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

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.