Over the last two decades, water quality remote sensing has matured to facilitate operational monitoring of anthropogenic impact on inland waters. In doing so, optical measurements of reflected sunlight are acquired and interpreted. This upwelling signal consists of contributions from different depths within the illuminated layer. But vertical variations in water quality add significantly to the complexity of water-leaving signals, making their interpretation based on todays satellite observations an ill-posed task.
The OCI sensor on-board the PACE satellite will sample the colors of reflected sunlight in more detail than current satellites, which opens new opportunities for signal interpretation. The main goal of the project HyperStrata is to account for vertical variations in water quality via data acquired by OCI and improved retrieval methods. The second goal is to support these retrieval methods by continuously measuring and modeling the physical and biogeochemical processes that cause vertical gradients in water quality.
Our test site is Lake Geneva on the border of France and Switzerland, which is about 580 km2 in area and the largest lake in Western Europe. It stratifies every year between May and October. Visibility in Lake Geneva varies between 5 and 20 meters, and is often larger than the stratification depth. It therefore constitutes an appropriate target for investigating vertical variations as seen by optical satellite sensors like OCI.
In order to prepare a novel retrieval method in time for the launch of PACE in 2022, we rely on the combined use of three independent data sources available for Lake Geneva:
- OCI-analog data acquired by the airborne imaging spectrometer AVIRIS NG
- Automated in situ measurements taken on the LéXPLORE research platform
- Operational hydrodynamic simulations and forecasting on Meteolakes
In the scope of HyperStrata, we will link physical stratification obtained from hydrodynamic simulations to optical remote sensing signals, and identify sensitive wavelength domains that become accessible due to the improved spectral resolution of OCI. Different mathematical approaches will be evaluated to facilitate the inverse application of these principles, namely to obtain stratification properties from remotely sensed optical signals. The best performing approach will be implemented, validated and made accessible to other researchers.