Estimation of Total Dissolved Solids in Water Bodies by Spectral Indices Case Study: Shatt al-Arab River

• Published in: Water, air, and soil pollution air, and soil pollution, 2020-09, Vol.231 (9), Article 482
• Main Authors: Maliki, Ali A. A., Chabuk, Ali, Sultan, Maitham A., Hashim, Bassim M., Hussain, Hussain M., Al-Ansari, Nadhir
• Format: Article
• Language: English
• Subjects: Agricultural management; Aquatic resources; Atmospheric correction; Atmospheric models; Atmospheric Protection/Air Quality Control/Air Pollution; Case studies; City centres; Climate Change/Climate Change Impacts; Correlation coefficient; Correlation coefficients; Decision making; Earth and Environmental Science; Earth resources technology satellites; Environment; Environmental monitoring; Geographic information systems; Geographical information systems; Geoteknik; GIS; Hydrogeology; Landsat; Landsat satellites; Landsat-8 OLI; Management; Reflectance; Regression analysis; Remote sensing; Resource management; Rivers; Salinity; Salinity effects; Salinity indices; Satellite imagery; Shatt al-Arab; Soil Mechanics; Soil Science & Conservation; Spaceborne remote sensing; Spectra; Spectral index; Spectral reflectance; Stations; Total dissolved solids; Water; Water quality; Water Quality/Water Pollution; Water resources
• ISSN: 0049-6979; 1573-2932; 1573-2932
• DOI: 10.1007/s11270-020-04844-z

In recent years, the problem of rising salinity levels in the Shatt al-Arab river in southern Iraq has been repeated, which has directly affected the living and health situation and the agricultural activity of these areas. Six sampling stations were selected along Shatt al-Arab to estimate the concentration of total dissolved solids (TDS) in the river; these stations included the following: Qurna, Labani, City Centre, Kateban, Corniche, and Sihan. In addition, three Landsat-8 satellite images which were taken at the same time as collected samples also used for detecting the salinity in the river. After processing of atmospheric correction and inserted remote sensing indices, the reflectance of water extracted from satellite images was used to express the spectral characteristics of different TDS concentrations. Correlation and regression were used to obtain accurate models for detecting the salinity depending on the spectral reflectance of Landsat 8 operational land image OLI. The results presented Pearson correlation ( r ) value of 0.70, 0.97, and 0.71, and correlation coefficient ( R 2 ) of 0.56, 0.94, and 0.85 between field data with spectral data of salinity index 2 (SI-2) derived from the green and blue bands of Landsat obtained in 2015, 2017, and 2018 respectively. In conclusion, remote sensing and GIS technologies coupled with spectral modeling are useful tools for providing a solution of future water resources planning and management, and also offer great undertaking as a means to improve knowledge of water quality and support water decision making.