Water Quality Measurements from Hyperspectral Remote Sensing: The Case of the River Ganga

Abstract:

Water pollution is a major challenge in large river systems such as the Ganga (i.e. Ganges). With a population of 400 million, widespread agriculture and a heavy industrial base, this river basin is facing multiple stressors and as a result, is now notorious for poor water quality. One of the key issues in addressing this problem remains basic water quality monitoring with systematic and reliable methods. Currently, water quality datasets in the River Ganga are highly fragmented and inadequate for most investigations. Given the sub-continental scale of the system, remote sensing could offer a plausible solution if capable of producing holistic assessments of water quality with a standardised methodology. Specifically, the development of hyperspectral remote sensing, capable of detecting very small changes in incident radiation, offers the potential to mimic laboratory spectroscopy and thus identify the chemicals polluting a body of water, and perhaps, even measure their concentration. However, the use of hyperspectral remote sensing in order to measure water quality is not yet established and remains a very challenging problem.

In this study, laboratory experiments, ancillary field data and hyperspectral imagery from the Hyperion sensor were used to explore the feasibility of using remote sensing to detect chromium pollution in the River Ganga. The laboratory experiments demonstrated that field spectroscopy was indeed capable of detecting chromium in concentrations that can currently be found in the Ganga. Furthermore, the analysis of the Hyperion images of the River Ganga shows some promising results which suggest that chromium compounds can be detected using hyperspectral satellite imagery. However, the results confirm that measuring water quality from spaceborne hyperspectral imagery is extremely challenging and further research is required to improve the confidence of these results and refine this methodology.