In the face of climatic change and anthropogenic pressure, innovative approaches are urgently needed to monitor and manage water resources across diverse environments. The integration of remote sensing and Artificial Intelligence (AI) offers transformative potential for water quality monitoring and modeling, with broad implications for ecosystem health, public safety, and economic resilience.

This session invites contributions exploring remote sensing platforms, machine learning and deep learning models for water quality analysis, data-driven decision support tools, and optimized sensor networks for real-time monitoring. We also welcome research on urban water quality challenges, which include but are not limited to, nutrient loading, pollutant transport, and runoff impacts on downstream rivers or lakes, and harmful algal bloom (HAB) detection, forecasting, and management. Interdisciplinary studies bridging hydrology, environmental engineering, public health, social sciences, and policy, as well as research addressing data gaps, underrepresented regions, and vulnerable communities, are strongly encouraged.

• B - Biogeosciences
• NH - Natural Hazards