Acknowledgment to the Reviewers of Journal of Environmental & Earth Sciences in 2025
2026-02-06
Remote Sensing Big Data for Sustainable Development: Emerging Analytics, Applications, and Global Pathways
Authors
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Huiling Li
Network Information Center, Hetao College, Bayannur 015000, China
DOI:
https://doi.org/10.30564/jees.v8i1.12936Abstract
The development of remote sensing has seen the creation of a global measurement infrastructure of sustainable development due to growing multipolar archives, rising revisit frequency, and the availability of cloud-accessible platforms of Earth observation. This review summarizes how remote sensing big data is being organized into decision-grade sustainability intelligence, the new approaches to analytics, and how Sustainable Development Goals (SDGs)-oriented application pathways inter-relate action pathways that bridge observations with action. The terminologies like new data ecosystem, data readiness and interoperability, changing economics of scalable computation, and detailing the functions of diversity of modalities (optical, Synthetic Aperture Radar—SAR, thermal, Light Detection and Ranging—LiDAR, hyperspectral) have been defined. These themes of analytics, which are transforming the practice of operational analytics, are then condensed: foundations and self-supervised learning of transferable representations, multi-modal fusion to gap fill and richer inference, spatiotemporal intelligence to trend of early warning, physics-aware hybrid methods to enhance robustness and meaning under non-stationary conditions. Across the climate risk, food systems, water resources, sustainable cities, ecosystems and biodiversity, energy transitions, and health exposure pathways, the roles of Earth Observation (EO) products as direct measures and proxies, and concepts of validating, semantic comparability, and communicating uncertainties play a key role in EO products becoming credible when faced with high-stakes deployment decisions. Lastly, we chart world ways of implementation via monitoring services, early warning systems, and systems of multiple regimes, and previously underline cross-cutting priorities, scalable structures in validation, performance, so that domains of shift, agreeable governance, and Dual-use risk safeguards, and sustainable lifecycle support of EO services. These priorities form a realistic set of priorities on the alignment of remote sensing innovation with quantifiable SDGs progress.
Keywords:
Remote Sensing Big Data; Sustainable Development Goals; Geospatial Artificial Intelligence (AI); Measurement, Reporting and Verification (MRV); Uncertainty QuantificationReferences
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Li, H. (2026). Remote Sensing Big Data for Sustainable Development: Emerging Analytics, Applications, and Global Pathways. Journal of Environmental & Earth Sciences, 8(1), 117–145. https://doi.org/10.30564/jees.v8i1.12936
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