Soil salinity assessment of a natural pasture using remote sensing techniques in central Anatolia, Turkey

• Published in: PloS one 2022-04, Vol.17 (4), p.e0266915
• Main Authors: Kılıc, Orhan Mete, Budak, Mesut, Gunal, Elif, Acır, Nurullah, Halbac-Cotoara-Zamfir, Rares, Alfarraj, Saleh, Ansari, Mohammad Javed
• Format: Article
• Language: English
• Subjects: Arid regions; Arid zones; Biological activity; Complexity; Correlation; Datasets; Earth Sciences; Ecology and Environmental Sciences; Electrical conductivity; Electrical resistivity; Engineering and Technology; Environmental Monitoring - methods; Evaluation; Global positioning systems; GPS; Halophytes; Laboratories; Land degradation; Land use; Landsat; Landsat 5; Landsat satellites; Management; Methods; Nutrition; Organic farming; Pasture; Physical Sciences; Regression analysis; Remote sensing; Remote sensing techniques; Remote Sensing Technology; Reproducibility of Results; Research and Analysis Methods; Salinity; Salinity effects; Salt; Salt content; Salts; Satellite imagery; Satellites; Semi arid areas; Semiarid lands; Semiarid zones; Sensing techniques; Short wave radiation; Sodic soils; Soil; Soil salinity; Soil sciences; Soil surfaces; Soils, Salts in; Spatial discrimination; Spatial distribution; Spatial resolution; Surface water; Turkey; Vegetation
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0266915

Soil salinity is a major land degradation process reducing biological productivity in arid and semi-arid regions. Therefore, its effective monitoring and management is inevitable. Recent developments in remote sensing technology have made it possible to accurately identify and effectively monitor soil salinity. Hence, this study determined salinity levels of surface soils in 2650 ha agricultural and natural pastureland located in an arid region of central Anatolia, Turkey. The relationship between electrical conductivity (EC) values of 145 soil samples and the dataset created using Landsat 5 TM satellite image was investigated. Remote sensing dataset for 23 variables, including visible, near infrared (NIR) and short-wave infrared (SWIR) spectral ranges, salinity, and vegetation indices were created. The highest correlation between EC values and remote sensing dataset was obtained in SWIR1 band (r = -0.43). Linear regression analysis was used to reveal the relationship between six bands and indices selected from the variables with the highest correlations. Coefficient of determination (R2 = 0.19) results indicated that models obtained using satellite image did not provide reliable results in determining soil salinity. Microtopography is the major factor affecting spatial distribution of soil salinity and caused heterogeneous distribution of salts on surface soils. Differences in salt content of soils caused heterogeneous distribution of halophytes and led to spectral complexity. The dark colored slickpots in small-scale depressions are common features of sodic soils, which are responsible for spectral complexity. In addition, low spatial resolution of Landsat 5 TM images is another reason decreasing the reliability of models in determining soil salinity.