Soil salinity is another significant environmental threat to sustainable food production in the world. Studies indicate that if necessary measures were not taken, soil salinity's direct economic impacts would exceed $1 billion annually by 2030, only in the Central Valley, California. Saline soil is generally defined as a soil that its electrical conductivity (EC) of the saturated soil-paste extract in the root zone is more than 4 dS m−1 at 25 °C and contain 15% of exchangeable sodium  . Excess salt concentration in soil-paste reduces its osmotic potential, which in turn decreases water uptake of the plant by increasing the energy cost of water extraction, transpiration, and photosynthetic rate, and finally decreasing production . The main factors affecting the soil salinity include water quality and irrigation management, the soil's geological nature, excessive fertilization, drainage conditions, rainfall, and ET. As a result, a comprehensive management of the field is required to overcome salinity problem in susceptible aeras. Among the influential factors on soil salinity, some of them, such as soil's geological nature and drainage condition, can be estimated for a long time by one-time assessment, while the other factors, such as irrigation practices, fertilization, and ET estimation, need ongoing evaluation due to their rapidly changing nature.
Soil salinity can be estimated in different ways. The traditional method of assessing soil salinity by soil sampling is the most direct way which is expensive and ineffective for large-scale mapping. The remote sensing methods that are more efficient can be divided into two different categories; direct salinity assessment (using electromagnetic induction and soil’s electrical conductivity) , and indirect estimation (form the symptoms of the plants)  . A crop suffering from soil salinity (in the root zone) might have a different reflectance and ET characteristic than a normal plant. Usually, an increase in the visible range and a decrease in the near-infrared (NIR) region of the electromagnetic spectrum is expected. Vegetation indices such as NDVI and Canopy Response Salinity Index (CRSI) can be used for salinity quantification . However, a similar change in spectral response might happen due to variety of reasons such as nematode, drought, and other stressors. As a result, it might be impractical to link the spectral features to a particular cause without additional knowledge about the field and plants condition. A data fusion and analysis method that takes as many variables as possible and concludes based on the combined inputs would be inevitable in such studies.
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