## 5 Conclusions  
The adoption of the 2030 Agenda for SDGs reflects the ambitions of the countries to direct the policy and strategy toward ensuring a sustainable future. In this context, monitoring activities, along with the use of satellite-derived information, play a key role in defining implementation strategies to meet the SDGs goals. In this work we assessed the use of Sentinel-2 data derived indexes for the estimation of the GPP in the wetlands of Donana National Park. The GPP model predictions showed better performances than standard global MOD17 GPP products over the same area. The potentiality of Sentinel-2 data to enable the estimation of GPP at a finer scale has been demonstrated. Further improvements are foreseen with the increased amount of data and the use of longer time series of Sentinel-2 data, which will result in the modeling of interannual variation of the GPP and a more robust model. High spatial resolution products are key for allowing a detailed description of the distribution of GPP over the ecosystem, especially for heterogeneous ecosystems, thus improving the understanding of ecosystem functions, which are highly correlated to the health condition of ecosystems and the delivery of ecosystem services. Currently, there is a vast amount of unprocessed eddy covariance data in the European Fluxes Database Cluster and other repositories from multiple regions worldwide ([[https://fluxnet.org/about/regional-networks/](https://fluxnet.org/about/regional-networks/)]([https://fluxnet.org/about/regional-networks/](https://fluxnet.org/about/regional-networks/)), [[http://www.europe-fluxdata.eu/](http://www.europe-fluxdata.eu/)]([http://www.europe-fluxdata.eu/](http://www.europe-fluxdata.eu/)), etc., both links accessed on 12 December 2022). The present research workflow is expected to be implemented into GitHub and into the Virtual Earth Laboratory (VLab), a cloud-based platform openly and easily accessible to the users. We encourage relevant stakeholders and protected area managers to make use of the workflow and integrate it into projects and monitoring programs for policy development, implementation, and the ecosystem's status evaluation. Conceptualization, A.S. and M.A.F.-M.; methodology, A.S. and M.A.F.-M.; software, M.A.F.-M.; validation, A.S. and M.A.F.-M.; formal analysis, M.A.F.-M.; investigation, M.A.F.-M.; resources, A.S. and M.A.F.-M.; data curation, M.A.F.-M.; writing--original draft preparation, A.S.; writing--review and editing, A.S. and M.A.F.-M.; visualization, A.S.; supervision, A.S. and G.E.S. ; project administration, A.S.; funding acquisition, G.E.S. All authors have read and agreed to the published version of the manuscript. **Funding:** The work has been conducted within the framework of the e-shape project. e-shape has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement 820852. **Data Availability Statement:** Sentinel-2 images are available presented in this study are available on Google Earth Engine ([[https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR)]([https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR)), accessed on 12 December 2022); in situ flux data and classification maps presented in this study were provided by Donana's Singular Scientific-Technical Infrastructure (ICTS-RBD); the meteorological data presented in this study are available at the Climate Hazards Group InfraRed Precipitation (CHIRPS) database ([[https://www.chc.ucsb.edu/data/chirps](https://www.chc.ucsb.edu/data/chirps)]([https://www.chc.ucsb.edu/data/chirps](https://www.chc.ucsb.edu/data/chirps)), accessed on 12 December 186 2022). **Acknowledgments:** A special thanks to [PERSON] and [PERSON] who provided us with in situ measurements.