UNDERSEE

UNDERSEE is rated 3 out of 5 in the category environmental services. Read and write reviews about UNDERSEE. UNDERSEE started in 2016 and launched under ESA BIC Portugal to develop a multi-sensor fusion system for water quality monitoring. To move from a reactive to a preventive decision-making approach regarding water quality monitoring and environmental impact assessments in rivers, lakes and oceans we need in-situ data, satellite data and numerical modelling. The lack of real measurements of water quality variables affects the reliability of numerical simulations. Some water quality parameters can be measured via satellite, but for predictions to be accurate, satellite measurements need to be validated by in-situ data. Our team is committed to develop a scalable solution to bring knowledge marine environments to water dependent stakeholders by providing simple access to water quality data. UNDERSEE developed a compact and easy to install in-situ water data acquisition system (UNDERSEE_water) and a user-friendly cloud platform (UNDERSEE_cloud) combined with EOD products and predictive models, which enables the spread of Ships of Opportunity and Precision Aquaculture projects in a large scale. This powerful decision-making tool is valuable to maintain water quality at high level, which has a great importance for different stakeholders that depends on the quality of marine environments. UNDERSEE solution allows users to monitor and predict water quality changes in rivers, lakes or oceans, without dealing with sensors maintenance. We provide an end-to-end service with an integrated offering of hardware, software, installation, maintenance and customer support, based on a Connected Product-as-a- Service model where customers subscribe for the usage of the product (data-based fee) and automated decision-making tools. The service is composed by the following value layers and correspondent technologies: “Real-time water quality data (In-situ acquisition system)”, “Managing tools (Data Fusion & Web Services)” and “Early warnings (Predictive models)”.