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A cloud-computing framework for downscaled global 300 m SIF retrieval from Sentinel-3 and TROPOSIF
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DOI：10.1016/j.jag.2026.105330.png)
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Abstract
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• Developed the first global 300 m, 4-day S3-SIF743 by fusing S3 and TROPOSIF743. • Implemented a Random Forest model fully in GEE for scalable cloud processing. • Demonstrated overall agreement with TROPOSIF743 and ground-based SIF observations. • Demonstrated that S3-SIF743 reproduces global SIF patterns with reduced noise. • Provides a pathway linking current satellites and ESA FLEX for sub-km monitoring.
Keywords:
Sun-induced fluorescence
Sentinel-3 OLCI
TROPOMI
Google Earth Engine
Machine learning
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