Predicting Gross Primary Productivity (GPP) is key for understanding ecosystem health and quantifying the global carbon cycle. While data-driven models have shown strong performance in capturing GPP dynamics at specific sites, their ability to generalize across ecosystems without site-specific recalibration remains largely untested. This study addresses this gap by evaluating the applicability of XGBoost and LSTM models in estimating GPP across different European ecosystems. We developed a unified (cross-site) modeling framework that integrates in-situ eddy covariance observations and Sentinel-2–derived vegetation indices using incremental learning. Models’ performance was assessed via: (i) site-specific models, developed to capture individual site characteristics, and (ii) cross-site generalization, including evaluation on an independent dataset of unseen ecosystems. SARIMAX is included as a site-specific statistical benchmark for comparison. Our findings indicate that XGBoost consistently outperformed the other models, achieving site-specific R² values above 0.90 in forest and grassland ecosystems and an average R² of 0.72 across unseen sites (range 0.66–0.78). LSTM exhibited better accuracy in predicting GPP peaks at site-specific level, particularly in cropland and forest ecosystems. At site-level, SARIMAX showed comparable performance to XGBoost but struggled in capturing the rapid temporal variation of GPP. These findings demonstrate the feasibility of a data-driven framework for cross-site GPP monitoring within European flux-tower networks, making a first step toward transferable GPP prediction without site-specific recalibration.

This study addresses the challenges posed by climatic changes and biodiversity loss to ecosystem stability, by quantifying gross primary production (GPP) changes. An improved earth observation product is obtained by integrating in-situ and remote sensing data via data-driven models. Employing a user-centered strategy, our methodology builds on users' engagement, ensuring both the identification of user needs and practical product demonstrations. With GEOSS as central and integrated stakeholder, we strive for a broad interoperability and accessibility of generated outcomes. The project outcomes include a curated dataset with FAIR metadata, openly available code, and reports for reproducibility, contributing to the broader Earth Intelligence supply chain.