Estimating chlorophyll-a concentrations in rivers through analysis of hyperspectral PRISMA imagery

Abstract

It is important to monitor the water quality of rivers, as people are dependent on this source of fresh water, and all other services rivers provide. Chlorophyll-a (chl-a) concentration is an important indicator for a proper quality of water and eutrophication. Hyperspectral satellite imagery can be used to estimate chl-a concentration in surface water, but methods for this have mostly been applied to large water bodies, like lakes, estuaries, and oceans. The aim of this research was to investigate how medium spatial resolution, hyperspectral PRISMA imagery can be used for estimating and monitoring water quality of rivers and at what accuracy. For this, two existing chl-a concentration algorithms, a band-ratio algorithm (Gurlin et al., 2011) and a normal difference chlorophyll index (NDCI) algorithm (Mishra & Mishra, 2012), both using PRISMA bands 34 (665nm) and 38 (709nm), were used. These algorithms were validated using a local chl-a concentration and reflectance dataset (n=11) of the Danube-Sava confluence. These algorithms were also recalibrated and validated using this same dataset to investigate if the results improve. The original Mishra and Mishra NDCI algorithm had the overall best performance, with a NMAE of 0.07 and a NRMSE of 0.09. It was possible to map spatial patterns of chl-a in rivers with qualitative concentration estimates and to refine that into quantitative estimates with a certain uncertainty range. The performance of both algorithms did not improve after recalibration. Large-scale sources of chl-a in rivers, like larger tributaries, could be deduced from the chl-a distribution maps, showing that the Danube has a higher chl-concentration than the Sava before the confluence. The (limited) mixing of water at the confluence and the distribution of chl-a after the confluence could be observed and interpreted. Small scale sources of chl-a, like wastewater outlets, could not be deduced in this research as the spatial resolution of the chl-a distribution maps was still too high compared the small and localized chl-a influx of these sources. With further validation of the Mishra and Mishra NDCI algorithm using a more extensive dataset, these methods could be developed into an automated monitoring system, as medium resolution hyperspectral imagery is suitable for estimating and monitoring the water quality of rivers.