Integration of Spatio-Temporal Satellite Data, Machine Learning, and Water Quality Indices for Depicting Precise Water Quality Levels

Essam Sharaf El Din, Ahmed Shaker

Monitoring surface water quality over large river systems remains challenging due to sparse in situ sampling and the need for decision-ready indicators. This study aims to address this problem by developing and evaluating an integrated Landsat 8-based backpropagation neural network and Canadian Council of Ministers of the Environment Water Quality Index (L8-BPNN-CCME-WQI) for precise surface water quality assessment over the Saint John River (SJR), New Brunswick, Canada. The proposed approach combines atmospherically corrected Landsat 8 imagery, BPNN for estimating multiple surface water quality parameters (SWQPs), and CCME-WQI to translate SWQP fields into transparent water quality levels. The L8-BPNN-CCME-WQI models were trained using in situ measurements of turbidity, total suspended solids (TSS), total solids (TS), total dissolved solids (TDS), chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), pH, electrical conductivity (EC), and temperature collected during our five field campaigns (from June 2015 to August 2016) and surface reflectance from five Landsat 8 scenes. The developed models achieved high performance during internal calibration and testing (R2 ≥ 0.80 for all SWQPs) and demonstrated robust performance (R2 ≈ 0.75–0.88) when applied to two independent surface water quality datasets from additional rivers across New Brunswick. Pixel-wise SWQP predictions were then input to the CCME-WQI formulation to derive reach-scale water quality levels, revealing that the lower Saint John River basin (below the Mactaquac Dam) is generally classified as “Fair” (CCME-WQI ≈ 67), whereas the middle basin upstream (above the Mactaquac Dam) is “Marginal” (CCME-WQI ≈ 59), reflecting stronger industrial and agricultural pressures. Overall, the L8-BPNN-CCME-WQI framework provides a scalable methodology for converting multi-parameter satellite-derived water quality information into spatially exhaustive CCME-WQI classes, supporting targeted regulation, prioritization of mitigation in critical reaches, and evaluation of management actions in large river systems.