Assessing WQI Using Spatial Land-Use Context Derived from Google Earth Imagery and Advanced Convolutional Neural Networks in South Korea

Assessing water quality indices (WQIs) derived from physicochemical measurements accurately and efficiently is essential for effective water resource management. However, conventional monitoring approaches based on single-point measurements and limited spatial coverage face constraints in representing large-scale river environments. To address these limitations, this study integrates high-resolution Google Earth RGB imagery with national water quality monitoring data from South Korea to construct an image-based dataset for WQI estimation. Water quality monitoring records from 1762 sampling sites collected between January 2000 and September 2020 were used to calculate WQI values. The index was computed using seven parameters—temperature, pH, dissolved oxygen, total solids, biochemical oxygen demand, nitrate, and phosphate—following the standard weighting procedure. Corresponding Google Earth RGB imagery acquired within ±1 day of field measurements over the same 2000–2020 period was compiled, resulting in 34108 image–sample pairs. Based on this integrated dataset, a ResNeXt-based convolutional neural network enhanced with convolutional block attention modules was implemented and applied to estimate WQI values from spatial land-use context and river morphology captured in RGB imagery. The proposed model demonstrated superior predictive performance compared to baseline neural network models, achieving a coefficient of determination (R 2 ) of 0.94 and an index of agreement (IOA) of 0.96. Grad-CAM analysis indicates that the model primarily utilizes spatial land-use patterns, riparian context, and river morphology rather than direct visual signals from the water surface itself. These findings suggest that RGB imagery contains spatial information related to observed WQI values. Accordingly, the framework provides a spatially continuous perspective on river conditions that may support large-scale monitoring efforts.