BROCKMANN CONSULT GMBH (DE)
Despite their global coverage and public availability, measurements of water quality parameters retrieved by remote sensing have not been used for local short-term forecasting of water quality yet. In fact, forecasting based on remote sensing water quality products with on-ground resolution from, say, 30 m (Sentinel-2 MSI, Landsat-8/9 OLI) to 300 m (Sentinel-3A/B OLCI) or 500 m (GOCI) constitutes a potential breakthrough since the availability of in situ measurements of water quality is often limited. For a given location, availability of remote sensing water quality products is mainly determined by satellite revisit frequencies (3-5 days for Sentinel-2A/B, 1-3 days for Sentinel-3A/B, and 30 minutes for GOCI) and constrained by cloud coverage.
The Forecasting Water Quality from Space (FC-WQ) project aims to develop and validate a method for local short-term forecasting of water quality based on EO data in coastal and inland waters. This method will be based on time series of remotely sensed water quality parameters, combined with past, present, and forecast data of physical parameters (i.e., meteorological, hydrological, and specific environmental parameters). The proposed method exploits the capability of Machine Learning (ML) to learn and model the complex relationships in aquatic ecosystems.
Specific objectives of this undertaking comprise: