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From Data to Policy: Strengthening essential climate variable monitoring with deep learning algorithms and data quality standards
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DOI：10.1109/MGRS.2026.3663666.png)
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Abstract
En 中文
Essential climate variables (ECVs) are critical for understanding and monitoring climate systems, providing important data to assess climate change and supporting policy formulation. This review emphasizes the importance of ensuring data quality, traceability, and consistency to derive reliable features from ECV datasets, addressing challenges such as temporal and spatial coverage gaps, calibration discrepancies, and harmonization across diverse sources. Furthermore, we highlight both the transformative potential and limits of advanced analytics, including artificial intelligence (AI), in enhancing the monitoring and prediction of ECVs using three case studies: 1) climate modeling and prediction of temperatures for planning scenario with machine learning (ML), 2) the Earth’s surface processes, and 3) monitoring the Earth radiation budget (ERB). This article also explores how ECVs are integrated into global frameworks like the Global Climate Observing System (GCOS) and the WMO Integrated Global Observing System (WIGOS), which establish standardized protocols for reliable and interoperable data. By synthesizing advances in technology, data quality practices, and global collaboration efforts, this review underscores the importance of interdisciplinary approaches to bridge the gap between scientific knowledge and actionable climate policies.
Keywords:
Monitoring
Data integrity
Standards
Sea measurements
Measurement uncertainty
Reliability
Climate change
Earth Observing System
Deep learning
Global navigation satellite system
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