A Summary of Recent Advances in the Literature on Machine Learning Techniques for Remote Sensing of Groundwater Dependent Ecosystems (GDEs) from Space

While groundwater-dependent ecosystems (GDEs) occupy only a small portion of the Earth’s surface, they hold significant ecological value by providing essential ecosystem services such as habitat for flora and fauna, carbon sequestration, and erosion control. However, GDE functionality is increasingly threatened by human activities, rainfall variability, and climate change. To address these challenges, various methods have been developed to assess, monitor, and understand GDEs, aiding sustainable decision-making and conservation policy implementation. Among these, remote sensing and advanced machine learning (ML) techniques have emerged as key tools for improving the evaluation of dryland GDEs. This study provides a comprehensive overview of the progress made in applying advanced ML algorithms to assess and monitor GDEs. It begins with a systematic literature review following the PRISMA framework, followed by an analysis of temporal and geographic trends in ML applications for GDE research. Additionally, it explores different advanced ML algorithms and their applications across various GDE types. The paper also discusses challenges in mapping GDEs and proposes mitigation strategies. Despite the promise of ML in GDE studies, the field remains in its early stages, with most research concentrated in China, the USA, and Germany. While advanced ML techniques enable high-quality dryland GDE classification at local to global scales, model performance is highly dependent on data availability and quality. Overall, the findings underscore the growing importance and potential of geospatial approaches in generating spatially explicit information on dryland GDEs. Future research should focus on enhancing models through hybrid and transformative techniques, as well as fostering interdisciplinary collaboration between ecologists and computer scientists to improve model development and result interpretability. The insights presented in this study will help guide future research efforts and contribute to the improved management and conservation of GDEs.