Emerging contaminants like PFAS, nanoparticles and microplastics threaten ecosystems, public health, and water security. Their transport through water, air, and soil is influenced by ecological processes, hydroclimatic conditions, land use changes, and extreme events. Understanding their spatial and temporal dynamics is crucial for identifying exposure hotspots, assessing risks, and designing mitigation strategies. However, Global-scale contaminants monitoring is logistically challenging due to high cost and spatial constraints. Thus, remote sensing, combined with in-situ observations and computational approaches, can track sources, transport pathways, and impacts of contaminants, supporting environmental surveillance and science-based decision-making. This session seeks research that advances remote sensing–informed risk mapping, characterizes contaminant dynamics following extreme events, transforms contaminant management, develops satellite-based proxy indicators for these contaminants, and/or integrates remote sensing with in-situ monitoring networks. We welcome research that presents novel modeling frameworks, including machine learning, deep learning, and Bayesian spatial methods, in tandem with hydrological and atmospheric transport models.