Hamidreza Matinfar; foziyeh kohani; Ali Akbar Asilian mahabadi
Abstract
Soil salinity is one of the most important environmental problems, and the identification and zoningof saline soils is difficult due to the need for sampling and laboratory analysis, as well as havingtemporal and spatial variability. In recent years, the use of satellite imagery has always been ofinterest ...
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Soil salinity is one of the most important environmental problems, and the identification and zoningof saline soils is difficult due to the need for sampling and laboratory analysis, as well as havingtemporal and spatial variability. In recent years, the use of satellite imagery has always been ofinterest to experts due to its ease of use and ability to detect phenomena. Remote sensing informationgreatly 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 tothe type of study and physiographic types and soil units. Then, pH and EC values were measuredusing standard methods. Soil salinity values were evaluated using correlations between EC electricalconductivity values obtained from ground data and variables obtained from Landsat 8 satellite imagesincluding bands, salinity indices, vegetation indices and soil indices. Finally, the soil surface salinityestimation model was obtained using stepwise regression method. This method involves the automaticselection of independent variables, and with the availability of statistical software packages, it ispossible to do so even in models with hundreds of variables. In previous studies, indicators and bandshave been used separately and in a limited way, but in this study, an attempt has been made to use acombination of different indicators more widely, and finally to achieve the best relationship byeliminating the indicators that have the least impact on soil salinity estimation. Using significant levelanalysis 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, thehighest 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.
