Unlocking the potential of remote sensing for arsenic contamination detection and management: Challenges and perspectives

This work explores the current status of remote sensing (RS) applications for managing global arsenic (As) pollution in water, impacting health and ecosystems. We detailed the complex, indirect relationship between remote sensing and arsenic contamination detection. Satellite imagery from Landsat, Sentinel, and Hyperion satellites are notably effective in identifying As minerals, providing a proxy for groundwater As pollution. These methods can be further enhanced by integrating GRACE satellite data on groundwater fluctuations, land use maps, and machine learning. Despite these advances in the RS technologies, challenges of data accuracy, interpretations, and ground-truthing are likely to persist. This work also adds to the narrative and the perspective of AI applications in environmental data improvement, diagnostics and prognostics for groundwater, and that further understanding of environmental complexity is needed to boost innovation in mitigating and democratizing As-related challenges. • Hyperspectral imaging, conjointly novel algorithms, can help arsenic detection. • RS imagery integrated with AI/ML & aquifer attributes can predict hydrogeochemistry. • Ground truthing remains a challenge for remote sensing (RS) applications in water quality. • The full potential of RS is still to be maximised for geogenic contaminant tracing.