A study from Chinese researchers demonstrates how combining satellite imagery, land use data, and machine learning can improve pollution monitoring in fast-changing urban rivers. The study focuses on non-optically active pollutants in the Weihe River Basin and showcases promising results for remote, data-driven water quality assessments.
In a significant advance for environmental monitoring, a study published in the Journal of Hydrology, focuses on China’s Weihe River Basin and explores a more effective way to monitor critical water quality parameters like ammonia nitrogen, total phosphorus, chemical oxygen demand, and dissolved oxygen.
Urban rivers, especially those like the Weihe River and its tributaries, face rapid degradation due to domestic, agricultural, and industrial pollution. Traditional methods of sampling and lab-based water analysis are resource-intensive and spatially limited. Moreover, the dynamic nature of rivers, with variable flow and narrow morphology, complicates the use of standard low-resolution remote sensing tools typically designed for broader water bodies like lakes.
“While remote sensing has been successfully used in lakes, the complexities of river systems—fast flows, narrow widths, and human impacts—require a more comprehensive monitoring strategy,” the authors explained.
What sets this study apart is its integration of multi-source data: field sample data, meteorological elements such as temperature, precipitation, evapotranspiration, and wind, as well as detailed land use classifications.
Land use proved to be a crucial variable. Urban and agricultural areas were strongly associated with poorer water quality, while forested regions generally correlated with better conditions. The machine learning (ML) models incorporated these land characteristics to predict pollutant levels more accurately.
Source: Spectroscopy, April 30, 2025. https://www.spectroscopyonline.com/…/ai-and-satellite…
China Environment News 