Towards a comprehensive and consistent global aquatic land cover characterization framework addressing multiple user needs

Aquatic land cover represents the land cover type that is significantly influenced by the presence of water over an extensive part of a year. Monitoring global aquatic land cover types plays an essential role in preserving aquatic ecosystems and maintaining the ecosystem service they provide for humans, while at the same time their accurate and consistent monitoring for multiple purposes (e.g. climate modelling, biodiversity conservation, water resource management) remains challenging. Although a number of global aquatic land cover (GALC) datasets are available for use to monitor aquatic ecosystems, there are prominent variabilities among these datasets, which is primarily caused by the inconsistency between different land versus water-related monitoring approaches and character-ization schemes. As aquatic land cover exists in many different forms on Earth (e.g. wetland, open water) and can be mapped by different approaches, it is necessary to consider a much more consistent and comprehensive characterization framework that not only ensures the consistency in the monitoring of aquatic land cover but also serves the needs of multiple users (e.g. climate users, agricultural users) interested in different aspects of aquatic lands. In this study, we addressed this issue by 1) reviewing 33 GALC datasets and user needs identified from the citing papers of current datasets and international conventions, policies and agreements in relation to aquatic ecosystems, 2) proposing a global characterization framework for aquatic land cover based on the Land Cover Classification System (LCCS) classifier principles and the identified user needs, and 3) highlighting the opportunities and challenges provided by remote sensing techniques for the implementation of the proposed framework. Results show that users require or prefer various kinds of information on aquatic types including vegetation type, water persistence, the artificiality of cover (i.e. artificial vs natural), water salinity, and the accessibility to the sea (i.e. coastal vs inland). Datasets with medium to high spatial resolution, intra-annual dynamics and inter-annual changes are needed by many users. However, none of the existing datasets can meet all these requirements and a rigorous quantitative accuracy assessment is lacking to evaluate its quality for most of the GALC datasets. The proposed framework has three levels and users are allowed to derive their aquatic land cover types of interest by combining different levels and classifiers of information. This comprehensive mapping framework can help to bridge the gap between user needs and current GALC datasets as well as the gap between generic and aquatic land cover monitoring. The implementation of the framework can benefit from evolving satellite-data availability, improved computation capability and open-source machine learning algorithms, although at the same time it faces challenges mainly coming from the complexity of aquatic ecosystems. The framework proposed in this study provides insights for future operational aquatic land cover monitoring initiatives and will support better understanding and monitoring of complex aquatic ecosystems.