Author(s)

Abderrazak Bannari^1*, Ali El-Battay¹, Nadir Hameid¹, Fadia Tashtoush²

¹Department of Geoinformatics, College of Graduate Studies, Arabian Gulf University, Manama, Kingdom of Bahrain.
²Department of Natural Resources and Environment, Arabian Gulf University, Manama, Kingdom of Bahrain.

The aim of this research is to map the salt-affected soil in an arid environment using an advanced semi-empirical predictive model, Operational Land Imager (OLI) data, a digital elevation model (DEM), field soil sampling, and laboratory and statistical analyses. To achieve our objectives, the OLI data were atmospherically corrected, radiometric sensor drift was calibrated, and distortions of topography and geometry were corrected using a DEM. Then, the soil salinity map was derived using a semi-empirical predictive model based on the Soil Salinity and Sodicity Index-2 (SSSI-2). The vegetation cover map was extracted from the Transformed Difference Vegetation Index (TDVI). In addition, accurate DEM of 5-m pixels was used to derive topographic attributes (elevation and slope). Visual comparisons and statistical validation of the semi-empirical model using ground truth were undertaken in order to test its capability in an arid environment for moderate and strong salinity mapping. To accomplish this step, fieldwork was organized and 120 soil samples were collected with various degrees of salinity, including non-saline soil samples. Each one was automatically labeled using a digital camera and an accurate global positioning system (GPS) survey (σ ≤ ± 30 cm) connected in real time to the geographic information system (GIS) database. Subsequently, in the laboratory, the major exchangeable cations (Ca²⁺, Mg²⁺, Na⁺, K⁺, Cl^- and SO₄^2-), pH and the electrical conductivity (EC_-Lab) were extracted from a saturated soil paste, as well as the sodium adsorption ratio (SAR) being calculated. The EC_-Lab, which is generally accepted as the most effective method for soil salinity quantification was used for statistical analysis and validation purposes. The obtained results demonstrated a very good conformity between the derived soil salinity map from OLI data and the ground truth, highlighting six major salinity classes: Extreme, very high, high, moderate, low and non-saline. The laboratory chemical analyses corroborate these results. Furthermore, the semi-empirical predictive model provides good global results in comparison to the ground truth and laboratory analysis (EC_-Lab), with correlation coefficient (R²) of 0.97, an index of agreement (D) of 0.84 (p < 0.05), and low overall root mean square error (RMSE) of 11%. Moreover, we found that topographic attributes have a substantial impact on the spatial distribution of salinity. The areas at a relatively high altitude and with hard bedrock are less susceptible to salinity, while areas at a low altitude and slope (≤2%) composed of Quaternary soil are prone to it. In these low areas, the water table is very close to the surface (≤1 m), and the absence of an adequate drainage network contributes significantly to waterlogging. Consequently, the intrusion and emergence of seawater at the surface, coupled with high temperature and high evaporation rates, contribute extensively to the soil salinity in the study area.

Soil Salinity, Remote Sensing, Landsat-OLI, GIS, Semi-Empirical Model, Salinity Spectral Indices, Topographic Attributes, Arid Environment

Bannari, A. , El-Battay, A. , Hameid, N. and Tashtoush, F. (2017) Salt-Affected Soil Mapping in an Arid Environment Using Semi-Empirical Model and Landsat-OLI Data. Advances in Remote Sensing, 6, 260-291. doi: 10.4236/ars.2017.64019.