Detection and modeling of soil salinity variations in arid lands using remote sensing data
Soil salinization is a ubiquitous global problem.
The literature supports the integration of remote sensing
(RS) techniques and field measurements as effective
methods for developing soil salinity prediction models.
The objectives of this study were to (i) estimate the level
of soil salinity in Abu Dhabi using spectral indices and
field measurements and (ii) develop a model for detecting
and mapping soil salinity variations in the study area
using RS data. We integrated Landsat 8 data with the electrical
conductivity measurements of soil samples taken
from the study area. Statistical analysis of the integrated
data showed that the normalized difference vegetation
index and bare soil index showed moderate correlations
among the examined indices. The relation between these
two indices can contribute to the development of successful
soil salinity prediction models. Results show that
31% of the soil in the study area is moderately saline and
46% of the soil is highly saline. The results support that
geoinformatic techniques using RS data and technologies
constitute an effective tool for detecting soil salinity by
modeling and mapping the spatial distribution of saline
soils. Furthermore, we observed a low correlation between
soil salinity and the nighttime land surface temperature.