Deciphering Multi-Scale Anthropogenic Drivers of River Water Quality: A Synergistic ML-GAM Cascade Framework with Sentinel-2

While understanding the drivers of river water quality is crucial, the dependence on ground observations hinders the accurate quantification of driver thresholds, as well as the scale-dependent effects of buffer zones. By transcending the limitations of ground observations, satellite remote sensing provides the spatially continuous data required to define effective buffer zones and determine the threshold intervals for natural and anthropogenic drivers, effectively promoting sustainable watershed management. Herein, we determined the total nitrogen (TN), total phosphorus (TP), permanganate index (CODMn), and turbidity in the Minjiang River of Fujian Province by synergizing Sentinel-2 imagery and in situ data (2021–2024). Subsequently, we further employed generalized additive models (GAMs) considering scale-dependent (50 m to 20 km) characteristics to screen and evaluate the natural–anthropogenic factors influencing the water quality indicators. The GAMs revealed that TN exhibited multiphasic responses to forest cover and water area, characterized by alternating positive and negative effects across their range. TP was found to be predominantly driven by agricultural and urban land use, showing clear scale–threshold effects. This study provides an integrated framework that moves beyond retrieval to quantitatively assess the impact of multi-scale natural–anthropogenic factors, offering actionable insights for precise watershed zoning and science-based management for the sustainable development of river systems.