The next frontier in stormwater management and models: Multispectral and hyperspectral imaging of build-up and wash-off

Stormwater pollution poses risks to ecosystems and public health, yet traditional monitoring is costly, labour-intensive, and spatially limited. These constraints impede reliable catchment-scale data collection for water quality modelling and management. This study demonstrates a non-contact method using multispectral and hyperspectral imaging to monitor pollutant build-up and wash-off on urban impervious surfaces. Experiments under controlled (5-500 g applied road dust) and natural dry-wet conditions showed that multispectral imaging effectively quantified build-up and wash-off with strong sensitivity to particulates. Pollutant build-up showed a strong relationship with a pre-defined Near Infrared-Long Wavelength Infrared spectral index (controlled conditions, R² = 0.98; field conditions, R² = 0.7), and the wash-off spectral index followed the same linear trend. In contrast, hyperspectral imaging (272 bands, 400-900 nm) detected particle-bound pollutants such as Fe³⁺ via distinct reflectance-absorption features, confirmed using hematite reference spectra from the U.S. Geological Survey Spectral Library. When integrated with unmanned aerial vehicles (UAVs), this approach can replace recurrent physical sampling, reduce monitoring costs, and improve data reliability and coverage. It establishes a novel pathway for catchment-scale stormwater monitoring and modelling, enabling more efficient, data-driven urban water management.