field data.