The conservation, restoration and sustainable use of wetlands is the target of several international agreements, among which are the Sustainable Development Goals (SDGs). Earth Observation (EO) technologies can assist national authorities in monitoring activities and the environmental status of wetlands to achieve these targets. In this study, we assess the capabilities of the Sentinel-2 instrument to model Gross Primary Productivity (GPP) as a proxy for the monitoring of ecosystem health. To estimate the spatial and temporal variation of GPP, we develop an empirical model correlating in situ measurements of GPP, eight Sentinel-2 derived vegetation indexes (VIs), and different environmental drivers of GPP. The model automatically performs an interdependency analysis and selects the model with the highest accuracy and statistical significance. Additionally, the model is upscaled across larger areas and monthly maps of GPP are produced. The study methodology is applied in a marsh ecosystem located in Doñana National Park, Spain. In this application, a combination of the red-edge chlorophyll index (CLr) and rainfall data results in the highest correlation with in situ measurements of GPP and is used for the model formulation. This yields a coefficient of determination (R ²) of 0.93, Mean Absolute Error (MAE) equal to 0.52 gC m ⁻² day ⁻¹, Root Mean Squared Error (RMSE) equal to 0.63 gC m ⁻² day ⁻¹, and significance level p < 0.05. The model outputs are compared with the MODIS GPP global product (MOD17) for reference; an enhancement of the estimation of GPP is found in the applied methodology.