Multi-scale Monitoring of Water Quality in a Phytoplankton Carrying, European River - a case study of the Moselle

River management, e.g. in the context of the European Water Framework Directive, requires a comprehensive monitoring of inland water bodies. Traditional water quality assessment methods using probes and laboratory analyses are time-consuming, expensive and insufficiently capture the large-scale dynamic nature of river systems. Remote sensing offers a promising additional data source, though limited river widths are challenging to resolve and constrain sensor selection and often necessitates integrating multiple data sources of variable resolution. In addition, using sensors with higher resolution enables the investigation of small-scale effects.

The MeskalMon-Project (Multi-scale monitoring in rivers using remote sensing and in-situ methods for the parameters chlorophyll and suspended matter) develops an innovative approach that combines in-situ measurements with remote sensing data from various platforms, including a hyperspectral sensor mounted to bridges, a spectrometer, a multispectral UAS-sensor and multispectral satellite imagery. The research focusses on the characterization of the spatial variability and spectral interactions of chlorophyll-a and turbidity through a comprehensive monitoring strategy.

Measurement campaigns on the river Moselle during 2022-2024 employed diverse sampling techniques, including longitudinal, lateral, and vertical measurements in the water column. By applying indices such as the Normalized Difference Chlorophyll Index (NDCI), we have facilitated comparability between different spatial resolutions, data acquisition methods and platforms. Preliminary results show a promising agreement between satellite, camera, spectrometer and in-situ measurement methods.

Our findings indicate that water, sediments and nutrients in the river Moselle are well mixed, which makes surface data from remote sensing representative despite its limited penetration depth. However, the variable composition of different algae groups in the water and surface scum formation in the case of intensive cyanobacterial blooms, pose major challenges in the interpretation of remote sensing data to derive the concentration of suspended sediment and chlorophyll-a (Chl-a) in the water column.

Analysis of multispectral satellite data (here Sentinel-2) shows good results for Chl-a and turbidity quantification. We achieve high determination coefficients of up to 0.79, using different atmospheric corrections in combination with various algorithms for deriving Chl-a from satellite data using in-situ measurements. Limitations arise if algae groups vary or high Chl-a concentrations are accompanied by high turbidity. The analyses demonstrated the intricate optical interactions within aquatic environments, highlighting the challenges of accurately distinguishing and measuring water quality indicators through remote sensing techniques, showing advantages of hyperspectral methods.

Our research revealed significant variations in the performance of Chl-a algorithms and indices depending on the mix of algal groups present in the water. The current spectral bands available on the used UAS-sensor and the satellites proved insufficient for differentiating algal groups. However, the upcoming CHIME mission provides new opportunities for a more detailed analysis of aquatic ecosystems in the necessary spatial, spectral and temporal resolution and demonstrates the potential of advanced remote sensing technologies.

This research provides a novel, integrated framework for remote sensing based water quality monitoring that overcomes some limitations of traditional monitoring methods. It represents a significant step towards more dynamic, comprehensive, and efficient environmental monitoring strategies, with future research poised to leverage emerging satellite technologies for more nuanced ecological insights.