Earth Observation for Quantifying Sustainable Development Goals

Dear Colleagues,

The Sustainable Development Goals (SDGs) provide a global framework for assessing progress toward sustainable development, yet their quantification remains constrained by data gaps, inconsistent spatial scales, limited validation, and complex interlinkages among goals. Earth observation (EO), combined with geospatial analytics, artificial intelligence, and big-data approaches, is increasingly able to provide objective, repeatable and spatially explicit evidence for SDG assessment. This Special Issue focuses on “Earth Observation for Quantifying Sustainable Development Goals”: how multi-source EO and geospatial data can be converted into quantitative, validated and policy-relevant SDG evidence. The focus goes beyond broad EO applications for SDG monitoring by emphasizing transparent data–model–indicator chains, uncertainty characterization, cross-scale validation, and the interpretation of spatial interlinkages and spillover effects. The topic is timely because open satellite archives, cloud-based processing, AI-enabled retrieval methods and global high-resolution products are rapidly improving the feasibility of producing comparable SDG evidence, while 2030 and post-2030 policy processes require more frequent, localizable and actionable assessments.

Aligned with the scope of Remote Sensing (the Remote Sensing for Geospatial Science Section) this Special Issue will collect studies that advance EO-based SDG quantification as a coherent methodological framework. It aims to highlight methods, datasets, workflows and applications that translate observations into measurable SDG indicators and integrated evidence for land, water, food, urban, climate and ecosystem sustainability. Priority will be given to papers with explicit SDG-target mapping, geospatial analytical depth, reproducible workflows, and validation with ground observations, statistics or benchmark datasets.

We invite original research articles, review papers, and technical notes covering (but not limited to) the following themes:

• EO-derived quantitative SDG indicators with explicit data–model–indicator chains;
• Multi-source EO fusion and AI methods only where directly linked to SDG indicator retrieval, uncertainty or validation;
• Spatial interlinkages, synergies/trade-offs and spillover effects among SDG indicators;
• Cross-scale validation of EO-derived SDG indicators using ground observations, statistics or benchmark datasets;
• Scenario-ready EO evidence for land, water, food, urban, climate and ecosystem sustainability;
• Open datasets, benchmark products and reproducible geospatial workflows for SDG monitoring;
• Reviews or perspectives on EO-enabled SDG monitoring for the 2030 and post-2030 agenda.

Submissions that combine interdisciplinary approaches (e.g., remote sensing, big data analytics, environmental science, spatial economics and sustainability science) are particularly welcome.

Dr. Yu Chen
Dr. Yongze Song
Prof. Dr. Min Cao
Dr. Huijuan Xiao
Guest Editors

Manuscript Submission Information

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