Abstract
The Ismailia Canal is one of the most important tributaries of the River Nile in Egypt. It is threatened by extinction from several sources of pollution, in addition to the intersection and nearness of the canal path with the Bilbayes drain and the effluent from the two largest conventional wastewater treatment plants in Greater Cairo. In this study, the integration of remote sensing and geospatial information system techniques is carried out to enhance the contribution of satellite data in water quality management in the Ismailia Canal. A Landsat-8 operational land imager image dated 2018 was used to detect the land use and land cover changes in the area of study, in addition to retrieving various spectral band ratios. Statistical correlations were applied among the extracted band ratios and the measured in situ water quality parameters. The most appropriate spectral band ratios were extracted from the NIR band (near infrared/blue), which showed a significant correlation with eight water quality metrics (CO3, BOD5, COD, TSS, TDS, Cl, NH4, and fecal coliform bacteria). A linear regression model was then established to predict information about these important water quality parameters along Ismailia Canal. The developed models, using linear regression equations for this study, give a set of powerful decision support frameworks with statistical tools to provide comprehensive, integrated views of surface water quality information under similar circumstances.