Real time monitoring of inland water quality

Real time monitoring of inland water quality


Near-real time space based solutions for monitoring inland water quality are aiding government agencies in taking effective decisions

Water quality is affected by increasing pressures from land use change, agri- and aquaculture, and water-related recreational activities. Monitoring the temporal and spatial dynamics of inland water quality is essential for the improved understanding of aquatic ecosystems. However, this can be a challenging task due to the highly variable nature of water quality environments as well as the large spatial scales involved.



Satellite-based earth observation offers an efficient and cost-effective mechanism to rapidly assess a variety of physical and biological parameters in aquatic ecosystems over large areas.

Water bodies have specific reflectance characteristics (spectra) based on the scattering and absorbing properties of the optically active constituencies in the water. Optically active water constituents include suspended matter and phytoplankton, as well as particulate and dissolved coloured organic matter. With sufficient knowledge of these characteristics it is possible to use the data measured by the satellite sensors in order to quantitatively estimate the concentrations of the water constituencies.

A unique algorith algorithm workflow, MIP (Modular Inversion Program), has been developed, which includes all relevant processing steps to guarantee a reliable, standardised and automatic processing of water quality parameters from satellite data.

Small lakes water quality monitoring

Regular monitoring of the ecological status of freshwaters larger than 1 ha is obligatory for European Member states, as per the Water Frame Directive (WFD) of the European Commission. Europe contains over 500,000 natural lakes, larger than 1 ha, as well as large number of rivers. Authorities are not capable of monitoring these water bodies with adequate temporal resolution. To solve this, EOMAP provided the Environmental Agency of the state of Baden-Wuerttemberg in Germany with regular monitoring of all lakes greater than 1ha using satellite derived water quality products. To establish this service and, more importantly, to deploy satellite-based high-resolution water quality monitoring on a transnational level, EOMAP is currently leading a project funded by the EU, called FRESHMON (High resolution freshwater monitoring).

Due to different spatial and spectral resolutions, not all satellites are capable of measuring the same set of parameters. However, with suitable processing technologies, the various data sources deliver standardised and objective in-water measures of spectral scattering and absorption of water constituents. For example, sensors with a spatial resolution of more than 100m are currently not capable of deriving chlorophyll independent from the load of dissolved organic materials. However, a range of maximum chlorophyll concentrations can be retrieved on combining the dependencies of phytoplankton to scattering of particles, and absorption to pigment-absorption of phytoplankton.


Established through the collaboration with German authorities, the maximum chlorophyll indicator gives a standardised, large-scale overview of the ecological status of relatively small lakes.

In combination with the knowledge of the individual characteristics of the water bodies, the state authorities are able to insert the new measurements into their water quality monitoring sampling routine, thereby improving both the monitoring time interval and the area covered. Exceedance of critical concentration limits can be reported in near real time.


Mekong river water quality monitoring

The Mekong River system has a wide range of stakeholders and the delta is one of the most productive food regions in the world. The entire Mekong is under significant pressure from various sources, e.g. an increasing number of upstream dams are causing a significant reduction of the downstream nutrient-rich sedimentation. There is, therefore, an ongoing need to monitor the environmental status of the entire Mekong river system.

To this end, two significant environmental variables that can be routinely measured are turbidity and related total suspended matter concentrations in the river. Directly linked to water quality, these variables provide quantitative information on sediment and related nutrient transports, and allow for the monitoring of the effects of environmental and physical changes along the river system.

Satellite-derived and quantitatively measured turbidity, with related TSM and Organic Absorption estimates can overcome both the challenge of consistently acquiring standardised, comparable measurements through space and time, as well as the limitations of access to historical data.

Within the framework of the WISDOM project (Water Information System for the Mekong), a processing infrastructure for the automatic and standardised processing of water quality parameters was established. Water quality maps for more than 5,000 satellite scenes of the Mekong delta were delivered. This equates to more than 900 million sq km in total surface area mapped. Based on sensor-independent processing chain, different satellite data with different spatial resolutions could be seamlessly integrated. This capability enables the mapping of both small and large rivers, as well as extended maritime areas.

The results are of significant interest, illustrating dramatic changes in the nutrient and suspended matter status of the Mekong and other rivers within the last two decades.


Earth observation-derived water quality parameters are a uniquely powerful way to have a window into past water quality environments. Alternative sources for spatial and sufficiently standardised in-situ measurements are typically scarce or non-existent. Furthermore, EO allows for extended areas of lakes, rivers and oceans to be contiguously monitored on a regular basis. Finally, it allows for a standardised approach which guarantees comparable measurements across political boundaries.