A data- and expert-driven framework for establishing land cover–related essential variables for SDG monitoring and assessment

• A novel framework to identify, refine and prioritize ELcV4SDGs was proposed. • A set of expert knowledge and data-driven methods were integrated to establish variables. • The ELcV4SDGs contains 3 main categories, involving 50 key variables. • This work supports a more coordinated, effective and detailed spatial monitoring of the SDGs. Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals (SDGs). Although land cover information has long been recognized as an essential component for monitoring SDGs, a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist. Therefore, we propose a novel expert- and data-driven framework for identifying, refining, and selecting a priority list of Essential Land cover-related Variables for SDGs (ELcV4SDGs). This framework integrates methods including expert knowledge-based analysis, clustering of variables with similar attributes, and quantified index calculation to establish the priority list. Applying the framework to 15 specific SDG indicators, we found that the ELcV4SDGs priority list comprises three main categories, type and structure, pattern and intensity, and process and evolution of land cover, which are further divided into 19 subcategories and ultimately encompass 50 general variables. The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment, thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local, national, and global levels.