Geospatial assessment of water quality in the Ganga River: Leveraging Landsat-8 and GIS

The unchecked exploitation of water resources has resulted in significant pollution in major rivers, driven by anthropogenic activities and industrial effluents. Within the scope of this study, the capability of remote sensing (RS) and geographic information system (GIS) was explored for the examination of the condition of surface water quality (WQ) in the Kanpur stretch of Ganga. Traditional methods, which are labour-intensive, time-consuming, and costly, were addressed by using Landsat-8 satellite imagery to extract six key WQ parameters: dissolved oxygen (DO), pH, total coliform (TC), electrical conductivity (EC), temperature, and biochemical oxygen demand (BOD). Multiple linear regression (MLR) models were formulated to relate satellite band information to these WQ parameters. Ground truth data from monitoring stations were collected between 2019 and 2021. High correlation coefficients (R2 values amidst 0.72 and 0.92) were observed, demonstrating strong relationships between satellite-derived data and actual water quality measurements. The Water Quality Index (WQI) derived from satellite data captured 80% of the variability in observed values, highlighting the model's accuracy. This study underscores the effectiveness of RS and GIS in real-time, large-scale water quality monitoring, offering a cost-effective and spatially comprehensive solution for guiding water management strategies in polluted river systems.