A cloud-computing framework for downscaled global 300 m SIF retrieval from Sentinel-3 and TROPOSIF

Sun-induced chlorophyll fluorescence (SIF) is a critical indicator of photosynthetic activity. Yet, existing satellite SIF products typically suffer from coarse spatial resolutions, generally coarser than 500 m, which limits their utility for fine-scale ecosystem studies. Here, we present a cloud-computing framework designed for the generation of a downscaled SIF product (S3-SIF 743 ) derived from Sentinel-3 (S3) Ocean and Land Colour Instrument (OLCI), with a spatiotemporal resolution of 300 m and 4 days. Our approach uses the Google Earth Engine (GEE) cloud-computing platform to integrate SIF produced from TROPOspheric Monitoring Instrument measurements within the 743–758 nm retrieval ( TROPOSIF 743 ), S3 radiances, S3-based vegetation traits, latitude and longitude within a Random Forest (RF) regression framework. Model training over Europe achieved robust performance against TROPOSIF 743 reference data ( R 2 = 0.767, RMSE = 0.137 mW m −2 sr − 1 nm − 1 ), and the approach was subsequently extended globally. Validation against ground-based tower observations confirmed that S3-SIF 743 effectively reproduces seasonal dynamics across diverse ecosystems. Comparisons against TROPOSIF 743 demonstrated strong spatial consistency in temperate agricultural regions, with the highest R 2 values in croplands and lower agreement in sparsely vegetated or persistently cloudy regions. Global mapping revealed coherent patterns of photosynthetic activity, with peak values in tropical rainforests and major agricultural zones. Importantly, S3-SIF 743 reduces retrieval noise relative to TROPOSIF 743 and provides unprecedented insights into sub-kilometer spatial heterogeneity. By combining S3’s rich spectral capabilities with GEE’s scalable computing environment, our approach bridges the gap between current coarse-resolution SIF products and ESA’s upcoming FLEX mission, offering a flexible and operational pathway for high-resolution monitoring of terrestrial photosynthesis. • Developed the first global 300 m, 4-day S3-SIF 743 by fusing S3 and TROPOSIF 743 . • Implemented a Random Forest model fully in GEE for scalable cloud processing. • Demonstrated overall agreement with TROPOSIF 743 and ground-based SIF observations. • Demonstrated that S3-SIF 743 reproduces global SIF patterns with reduced noise. • Provides a pathway linking current satellites and ESA FLEX for sub-km monitoring.