Funded by EU H2020, MONOCLE brings together 12 partners from across Europe to create sustainable in situ observation solutions for Earth Observation (EO) of optical water quality in inland and transitional waters.
The overarching aim of MONOCLE is to developing essential research and technology to lower the cost of acquisition, maintenance, and regular deployment of in situ sensors. This is done via two routes: increased autonomy of sensors, particularly in remote locations, and new sensors to support citizen science in the fields of optics (physics) and biogeochemistry.
The MONOCLE ecosystem establishes stronger links between operational Earth Observation (EO) and essential environmental monitoring in inland and transitional water bodies by developing efficient and standardized data flows and near-real time data processing.
We focus on inland and nearshore aquatic ecosystems, which are particularly vulnerable to direct human impacts, and which represent areas of the weakest performance in current satellite observation capability despite the major technological advances in recent decades. At the same time, these areas are of great economic importance and are crucial to sustainable food, energy, and clean water supply.
MONOCLE partners are developing low-cost optical sensors, methods and technologies to support water quality monitoring by regional and national agencies. In addition to our research programme we are exploring the role local communities and volunteers (led by MONOCLE partner Earthwatch Europe) can play in collecting essential environmental data to complement existing monitoring networks, evaluate the performance of in-situ sensors, and the role citizens can play in the maintenance and deployment of sensors.
Citizen science observatories are used to evaluate the role citizens can play in an integrated observation platform of in situ autonomous and citizen operated sensors and earth observation services to monitor the water quality parameters, for large rivers, lakes, reservoirs, estuaries, bays and other coastal zones around the world including, in particular, data poor regions.
Volunteers have been active in testing and demonstration activities in the Mälardalen in Sweden, Lake Tanganyika in Tanzania, the Pyrenees lakes in Spain, the Danube Delta in Romania, and in water reservoirs around Sao Paolo in Brazil.
The following scores were calculated using a statistically-driven machine-learning approach, a type of AI that learns to perform a task by analysing patterns in data. This is an experimental approach to citizen-science impact assessment, and the exact reasoning behind the scores is not explainable. The scores represent a best guess of the impact the project is having in each domain. Scores are recalculated and updated when “View impact report” is clicked.
Proportion of questions answered in each domain.