The classification of freshwater ecosystems is essential for effective biodiversity conser-vation and ecosystem management, particularly with increasing threats. We developed an automated approach to mapping and classifying freshwater ecosystem functional groups based on the IUCN Global Ecosystem Typology (GET), offering a scalable, dy-namic, and efficient alternative to current manual methods. Our method leveraged remote sensing data and thresholding algorithms to classify ecosystems into distinct ecosystem functional groups, accounting for challenges such as temporal and spatial variability of freshwater ecosystems, inconsistencies in manual classification, and the complexities of dynamic ecosystems. Unlike traditional approaches, which rely on manual cross-referencing to adapt existing maps and subjective biases, our system is repeatable, transparent, and adaptable to new incoming satellite data. We demonstrate the applicability of this method in the Paroo-Warrego region of Australia (~14,000,000 ha), highlighting the automated classification’s capacity to process large areas with diverse ecosystems. Although some functional groups require static datasets due to current lim-itations in satellite data resolution, the overall approach had high accuracy (84%) and potential for global scalability. This work provides a foundation for future applications to other freshwater ecosystems around the world underpinning biodiversity management, monitoring and reporting worldwide.