Hybrid FAPAR and FVC retrieval from Sentinel-3 SYNERGY with Gaussian processes: Development, validation, and cloud-readiness

Abstract

This study addresses the need for robust methods to retrieve essential vegetation traits (EVTs) from the Sentinel-3 SYNERGY 300 m reflectance product, named SY_2_SYN. SY_2_SYN surface reflectance is produced from a combination of Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) observations, available on cloud platforms. To routinely retrieve the fraction of absorbed photosynthetically active radiation (FAPAR) and fraction of vegetation cover (FVC) from SY_2_SYN imagery at continental scales, we developed and evaluated Gaussian Process Regression (GPR) models trained using Soil Canopy Observation of Photosynthesis and Evapotranspiration (SCOPE) simulations. For validation across multiple vegetation types, the Ground Based Observations for Validation (GBOV) service was used, a component of the Copernicus Land Monitoring Service (CLMS), which provides multi-year, upscaled in situ reference measurements. The GPR-SYN FAPAR/FVC products were also intercompared with established CLMS satellite-based estimates over the same GBOV validation sites. Overall, the GPR-SYN and CLMS products demonstrated strong agreement when validated against GBOV data, with GPR-SYN slightly outperforming CLMS. For FAPAR, GPR-SYN achieved an R² of 0.89 (RMSE: 0.13) compared to CLMS’s R² of 0.87 (RMSE: 0.13). For FVC, GPR-SYN demonstrated an R² of 0.83 (RMSE: 0.13) while CLMS yielded an R² of 0.81 (RMSE: 0.15). GPR-SYN FAPAR/FVC aligns with CLMS in intercomparisons, although with slight systematic underestimations. The scalability of the GPR-SYN models was demonstrated by producing the first Sentinel-3 SYNERGY-based, Europe-wide EVT maps with Bayesian uncertainty estimates. The workflow was deployed on cloud infrastructures via the openEO platform and made available through the PyEOGPR package, enabling large-scale computation. Key strengths of GPR-SYN include its high product quality, integration into cloud computing platforms, and its novelty as a scalable retrieval framework tailored for the Sentinel-3 SYNERGY product.