AquaWatch is developing an operational, multi‑sensor platform that delivers comprehensive water‑quality intelligence across freshwater, coastal and marine environments. By integrating satellite observations with in situ sensor networks and targeted field measurements, AquaWatch provides spatially continuous, high‑frequency insights into ecosystem condition that no single observing system can achieve. This includes incorporating parameters not directly measurable from space—such as nutrients, genomic indicators and citizen‑science observations—through coordinated field campaigns and community‑driven monitoring, including First Nations assessments.
At the core of AquaWatch is a scalable data architecture that unifies diverse observations within data lakes for high‑volume, heterogeneous datasets and data warehouses for curated, analysis‑ready products. This architecture enables advanced analytics, machine learning and algorithm development to transform raw observations into ecologically meaningful indicators such as chlorophyll‑a, turbidity, sediment dynamics and harmful algal bloom risk. Coupled with physics‑informed AI models, the platform supports forecasting of extreme events driven by climate variability and human pressures.
Case studies from the Murray–Darling Basin, Indonesian lakes and Australian coastal systems demonstrate AquaWatch’s ability to detect bloom formation, track sediment and turbidity plumes and characterise water‑quality responses to extreme rainfall and heat events. Together, these capabilities provide a transferable framework for operational monitoring and decision support, accelerating the translation of remote‑sensing innovations into actionable intelligence for managing climate‑ and anthropogenic‑driven change from satellites to shorelines.