I spy with my hyperspectral eye: unique reflectance database of plastics and riverbank-harvested litter

Airborne and spaceborne remote sensing (RS) collecting hyperspectral imagery provides a promising way forward for detection and monitoring of riverine and marine plastic pollution. However, a major challenge in the application of RS techniques is the lack of fundamental understanding of spectral signatures of floating plastic debris at multiple scales. Recent work emphasised the case for open-access hyperspectral reflectance reference libraries of commonly used polymer items. In this paper, we present a high-resolution hyperspectral image database of a unique mix of (i) 40 virgin macroplastic items, (ii) organic material of plant leaves, tree leaves and riparian vegetation, and (iii) 50 items of riverbank-harvested macrolitter including plastics and other anthropogenic debris. We used a double camera setup that covered the VIS-SWIR range from 400-1700 nm in a dark room experiment with controlled illumination. The cameras scanned the samples floating in water and captured high-resolution images in 336 spectral bands. From these images we identified diagnostic absorption features for different materials, item categories, and plastic polymers. The identification was done by applying a linear discriminant analysis to the spectra, allowing the creation of combined band indices distinguishing between the different item types and polymer categories. We present reflectance spectra of all items in our image dataset, complemented by easy-to-interpret visual representations of derived indices. We demonstrate the importance of high-resolution reference reflectance libraries, to (i) further optimise existing remote sensing monitoring techniques, and (ii) contribute towards the development of future plastic monitoring and classification missions.