Evaluation of the Efficiency of Spectral Data and Indices Derived from OLI and TIRS Sensors in Estimating Soil Salinity in Arid Regions of Southern Ilam Province

Document Type : Original Article

Authors

1 Associate Prof. of Soil Science, College of Agriculture, Lorestan University, Lorestan

2 Ph.D. Student of Soil Science, College of Agriculture, Lorestan University, Lorestan

3 Graduate Master of Soil Science, College of Agriculture, Ilam University, Ilam

Abstract

Soil salinity is one of the most important environmental problems, and the identification and zoning
of saline soils is difficult due to the need for sampling and laboratory analysis, as well as having
temporal and spatial variability. In recent years, the use of satellite imagery has always been of
interest to experts due to its ease of use and ability to detect phenomena. Remote sensing information
greatly aids the study of soil salinity and can be helpful in identifying salinity values. In this study,
220 soil samples were collected from Meymeh area of Dehloran, south of Ilam province, according to
the type of study and physiographic types and soil units. Then, pH and EC values were measured
using standard methods. Soil salinity values were evaluated using correlations between EC electrical
conductivity values obtained from ground data and variables obtained from Landsat 8 satellite images
including bands, salinity indices, vegetation indices and soil indices. Finally, the soil surface salinity
estimation model was obtained using stepwise regression method. This method involves the automatic
selection of independent variables, and with the availability of statistical software packages, it is
possible to do so even in models with hundreds of variables. In previous studies, indicators and bands
have been used separately and in a limited way, but in this study, an attempt has been made to use a
combination of different indicators more widely, and finally to achieve the best relationship by
eliminating the indicators that have the least impact on soil salinity estimation. Using significant level
analysis and correlation between the output of models and ground data, the best model with a value of
(R2 = 0.882) was selected and a soil salinity map was prepared based on it. In the study area, the
highest area belonged to non-saline class which comprises 75% of the total study area and about 1%
of the soils belong to the saline class.

Keywords

References

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