Environmental monitoring in the Earth observation (EO) age requires the assimilation of heterogeneous data from in-situ measurements, model simulations and satellite remote sensing to describe the state of the environment accurately. Here we present two open-source projects that facilitate near-real-time processing, visualisation and access to data on the condition of Swiss lakes. SenCast (https://gitlab.com/eawag-rs/sencast) is a Python toolbox for downloading Sentinel-2 and Sentinel-3 images and computing water quality parameters, and Datalakes (https://www.datalakes-eawag.ch/), is an open data platform for accessing, visualising and comparing heterogeneous environmental data. Together they form an operational pipeline that facilitates easy access to EO data and allows the joint interpretation of spatial patterns from satellite observations, 3D hydrodynamic simulations and in-situ measurements from vertical profilers and moorings. This proof of concept at a national scale shows how countries can produce accurate water quality information in line with the SDG indicator 6.3.2 monitoring requirements.