Surface water resources played a fundamental role in sustainable development of agriculture and aquaculture. In this study, the approach of Artificial Neuron Network was used to estimate and detect spatial changes of the Chemical Oxygen Demand (COD) concentration on optical remote sensing imagery (Landsat 8). Monitoring surface water quality was one of the essential missions especially in the context of increasing freshwater demands and loads of wastewater fluxes. Recently, remote sensing technology has been widely applied in monitoring and mapping water quality at a regional scale, replacing traditional field-based approaches. The study used the Landsat 8 (OLI) imagery as a main data source for estimating the COD concentration in river reaches of the Binh Dai district, Ben Tre province, a downstream river network of the Vietnamese Mekong Delta. The results indicated the significant correlation (R=0.89) between the spectral reflectance values of Landsat 8 and the COD concentration by applying the Artificial Neuron Network approach. In short, the spatial distribution of the COD concentration was found slightly exceeded the national standard for irrigation according to the B1 column of QCVN 08:2015.